ISVC’24 Special Tracks

ST1: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management

Multiple biomedical imaging modalities are used in cancer detection, diagnosis and management including X-ray (plain film and Computed Tomography (CT)), Ultrasound (US), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Optical Imaging and Digital Pathology. These imaging modalities form an essential part of cancer clinical decision making and are able to furnish morphological, structural, metabolic and functional information. In particular, biomedical imaging has become an important element for early cancer detection, determining the stage and the precise locations of cancer to aid in directing surgery and other cancer treatments, or to check if a cancer has returned.

This special track invites research contributions on innovative biomedical imaging techniques for cancer screening, diagnosis and staging, guiding cancer treatments, determining if a treatment works, and monitoring for cancer recurrence. Of particular interest are research contributions employing modern computer vision techniques, powered by AI, statistical and machine/deep learning models, addressing the above challenges.

Topics of interest include but are not limited to:

• Biomedical image analysis (e.g., detection, segmentation, classification, registration)
• Computer-aided detection/diagnosis of various cancer types in biomedical images
• Multi-modality fusion (e.g., MRI/PET, PET/CT, X-ray/ultrasound, etc.) for diagnosis, image analysis and image guided interventions
• Image reconstruction for biomedical imaging
• Cellular image analysis (e.g., genotype, phenotype, classification, identification, cell tracking)
• Molecular/pathologic image analysis (e.g., PET, digital pathology)
• Statistical and machine/deep learning models for biomedical image analysis
• Evaluating and interpreting machine/deep learning models
• Designing and building interfaces between algorithms and clinicians

Organizers:

George Bebis, University of Nevada, Reno, USA
Sokratis Makrogiannis, Delaware State University, USA
Hongfang Liu, University of Texas Health Science Center, Houston, TX, USA
Kurt R. Weiss,  University of Pittsburgh, Pittsburgh, PA, USA
Ines Lohse,  University of Pittsburgh, Pittsburgh, PA, USA
Ahmad P. Tafti,  University of Pittsburgh, Pittsburgh, PA, USA

ST2 Vision and Robotics for Agriculture

Increasing population, decreasing arable land, climate change, and a declining skilled workforce pose unprecedented challenges to the ability to satisfy the growing demand for food on a global scale. It is thus becoming more and more important to increase or at the very least maintain current productivity while using fewer inputs, such as water and agrichemicals. Precision agriculture aims to address this issue. In recent years there has been a boom in agricultural robotics, machine vision, artificial intelligence, and allied technology (e.g., vegetation-specific sensor development, digital twins, etc.) employed toward improving agricultural automation outcomes. Such efforts have so far been undertaken in a rather decoupled manner. However, several research labs (both in academic and private sector settings) around the world are increasingly co-designing the actuation and perception systems.

The goal of this special track is to solicit paper submissions and bring together researchers and practitioners developing tools in support of agricultural automation that merge visual sensing with autonomy and are deployed into physical robots and tested in the field. We anticipate including contributions that focus both on development of standalone relevant tools (e.g., vegetation-specific visual sensing) as well as system design and integration. To this end, of particular interest are techniques that integrate machine vision and artificial intelligence into agricultural robotics, but methods that focus into any of the topic areas listed below are also relevant and fit well into the scope of this special track, provided there is clear relevance to precision agriculture.

Topics of interest include but are not limited to:

• Specialized visual sensor design, deployment, and assessment
• Co-design of actuation and perception for agricultural robotics
• Real-time (multi-modal) data collection and processing
• Sensor selection, placement, and calibration for field data acquisition
• Vision-based deployment of (teams of) robots for information acquisition
• (Multi-modal) Machine vision for precision agriculture
• Digital twins in support of deployment of vision-integrated agricultural robotics
• Design of user interfaces for data collection, logging, and presentation
• Generative artificial intelligence in support of agricultural automation

Organizers:

Dimitris Zermas, Sentera, St Paul, Minnesota, USA
Konstantinos Karydis, University of California – Riverside, USA
Nikos Papanikolopoulos, University of Minnesota, USA
Kostas Alexis, Norwegian University of Science and Technology, Norway
George Bebis, University of Nevada – Reno, USA

ST3: Generalization in Visual Machine Learning

Generalization is particularly important in machine learning for visual computing due to the complex and diverse nature of visual data. In visual computing, machine learning models are often trained on large datasets of images or videos with the goal of performing tasks such as object recognition, segmentation, classification, or detection. Achieving good generalization is crucial for the practical utility of these models as they need to perform accurately on new, unseen images or videos. Good generalization is especially important in real-world applications where visual data can vary widely in appearance, context, and lighting conditions.

Another reason why generalization is important in machine learning for visual computing is the potential for bias and overfitting. Visual datasets are often biased towards specific classes, contexts, or viewpoints, which can lead to poor generalization when models are applied to new data outside of these biases. Additionally, machine learning models trained on visual data can easily overfit to noise or irrelevant features in the training data, leading to poor performance on new data.

To address these challenges, researchers in machine learning for visual computing have developed a range of techniques to improve generalization. These include regularization techniques to prevent overfitting, transfer learning and domain adaptation techniques to leverage pre-trained models or adapt to new domains, data augmentation techniques to increase the diversity of the training data, and uncertainty estimation techniques to quantify model confidence and detect potential errors.

We invite research contributions to this special issue on Generalization in Visual Machine Learning. We welcome original research articles, reviews, and survey papers on the above topics. All submissions will be rigorously peer-reviewed and selected based on their relevance, technical quality, and originality.

Topics of interest include but are not limited to:

• Regularization techniques for improving generalization in visual computing
• Novel hierarchical architecture for domain generalization
• Transfer learning and domain adaptation for visual computing
• Data augmentation and synthesis techniques for improving generalization in visual computing
• Uncertainty estimation in visual computing
• Generalization in aerial surveillance under complex and contested environments
• Generalization in object detection
• Robustness and adversarial attacks in visual computing
• Explainability and interpretability of visual computing models
• Novel approaches for improving generalization in visual computing
• Generalization in visual object tracking
• Generalization in biometric recognition techniques
• Generalization on medical image segmentation
• Zero-shot learning for visual computing
• Disentangled representations for improving generalization in visual computing
• Graph based approaches (Graph Signal Processing, Graph Neural Networks) in visual computing

Organizers:

Mohamed S. Shehata, University of British Columbia, BC, Canada
Minglun Gong, University of Guelph, Ontario, Canada
Thierry Bouwmans, La Rochelle Université, La Rochelle, France
Ahmed R. Hussein, University of Guelph, Ontario, Canada
Paola Barra, Università degli studi di Napoli « Parthenope », Italy
Deepak Kumar Jain, University of Chinese Academy of Sciences, China
Soon Ki Jung,  Kyungpook National University,  South Korea
Sajid Javad, Khalifa University of Science and Technology, UAE

ST4: Artificial Intelligence in Aerial and Orbital Imagery

Arificial inteligence and computer vision play a major role in both Earth bound applications as well as space exploration. On Earth, usage of high resolution and large coverage maps includes climate monitoring, resource utilization, fire and floods detection, agriculture and forestry. High resolution mapping from orbital imagery also supports topographic feature detection on Solar System planets including Moon and Mars. These application support in turn scientific discovery about planetary formation and support current and future space exploration missions, Steady increase in camera resolution and parallel computing over the past decade enabled creation of planetary maps at unprecedented coverage and resolution.

The purpose of this special track is to gather novel techniques for artificial intelligence and high resolution/large scale mapping that would enable the technologies of the future and support various terrestrial and planetary applications.

The authors of all accepted papers in the special track will be invited to submit an extended version of their work for review and possible publication in a Special Issue of the Remote Sensing journal published by Multidisciplinary Digital Publishing Institute (MDPI)

Topics of interest include but are not limited to:

• 3D stereo reconstruction from orbital and aerial imagery
• Shape from shading, shape from silhouette reconstruction
• Automatic detection and 3D reconstruction of vehicle, building, people
• Automatic detection of natural feature including craters, boulders, flood, forest) detection,
• Feature matching and automatic localization within orbital/aerial imagery.

Organizers:

Ara V. Nefian, KBR at NASA Ames Research Center, USA
Larry Edwards, NASA Ames Research Center, USA
Gary Bradski, OpenCV founder, USA
Mark Shirley, NASA Ames Research Center, USA

ISVC’23 Special Tracks

ST1: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management

Multiple biomedical imaging modalities are used in cancer detection, diagnosis and management including X-ray (plain film and Computed Tomography (CT)), Ultrasound (US), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Optical Imaging and Digital Pathology. These imaging modalities form an essential part of cancer clinical decision making and are able to furnish morphological, structural, metabolic and functional information. In particular, biomedical imaging has become an important element for early cancer detection, determining the stage and the precise locations of cancer to aid in directing surgery and other cancer treatments, or to check if a cancer has returned.

This special track invites research contributions on innovative biomedical imaging techniques for cancer screening, diagnosis and staging, guiding cancer treatments, determining if a treatment works, and monitoring for cancer recurrence. Of particular interest are research contributions employing modern computer vision techniques, powered by AI, statistical and machine/deep learning models, addressing the above challenges.

Topics of interest include but are not limited to:

• Biomedical image analysis (e.g., detection, segmentation, classification, registration)
• Computer-aided detection/diagnosis of various cancer types in biomedical images
• Multi-modality fusion (e.g., MRI/PET, PET/CT, X-ray/ultrasound, etc.) for diagnosis, image analysis and image guided interventions
• Image reconstruction for biomedical imaging
• Cellular image analysis (e.g., genotype, phenotype, classification, identification, cell tracking)
• Molecular/pathologic image analysis (e.g., PET, digital pathology)
• Statistical and machine/deep learning models for biomedical image analysis
• Evaluating and interpreting machine/deep learning models
• Designing and building interfaces between algorithms and clinicians

Organizers:

George Bebis, University of Nevada, Reno, USA
Sokratis Makrogiannis, Delaware State University, USA
Hongfang Liu, University of Texas Health Science Center, Houston, TX, USA
Kurt R. Weiss,  University of Pittsburgh, Pittsburgh, PA, USA
Ines Lohse,  University of Pittsburgh, Pittsburgh, PA, USA
Ahmad P. Tafti,  University of Pittsburgh, Pittsburgh, PA, USA

 

ST2: Generalization in Visual Machine Learning

Generalization is particularly important in machine learning for visual computing due to the complex and diverse nature of visual data. In visual computing, machine learning models are often trained on large datasets of images or videos with the goal of performing tasks such as object recognition, segmentation, classification, or detection. Achieving good generalization is crucial for the practical utility of these models as they need to perform accurately on new, unseen images or videos. Good generalization is especially important in real-world applications where visual data can vary widely in appearance, context, and lighting conditions.

Another reason why generalization is important in machine learning for visual computing is the potential for bias and overfitting. Visual datasets are often biased towards specific classes, contexts, or viewpoints, which can lead to poor generalization when models are applied to new data outside of these biases. Additionally, machine learning models trained on visual data can easily overfit to noise or irrelevant features in the training data, leading to poor performance on new data.

To address these challenges, researchers in machine learning for visual computing have developed a range of techniques to improve generalization. These include regularization techniques to prevent overfitting, transfer learning and domain adaptation techniques to leverage pre-trained models or adapt to new domains, data augmentation techniques to increase the diversity of the training data, and uncertainty estimation techniques to quantify model confidence and detect potential errors.

We invite research contributions to this special issue on Generalization in Visual Machine Learning. We welcome original research articles, reviews, and survey papers on the above topics. All submissions will be rigorously peer-reviewed and selected based on their relevance, technical quality, and originality.

Topics of interest include but are not limited to:

• Regularization techniques for improving generalization in visual computing
• Novel hierarchical architecture for domain generalization
• Transfer learning and domain adaptation for visual computing
• Data augmentation and synthesis techniques for improving generalization in visual computing
• Uncertainty estimation in visual computing
• Generalization in aerial surveillance under complex and contested environments
• Generalization in object detection
• Robustness and adversarial attacks in visual computing
• Explainability and interpretability of visual computing models
• Novel approaches for improving generalization in visual computing
• Generalization in visual object tracking
• Generalization in biometric recognition techniques
• Generalization on medical image segmentation
• Zero-shot learning for visual computing
• Disentangled representations for improving generalization in visual computing
• Graph based approaches (Graph Signal Processing, Graph Neural Networks) in visual computing

Organizers:

Mohamed S. Shehata, University of British Columbia, BC, Canada
Minglun Gong, University of Guelph, Ontario, Canada
Thierry Bouwmans, La Rochelle Université, La Rochelle, France
Ahmed R. Hussein, University of Guelph, Ontario, Canada
Paola Barra, Università degli studi di Napoli « Parthenope », Italy
Deepak Kumar Jain, University of Chinese Academy of Sciences, China
Soon Ki Jung,  Kyungpook National University,  South Korea

 

ST3: Autonomous Anomaly Detection in Images  

Detecting image anomalies is even more difficult than detecting anomalies in structured datasets or from time series data. It is partly because visual features are more difficult to capture than numerical features in structured datasets. That is where Deep Learning (DL) techniques come in, as deep learning models can perform auto-feature extraction for unstructured data like images. The track will focus on the application of anomaly detection algorithms to identify and classify anomalous images.

This special track invites research contributions on innovative advances in anomaly detection algorithms, including deep learning-based approaches and other methods. Particular interests are challenges associated with applying anomaly detection in the field of image processing, such as the need for manual annotation and the difficulty of dealing with large datasets, along with exploring the application of anomaly detection in various contexts. This will include applications such as automated vehicle systems, medical image analysis, facial recognition, and automated surveillance.

Topics of interest include but are not limited to:

• Video Surveillance Anomaly Detection
• Traffic Monitoring and Pattern Analysis
• Autonomous or Semi-Autonomous Vehicles – Automatic Unwanted Object Detection
• Visual Driving Assistance Systems
• Image Preprocessing for Anomaly Detection
• Feature Extraction and Representation for Anomaly Detection
• Supervised and Unsupervised Anomaly Detection in Images
• Vehicle Detection, Classification, and Tracking, Authorized License Plate Recognition
• Pedestrian Detection and Tracking
• Urban Driving Assistance Systems, Road Sign Detection and Speed Assist
• Intersection Safety Monitoring and Analysis
• Vision Based Localization for Autonomous Vehicles
• Deep Learning Techniques for Anomaly Detection in Images
• Anomaly Detection in Video Streams
• Anomaly Detection in Medical Images
• Anomaly Detection Using Generative Adversarial Networks
• Evaluation Metrics for Anomaly Detection in Images
• Applications of Anomaly Detection in Images

Organizers:

Aishwarya Asesh, Adobe, San Jose, CA, USA
Meenal Dugar, Pennsylvania State University, University Park, PA, USA

 

ST4: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture

The cyclical and time-sensitive nature of Agriculture poses certain limitations to the data-gathering capabilities of academic and industrial groups researching solutions to Precision Agriculture problems. Time constraints along with the wide range of targeted applications, different environments, and access to commonly calibrated equipment create a research environment with two kinds of datasets:

• Publicly available datasets with limited variability targeting academic research
• Private datasets collected by institutions for the development of IP-protected applications

The lack of commonly available datasets hinders the replication and verification of methods and obscures a clear view of their comparative performance. This poses constraints to data availability in the Precision Ag community with significant implications for the progress of the domain. On the other hand, the significant cost of data gathering and curation is understandably an obstacle for for-profit organizations to publicly release their data.

This special track aims to address these limitations by bringing together researchers, practitioners, and industry experts to discuss and share their experiences and the latest research findings in data collection, curation, and generation in Precision Agriculture. Of particular interest are techniques that deal with efficient data annotation and curation, synthetic data generation and photorealism, and general data handling and preparation before reaching the model training and inferencing cycle. We are striving to provide visibility into good practices and the low-cost generation of datasets for the benefit of the Precision Agriculture community.

Topics of interest include but are not limited to:

• Sensor selection, placement, and calibration in Precision Agriculture
• Data annotation, cleaning, and integration techniques
• Synthetic data generation techniques and their applications in Precision Agriculture
• Big Agriculture data quality control and curation
• Real-time data collection and processing for Precision Agriculture applications
• Privacy and security concerns in Precision Agriculture data collection and sharing
• Challenges and opportunities for collaboration between academia and industry in Precision Agriculture data gathering and curation

Organizers:

Dimitris Zermas, Sentera, St Paul, Minnesota, USA
Konstantinos Karydis, University of California – Riverside, USA
Nikos Papanikolopoulos, University of Minnesota, USA
Kostas Alexis, Norwegian University of Science and Technology, Norway
George Bebis, University of Nevada – Reno, USA

 

ST5: Artificial Intelligence in Aerial and Orbital Imagery

Arificial inteligence and computer vision play a major role in both Earth bound applications as well as space exploration. On Earth, usage of high resolution and large coverage maps includes climate monitoring, resource utilization, fire and floods detection, agriculture and forestry. High resolution mapping from orbital imagery also supports topographic feature detection on Solar System planets including Moon and Mars. These application support in turn scientific discovery about planetary formation and support current and future space exploration missions, Steady increase in camera resolution and parallel computing over the past decade enabled creation of planetary maps at unprecedented coverage and resolution.

The purpose of this special track is to gather novel techniques for artificial intelligence and high resolution/large scale mapping that would enable the technologies of the future and support various terrestrial and planetary applications.

The authors of all accepted papers in the special track will be invited to submit an extended version of their work for review and possible publication in a Special Issue of the Remote Sensing journal published by Multidisciplinary Digital Publishing Institute (MDPI)

Topics of interest include but are not limited to:

• 3D stereo reconstruction from orbital and aerial imagery
• Shape from shading, shape from silhouette reconstruction
• Automatic detection and 3D reconstruction of vehicle, building, people
• Automatic detection of natural feature including craters, boulders, flood, forest) detection,
• Feature matching and automatic localization within orbital/aerial imagery.

Organizers:

Ara V. Nefian, KBR at NASA Ames Research Center, USA
Larry Edwards, NASA Ames Research Center, USA
Gary Bradski, OpenCV founder, USA
Mark Shirley, NASA Ames Research Center, USA

 

ST6: Beyond Deep Learning: Exploring the Potential of Neurally-inspired Neural Networks for Intelligent Systems

In recent years, deep learning has achieved remarkable success in various applications, especially in the field of computer vision. However, it is not a secret that deep learning models are limited by their dependence on large amounts of labeled data and their susceptibility to adversarial attacks. Moreover, deep learning models are not robust to out-of-distribution data, which hinders their real-world applicability. Lastly, deep learning models lack the flexibility and adaptability of biological neural networks, which have evolved over millions of years to be highly efficient and robust. To address these limitations, there is a growing interest in neurally-inspired neural networks as a means of overcoming the limitations of deep learning and enabling the development of more intelligent systems.

The objectives of this year’s special track are twofold. First, we invite contributions that show the potential of neurally-inspired networks to overcome the limitations of traditional deep learning approaches. By more closely mimicking the functionality of biological neural networks, these networks have the potential to learn from unsupervised data, be more robust to adversarial attacks, and better handle out-of-distribution data. Furthermore, they may enable the development of more efficient and explainable AI systems.

Second, we will invite the latest advances in neuromorphic computing algorithms, hardware, and software that enable the development of neurally-inspired networks and their applications in computer vision. Of particular interest are new discoveries, challenges and opportunities associated with these new computing paradigms that more closely mimic the functionality of biological neural networks and their implications for the design of intelligent systems. Submissions that highlight the latest applications of these networks in computer vision such as super-resolution, image classification, and object recognition are encouraged.

Overall, this special track aims to bring together researchers from different disciplines to explore the potential of neurally-inspired neural networks for intelligent systems. By doing so, we hope to facilitate the development of more robust, efficient, and interpretable AI systems that can operate in real-world environments.

Topics of interest include but are not limited to:

• Neuromorphic Computing
• Spiking Neural Networks
• Event-based systems
• Self-supervised Learning
• Semi-supervised Learning
• Few-shot Learning
• Energy-based models
• Robust Classification/Object detection/ Object segmentation
• Generative Machine Learning
• Neuro-inspired AI
• Biologically Plausible AI
• Sparse Coding
• Sparse Distributed Representations
• Energy Efficient Machine Learning
• Top Down Feedback in Machine Learning
• Inhibitory and Excitatory Lateral/Feedback connections
• Cognitive Neural Architectures
• Non-von Neumann computing architectures and models

Organizers:

Edward Kim, Drexel University, USA
Garrett T. Kenyon, Los Alamos National Laboratory, USA
Michael Teti, Los Alamos National Laboratory, USA
Yijing Watkins, Pacific Northwest National Laboratory, USA

ST7: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures

Critical infrastructures are among the cornerstones that support modern daily living through the variety of essential services offered to their end-users. Water and gas utility networks, transportation networks (e.g., highways, road maintenance, airports, rail stations), communication networks, and the smart electric power grid are prominent cases of critical infrastructures, the reliable operation, security, and resilience of vital of which, underpin the functioning of a nation or region.

In addition, inspection and maintenance of civil infrastructures are of key importance such as cultural heritage sites, historic city blocks and monuments. The impact of climate change on these infrastructures and the design of new intervention actions to resist these infrastructures from environmental threats is a hot research topics nowadays.

Among the many reasons that mandate the monitoring of such infrastructures are:

• Ensuring continuity of essential services
• Detecting and preventing failures
• Facilitating resource allocation
• Complying with regulations
• Supporting decision making
• New design methods to compensate climate change impact

The rapid advancements in heterogeneous sensor development, sensor data acquisition, transmission and processing, and the Internet of Things, have created new possibilities for growth within critical infrastructures. By integrating computer vision and machine learning technologies into critical infrastructure monitoring systems, organizations can improve the accuracy, efficiency, and effectiveness of their monitoring efforts, enhancing the overall security, reliability, and resilience of these essential assets and services. By analyzing images or video feeds, computer vision can help track and manage inventory, equipment, and assets across various critical infrastructure facilities, identify damage and vulnerable areas, prioritize response efforts and assess the efficiency and performance of various infrastructure systems and assets.

This special track will feature key innovations in artificial intelligence, machine learning, signal and information processing put forward to advancing the design, analysis, optimization, operation and protection of critical infrastructures.

Topics of interest include but are not limited to:

• Visual inspection and maintenance
• Anomaly detection
• Safety and security monitoring
• Asset management and inventory tracking
• Disaster monitoring and response
• Traffic and transportation monitoring
• Environmental monitoring
• Performance monitoring and optimization
• Cultural heritage monuments/buildings inspection, maintenance, monitoring

Organizers:

Anastasios Doulamis, National Technical University of Athens, Greece
Nikolaos Bakalos, National Technical University of Athens, Greece
Hung (Jim) La, University of Nevada, Reno, USA

ISVC’22 Special Tracks

ST1: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management

Multiple biomedical imaging modalities are used in cancer detection, diagnosis and management including X-ray (plain film and Computed Tomography (CT)), Ultrasound (US), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Optical Imaging and Digital Pathology. These imaging modalities form an essential part of cancer clinical decision making and are able to furnish morphological, structural, metabolic and functional information. In particular, biomedical imaging has become an important element for early cancer detection, for determining the stage and precise localization of cancer lesions to aid in directing surgery and other cancer treatments, or to check if cancer has recurred.

This special track invites research contributions on innovative biomedical image analysis techniques for cancer screening, diagnosis and staging, guiding cancer treatments, determining if a treatment works, and monitoring for cancer recurrence. Of particular interest are research contributions employing modern computer vision techniques, powered by statistical and machine/deep learning models, addressing the above challenges.

The authors of all accepted papers in the special track will be invited to submit an extended version of their work for review and possible publication in a Special Issue on Biomedical and Biological Image Analysis Techniques for Cancer Detection, Diagnosis and Management of the Mathematical Biosciences and Engineering journal (published by the American Institute of Mathematical Sciences) with an expected submission deadline in the second quarter of 2023.

Topics of interest include but are not limited to:

• Biomedical image analysis (e.g., detection, segmentation, classification, registration)
• Computer-aided detection/diagnosis of various cancer types in biomedical images
• Multi-modality fusion (e.g., MRI/PET, PET/CT, X-ray/ultrasound, etc.) for diagnosis, image analysis and image guided interventions
• Image reconstruction for biomedical imaging
• Cellular image analysis (e.g., genotype, phenotype, classification, identification, cell tracking)
• Molecular/pathologic image analysis (e.g., PET, digital pathology)
• Statistical and machine/deep learning models for biomedical image analysis
• Evaluating and interpreting machine/deep learning models
• Designing and building interfaces between algorithms and clinicians

Organizers:
George Bebis, University of Nevada, Reno
Sokratis Makrogiannis, Delaware State University

ST2: Neuro-inspired Artificial Intelligence

This special track will focus on research that integrates themes in neuroscience that have yet to be thoroughly explored in machine learning and artificial intelligence. The current state-of-the-art is dominated by deep learning; however, recent research has uncovered critical issues that limits its advancement. While deep learning has had incredible success, especially when used in narrow, supervised settings, deep learning needs huge labeled databases to be successful. But, new breakthroughs in intelligence will not simply come from using more labeled data. As noted by LeCun, Bengio, and Hinton, “… we expect unsupervised learning to become far more important in the longer term. Human and animal learning is largely unsupervised: we discover the structure of the world by observing it, not by being told the name of every object.”

Traditional supervised models are also susceptible to adversarial attacks and are easily fooled. For example, small perturbations in the pixel intensities of an image that are imperceptible to humans, can
easily alter the output to a target class. This can partially be attributed to the fact that most supervised learning is based upon discriminative learning algorithms. In other words, it models the decision boundary between classes instead of modeling the distribution of classes themselves. Current AI models are also not robust to out-of-distribution data (e.g., ImageNet-C, ImageNet-R, ObjectNet). The out-of-distribution datasets includes cases that naturally happen during model deployment. For example, ImageNet-C provides a standard perturbation benchmark that simulates 75 real-world corruption examples that illustrate the weaknesses of current models.

In general, neural networks have gradually moved away from biological thematics. This has largely been due to engineering breakthroughs and brute force tactics in the past several years that have transformed the field of machine learning. Further engineering of these networks is reaching a saturation point where incremental novelty in the number of layers, activation function, parameter tuning, gradient function, etc., is only producing incremental accuracy improvements. Although there is evidence that AI has reached human levels on certain narrowly defined tasks, for general applications, biological AI remains far superior to that of any computer. Evidence from neuroscience suggest algorithmic and architectural methodology that could drive exciting and new research directions. For example, 95% of synapses in cortex are not related to feed-forward bottom-up drive but rather reflect local inhibitory, long-range lateral and top-down feedback projections, pathways that are mostly ignored by deep learning architectures. Spike timing may be a critical aspect of biological information encoding that have been abstracted away from current ML frameworks. Given the independent advances in neuromorphic software and hardware, machine learning, and neuroscience, the fields are again well positioned for cross pollination.

Topics of interest include but are not limited to:

• Neuromorphic Computing
• Spiking Neural Networks
• Self-supervised/ Unsupervised Learning
• Learning with Less Labels
• Robust Classification
• Generative Machine Learning
• Neuro-inspired AI
• Biologically Plausible AI
• Sparse Coding
• Sparse Distributed Representations
• Energy Efficient Machine Learning
• Top Down Feedback in Machine Learning
• Inhibitory and Excitatory Lateral/Feedback connections
• Cognitive Neural Architectures
• Non-von Neumann computing architectures and models
• Event based systems

Organizers:
Edward Kim, Drexel University
Yijing Watkins, Pacific Northwest National Laboratory
Garrett T. Kenyon, Los Alamos National Laboratory

ST3: Machine Learning in Ophthalmology

The advent of deep learning, a sub-field in Artificial Intelligence (AI), has made a significant impact on many biomedical imaging applications from detection of malignant tissue in mammographs to determining calcium signal propagations in smooth muscle cells. Recently, deep learning models have attracted the attention of researchers and clinicians in the ophthalmic domain. This has led to the approval of the first AI system for automatic diagnosis of diabetic retinopathy (DR) by the food and drug administration (FDA).  In this special track, we plan to bring researchers and clinicians from the two fields of machine learning and ophthalmology to discuss the most recent advances in deep learning that has made significant impacts on the way ophthalmic data is visualized, interpreted, and analyzed for diagnosis of vision threatening diseases.

This special track aims at presenting work inspired and implemented by advances made in the field of computer vision (e.g. deep learning) to help with diagnosis diseases that affect human visual perception. Thus the research presented in this special track covers both aspects of human perception and machine perception in the visual domain.

Topics of interest include but are not limited to:

• Automated Diagnosis of Ophthalmic Conditions
• Anomaly Detection in Ophthalmic Images
• 2D/3D Segmentation of Ophthalmic Data
• Generative Models for Ophthalmic Data Fusion, Analysis, and Interpretation
• Self/Semi- Supervised Learning from Limited Ophthalmic Data

Organizers:
Alireza Tavakkoli, University of Nevada, Reno
Julia Owen, University of Washington

ISVC’20 Special Tracks

ST1: Computational Bioimaging

In recent years extensive research has been performed in the visualization and modeling of objects present in digital images. These images originate in various areas of science and engineering, including medicine, biology, astronomy, and physics. In medicine, for example, computational procedures allow us to clearly visualize and model human organs captured in magnetic resonance images. These procedures may have different purposes, such as 3D shape reconstruction, segmentation, motion and deformation analysis, registration, simulation, and enhanced visualization.

The main goal of the special track is to bring together researchers working in the related fields of Image Acquisition, Segmentation, Registration, Tracking, Matching, Shape Reconstruction, Motion and Deformation Analysis, Medical Imaging, Software Development, Grid, Parallel and High Performing Computing, to discuss and share ideas that will lead us to set the major lines of development for the near future.

Topics of interest include but are not limited to:

• Image Processing and Analysis for Computational Bioimaging;
• Segmentation, Reconstruction, Tracking and Motion Analyse in Biomedical Images;
• Biomedical Signal and Image Acquisition and Processing;
• Computer Aided Diagnosis, Surgery, Therapy, Treatment and Telemedicine Systems;
• Software Development for Computational Bioimaging;
• Grid and High Performance Computing for Computational Bioimaging

Organizers:
Tavares João Manuel R. S. , Universidade do Porto, Portugal
Jorge Renato Natal, Universidade do Porto, Portugal

ST2: Computer Vision Advances in Geo-Spatial Applications and Remote Sensing

Multi-spectral imagery captured from aerial platforms or satellites provides a rich source of information for Earth and/or planetary science and exploration. The special track focuses on gathering the latest advances in machine learning  and computer vision that will make a significant contribution to the field. Topics of interest include but are not limited to sensor localization and calibration, terrain reconstruction, analysis, topographical features recognition and discovery.  Applications include planetary exploration, Earth science (climate change, water and flood detection, deforestation and agriculture) and monitoring (traffic analysis,
surveillance, etc).

Organizers:
Nefian Ara, NASA Ames Research Center, USA
Nestares Oscar, Intel Research, USA
Edwards Laurence, NASA Ames Research Center, USA
Zuleta Ignacio, Planet Labs, USA
Coltin Brian, NASA Ames Research Center, USA
Fong Terry, NASA Ames Research Center, USA

ISVC’19 Special Tracks

ST1: Vision for Remote Sensing and Infrastructure Inspection

Organizers:

Hung M. La, University of Nevada, Reno, USA

Alireza Tavakkoli, University of Nevada, Reno, USA  

Trung-Dung Ngo, University of Prince Edward Island, Canada

Trung H. Duong, Colorado State University- Pueblo, USA

ST2: Computational Vision, AI and Mathematical Methods for Biomedical and Biological Image Analysis 

Organizers:

Sokratis Makrogiannis , Delaware State University, USA

Alberto Santamaria-Pang, General Electric Global Research, USA

ISVC’18 Special Tracks

ST1: AI in Immersive Environments

Organizers:
Tavakkoli Alireza, University of Nevada, Reno, USA
Kim Edward, Villanova University, USA
Alexander Emma-Jane, University of Wyoming, USA
Klassner Frank, Villanova University, USA
Louis Sushil, University of Nevada, Reno, USA

ST2: Computational Bioimaging

Organizers:
Tavares João Manuel R. S. , Universidade do Porto, Portugal
Jorge Renato Natal, Universidade do Porto, Portugal

ST3: Intelligent Transportation Systems

Organizers:
Ambardekar Amol, Microsoft
Morris Brendan, University of Nevada, Las Vegas 

ST4: 3D Surface Reconstruction, Mapping, and Visualization

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

ST5: Intelligent Environments: Algorithms and Applications

Organizers:
Bebis George, University of Nevada, Reno, USA
Nicolescu Mircea, University of Nevada, Reno, USA
Tafazzoli Faezeh, Mercedes-Benz Research and Development, USA

ST6: Unconstrained Biometrics: Advances and Trends

Organizers:
Gholamreza Amayeh, Arraiy, USA
Erol Ali, Eksperta Software, Turkey

ISVC’16 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted through the one-line submission management system and will go through the same review process. When submitting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings.

ST1: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal

ST2: 3D Surface Reconstruction, Mapping, and Visualization

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

ST3: Advancing Autonomy for Aerial Robotics

Organizers:
Alexis Kostas, University of Nevada, Reno, USA
Chli Margarita,, University of Edinburgh, UK
Garcia Carrillo Rodolfo Luis, , University of Nevada, Reno, USA
Nikolakopoulos George, Lulea University of Technology, Sweden
Oettershagen Philipp, ETH Zurich, Switzerland
Oh Paul, University of Nevada, Las Vegas, USA
Papachristos Christos, University of Nevada, Reno, USA

ST4: Computer Vision as a Service

Organizers:
Yu Zeyun , University of Wisconsin-Milwaukee, USA
Arabnia Hamid, University of Georgia, USA
He Max, Marshfield Clinic Research Foundation, USA
Muller Henning, University of Applied Sciences Western, Switzerland
Tafti Ahmad, Marshfield Clinic Research Foundation, USA

ST5: Intelligent Transportation Systems

Organizers:
Ambardekar, Amol, Microsoft, USA
Morris, Brendan, University of Nevada, Las Vegas, USA

ST6: Visual Perception and Robotic Systems

Organizers:
La Hung, University of Nevada, Reno, USA
Sheng Weihua, Oklahoma State University, USA
Fan Guoliang, Oklahoma State University, USA
Kuno Yoshinori, Saitama University, Japan
Ha Quang, University of Technology Sydney, Australia
Zhang Hao, ,Colorado School of Mines, USA
Horn Joachim, Helmut-Schmidt-University, Germany

ISVC’15 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted through the one-line submission management system and will go through the same review process. When submitting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings.

ST1: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal

ST2: 3D Surface Reconstruction, Mapping, and Visualization

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

ST3: Observing Humans

Organizers:
Savakis Andreas, Rochester Institute of Technology, USA
Argyros Antonis, University of Crete, Greece
Asari Vijay, University of Dayton, USA

ST4: Advancing Autonomy for Aerial Robotics

Organizers:
Alexis Kostas, ETH Zurich, Switzerland
Chli Margarita,, University of Edinburgh, UK
Achtelik Markus, ETH Zurich, Switzerland
Kottas Dimitrios, University of Minnesota, USA
Bebis George, University of Nevada, Reno, USA

ST5: Spectral Imaging Processing and Analysis for Environmental, Engineering and Industrial Applications

Organizers:
Doulamis Anastasions (Tasos) , National Technical University of Athens, Greece
Loupos Konstantinos, Institute of Communications and Computer Systems, Greece

ST6: Big Data Visualization and Analytics

Organizers:
Yang Lei, University of Nevada, Reno, USA
Chen Xu, University of Goettingen, Germany
Lin Fuhong, University of Science and Technology Beijing, China
Zhang Rui, University of Hawaii, Honolulu, HI, USA

ST7: Unconstrained Biometrics: Challenges and Applications (tentative)

Organizers:
Proença Hugo, University of Beira Interior, Portugal
Ross Arun, Michigan State University, USA

ST8: Intelligent Transportation Systems

Organizers:
Ambardekar, Amol, Microsoft, USA
Morris, Brendan, University of Nevada, Las Vegas, USA

ST9: Visual Perception and Robotic Systems

Organizers:
La Hung, University of Nevada, Reno, USA
Sheng Weihua, Oklahoma State University, USA
Fan Guoliang, Oklahoma State University, USA
Kuno Yoshinori, Saitama University, Japan
Ha Quang, University of Technology Sydney, Australia
Tran Anthony (Tri), Nanyang Technological University, Singapore
Dinh Kien, Rutgers University, USA

ISVC’14 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted through the one-line submission management system and will go through the same review process. When submitting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings.

ST1: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal
Cunha Alexandre, Caltech, USA
Authors of the best papers presented in this special track will be invited to submit an extended version of their paper to the journal Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization published by Taylor & Francis.

ST2: 3D Mapping, Modeling and Surface Reconstruction

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

ST3: Tracking for Human Activity Monitoring

Organizers:
Savakis Andreas, Rochester Institute of Technology, USA
Argyros Antonis, University of Crete, Greece
Asari Vijay, University of Dayton, USA

ST4: Unmanned Autonomous Systems

Organizers:
Bebis George, University of Nevada, Reno, USA
Nicolescu Mircea, University of Nevada, Reno, USA
Bourbakis Nikolaos, Wright State University, USA
Tavakkoli Alireza, University of Houston, Victoria, USA

ST5: Intelligent Computing and Cyber Security

Organizers:
Sengupta Shamik, University of Nevada, Reno, USA
Li Ming, University of Nevada, Reno, USA
Kim Yoohwan, University of Nevada, Las Vegas, USA
Jo Juyeon, University of Nevada, Las Vegas, USA

ST6: Multimedia Forgery Detection

Organizers:
Hussain Muhammad, King Saud Univesity, Saudi Arabia
Muhammad Ghulam, King Saud Univesity, Saudi Arabia

ST7: Big Data Computer Vision

Organizers:
Sun Zehang, Apple, USA
Wang Junxian, Microsoft, USA

ST8: Unconstrained Biometrics: Challenges and Applications

Organizers:
Proença Hugo, University of Beira Interior, Portugal
Ross Arun, Michigan State University, USA
Ghouzali Sanaa, King Saud University, Saudi Arabia
Rattani Ajita, Michigan State University, USA

ST9: Intelligent Transportation Systems

Organizers:
Ambardekar, Amol, Microsoft, USA
Morris, Brendan, University of Nevada, Las Vegas, USA

ST10: Visual Perception and Robotic Systems

Organizers:
La Hung, University of Nevada, Reno, USA
Sheng Weihua, Oklahoma State University, USA
Vu Tam, University of Colorado, Denver, USA
Fernandez-Marquez Jose Luis, University of Geneva, Switzerland
Nguyen Thang, University of Exeter, UK
Gong Jie, Rutgers University, USA

ISVC’13 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted through the one-line submission management system and will go through the same review process. When submitting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal
Cunha Alexandre, Caltech, USA

ST2: 3D Mapping, Modeling and Surface Reconstruction

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA
Visentin Gianfranco, ESA European Space Research and Technology Centre, The Netherlands
Lourakis Manolis, Foundation for Research and Technology – Hellas, Greece
Chliveros Georgios, Foundation for Research and Technology – Hellas, Greece

ST3: Visual Computing in Digital Cultural Heritage

Organizers:
Doulamis Anastasios D., Technical University of Crete, Greece
Doulamis Nikolaos D., National Technical University of Athens, Greece
Ioannides Marinos, Cyprus University of Technology, Cyprus
Georgopoulos Andreas, National Technical University of Athens, Greece
Voulodimos Athanasios, National Technical University of Athens, Greece

ST4: Graphical Model Inference and Learning for Visual Computing

Organizers:
Komodakis Nikos, Ecole des Ponts ParisTech, France
Kohli Pushmeet, Microsoft Research Cambridge, UK
Kumar Pawan, Ecole Centrale de Paris, France
Blaschko Matthew, Ecole Centrale de Paris, France
Carsten Rother, Microsoft Research Cambridge, UK

ST5: Sparse Methods for Computer Vision, Graphics and Medical Imaging

Organizers:
Metaxas Dimitris, Rutgers University USA
Axel Leon, New York University, USA
Zhang Shaoting, Rutgers University, USA

ST6: Visual Computing in Geoscience and Reservoir Engineering

Organizers:
Brazil Emilio Vital , University of Calgary, Canada
Patel Daniel, Christian Michelsen Research, Norway
Sousa Mario Costa, University of Calgary, Canada

ST7: Visual Computing with Multimodal Data Streams

Organizers:
Zhang Hui, Indiana University, USA
Du Yingzi, Indiana University-Purdue University Indianapolis, Indianapolis, USA
Boyles Mike, Indiana University, USA
Wernert Eric, Indiana University, USA
Thakur Sidharth, Renaissance Computing Institute, USA
Ruan Guangchen, Indiana University, USA

ST8: Intelligent Environments: Algorithms and Applications

Organizers:
Bebis George, University of Nevada, Reno, USA
Nicolescu Mircea, University of Nevada, Reno, USA
Bourbakis Nikolaos, Wright State University, USA
Tavakkoli Alireza, University of Houston, Victoria, USA

ISVC’12 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted through the one-line submission management system and will go through the same review process. When submitting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal
Cunha Alexandre, Caltech, USA

ST2: 3D Mapping, Modeling and Surface Reconstruction

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

ST3: Best Practices in Teaching Visual Computing

Organizers:
Albu Alexandra Branzan, University of Victoria, Canada
Bebis George, University of Nevada, Reno, USA

ST4: Optimization for Vision, Graphics and Medical Imaging

Organizers:
Komodakis Nikos, University of Crete, Greece
Kohli Pushmeet, Microsoft Research Cambridge, UK
Kumar Pawan, Ecole Centrale de Paris, France
Maeder Anthony, University of Western Sydney, Australia
Carsten Rother, Microsoft Research Cambridge, UK

ST5: Deformable Models: Theory and Applications

Organizers:
Tsechpenakis Gavriil, University Indiana University-Purdue University Indianapolis, USA
Huang Xiaolei, Lehigh University, USA

ST6: Perceptual Organization

Organizers:
Parvin Bahram, Lawrence Berkeley National Laboratory, USA
Loss Leandro, Lawrence Berkeley National Laboratory, USA

ST7: Unconstrained Biometrics: Advances and Trends

Organizers:
Proença Hugo, University of Beira Interior, Covilhã, Portugal
Du Yingzi, Indiana University-Purdue University Indianapolis, Indianapolis, USA
Scharcanski Jacob, Federal University of Rio Grande do Sul Porto Alegre, Brazil
Ross Arun, West Virginia University, USA

ST8: Intelligent Environments: Algorithms and Applications

Organizers:
Bebis George, University of Nevada, Reno, USA
Nicolescu Mircea, University of Nevada, Reno, USA
Bourbakis Nikolaos, Wright State University, USA
Tavakkoli Alireza, University of Houston, Victoria, USA

ST9: Object Recognition

Organizers:
Scalzo Fabien, University of California at Los Angeles, USA
Salgian Andrea, The College of New Jersey, USA

ST10: Immersive Visualization

Organizers:
Sherman Bill, Indiana University, USA
Wernert Eric, Indiana University, USA
O’Leary Patrick, VisualIdeation, USA
Coming Daniel, Desert Research Institute, USA

ST11: Face Processing and Recognition

Organizers:
Hussain Muhammad, King Saud Univesity, Saudi Arabia
Muhammad Ghulam, King Saud Univesity, Saudi Arabia
Bebis George, University of Nevada, Reno, USA

ISVC11 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted through the one-line submission management system and will go through the same review process. When submitting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: 3D Mapping, Modeling and Surface Reconstruction

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

Committee:
Bradski Gary, Willow Garage, USA
Zakhor Avideh, University of California at Berkeley, USA
Cavallaro Andrea, University Queen Mary, London, UK
Bouguet Jean-Yves, Google, USA

ST2: Best Practices in Teaching Visual Computing

Organizers:
Albu Alexandra Branzan, University of Victoria, Canada
Bebis George, University of Nevada, Reno, USA

Committee:
Antonacopoulos Apostolos, University of Salford, UK
Bellon Olga Regina Pereira, Universidade Federal do Parana, Brasil
Bowyer Kevin, University of Notre Dame, USA
Crawfis Roger, Ohio State University, USA
Hammoud Riad, DynaVox Systems, USA
Kakadiaris Ioannis, University of Houston, USA
Lladós Josep, Universitat Autonoma de Barcelona, Spain
Sarkar Sudeep, University of South Florida, USA

ST3: Immersive Visualization

Organizers:
Sherman Bill, Indiana University, USA
Wernert Eric, Indiana University, USA
O’Leary Patrick, University of Calgary, Canada
Coming Daniel, Desert Research Institute, USA

Committee:
Su Simon, Princeton University, USA
Folcomer Samuel, Brown University, USA
Brady Rachael, Duke University, USA
Johnson Andy, University of Illinois at Chicago, USA
Kreylos Oliver, University of California at Davis, USA
Will Jeffrey, Valparaiso University, USA
Moreland John, Purdue University, Calumet, USA
Leigh Jason, University of Illinois, Chicago, USA
Schulze Jurgen, University of California, San Diego, USA
Sanyal Jibonananda, Mississippi State University of , USA
Stone John, University of Illinois, Urbana­‐Champaign, USA
Kuhlen Torsten, Aachen Univeristy, Germany

ST4: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal
Cunha Alexandre, Caltech, USA

Committee:
Santis De Alberto, Università degli Studi di Roma “La Sapienza”, Italy
Reis Ana Mafalda, Instituto de Ciências Biomédicas Abel Salazar, Portugal
Barrutia Arrate Muñoz, University of Navarra, Spain
Calvo Begoña, University of Zaragoza, Spain
Constantinou Christons, Stanford University, USA
Iacoviello Daniela, Università degli Studi di Roma “La Sapienza”, Italy
Ushizima Daniela, Lawrence Berkeley National Lab, USA
Ziou Djemel, University of Sherbrooke, Canada
Pires Eduardo Borges, Instituto Superior Técnico, Portugal
Sgallari Fiorella, University of Bologna, Italy
Perales Francisco, Balearic Islands University, Spain
Qiu Guoping, University of Nottingham, UK
Hanchuan Peng, Howard Hughes Medical Institute, USA
Pistori Hemerson, Dom Bosco Catholic University, Brasil
Yanovsky Igor, Jet Propulsion Laboratory, USA
Corso Jason, SUNY at Buffalo, USA
Maldonado Javier Melenchón , Open University of Catalonia, Spain
Marques Jorge S., Instituto Superior Técnico, Portugal
Aznar Jose M. García, University of Zaragoza, Spain
Vese Luminita, University of California at Los Angeles, USA
Reis Luís Paulo, University of Porto, Portugal
Thiriet Marc, Universite Pierre et Marie Curie (Paris VI), France
Mahmoud El-Sakka, The University of Western Ontario London, Canada
Hidalgo Manuel González, Balearic Islands University, Spain
Gurcan Metin N., Ohio State University, USA
Dubois Patrick, Institut de Technologie Médicale, France
Barneva Reneta P., State University of New York, USA
Bellotti Roberto, University of Bari, Italy
Tangaro Sabina, University of Bari, Italy
Silva Susana Branco, University of Lisbon, Portugal
Brimkov Valentin, State University of New York, USA
Zhan Yongjie, Carnegie Mellon University, USA

ST5: Interactive Visualization in Novel and Heterogeneous Display Environments

Organizers:
Rosenbaum Rene, University of California, Davis, USA
Tominski Christian, University of Rostock, Germany

Committee:
Isenberg Petra,, INRIA, France
Isenberg Tobias, University of Groningen, The Netherlands and CNRS/INRIA, France
Kerren Andreas, Linnaeus University, Sweden
Majumder Aditi, University of California, Irvine, USA
Quigley Aaron, University of St Andrews, UK
Schumann Heidrun, University of Rostock, Germany
Sips Mike, GFZ Potsdam, Germany
Slavik Pavel, Czech Technical University in Prague, Czech Republic
Weiskopf Daniel, University of Stuttgart, Germany

ISVC10 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted thourgh the one-line submission management system and will go through the same review process. When submiting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: 3D Mapping, Modeling and Surface Reconstruction

Organizers:
Nefian Ara, Carnegie Mellon University/NASA Ames Research Center, USA
Broxton Michael, Carnegie Mellon University/NASA Ames Research Center, USA
Huertas Andres, NASA Jet Propulsion Lab, USA

Committee:
Hancher Matthew , NASA Ames Research Center, USA
Edwards Laurence, NASA Ames Research Center, USA
Bradski Gary, Willow Garage, USA
Zakhor Avideh, University of California at Berkeley, USA
Cavallaro Andrea, University Queen Mary, London, UK
Bouguet Jean-Yves, Google, USA

ST2: Best Practices in Teaching Visual Computing

Organizers:
Albu Alexandra Branzan, University of Victoria, Canada
Bebis George, University of Nevada, Reno, USA

Committee:
Bergevin Robert, University of Laval, Canada
Crawfis Roger, Ohio State University, USA
Hammoud Riad, DynaVox Systems, USA
Kakadiaris Ioannis, University of Houston, USA, USA
Laurendeau Denis, Laval University, Quebec, Canada
Maxwell Bruce, Colby College, USA
Stockman George, Michigan State University, USA

ST3: Low-Level Color Image Processing

Organizers:
Celebi M. Emre, Louisiana State University, USA
Smolka Bogdan, Silesian University of Technology, Poland
Schaefer Gerald, Loughborough University, UK
Plataniotis Konstantinos, University of Toronto, Canada
Horiuchi Takahiko, Chiba University, Japan

Committee:
Aygun Ramazan , University of Alabama in Huntsville, USA
Battiato Sebastiano, University of Catania, Italy
Hardeberg Jon, Gjøvik University College, Norway
Hwang Sae, University of Illinois at Springfield, USA
Kawulok Michael, Silesian University of Technology, Poland
Kockara Sinan, University of Central Arkansas, USA
Kotera Hiroaki, Kotera Imaging Laboratory, Japan
Lee JeongKyu, University of Bridgeport, USA
Lezoray Olivier, University of Caen, France
Mete Mutlu, Texas A&M University – Commerce, USA
Susstrunk Sabine, Swiss Federal Institute of Technology in Lausanne, Switzerland
Tavares Joao, University of Porto, Portugal
Tian Gui Yun, Newcastle University, UK
Wen Quan, University of Electronic Science and Technology of China, China
Zhou Huiyu, Queen’s University Belfast, UK

ST4: Low Cost Virtual Reality: Expanding Horizons

Organizers:
Sherman Bill, Indiana University, USA
Wernert Eric, Indiana University, USA

Committee:
Coming Daniel, Desert Research Institute, USA
Craig Alan, University of Illinois/NCSA, USA
Keefe Daniel, University of Minnesota, USA
Kreylos Oliver, University of California at Davis, USA
O’Leary Patrick, Idaho National Laboratory, USA
Smith Randy, Oakland University, USA
Su Simon, Princeton University, USA
Will Jeffrey, Valparaiso University, USA

ST5: Computational Bioimaging

Organizers:
Tavares João Manuel R. S., University of Porto, Portugal
Natal Jorge Renato, University of Porto, Portugal
Cunha Alexandre, Caltech, USA

Committee:
Santis De Alberto, Università degli Studi di Roma “La Sapienza”, Italy
Reis Ana Mafalda, Instituto de Ciências Biomédicas Abel Salazar, Portugal
Barrutia Arrate Muñoz, University of Navarra, Spain
Calvo Begoña, University of Zaragoza, Spain
Constantinou Christons, Stanford University, USA
Iacoviello Daniela, Università degli Studi di Roma “La Sapienza”, Italy
Ushizima Daniela, Lawrence Berkeley National Lab, USA
Ziou Djemel, University of Sherbrooke, Canada
Pires Eduardo Borges, Instituto Superior Técnico, Portugal
Sgallari Fiorella, University of Bologna, Italy
Perales Francisco, Balearic Islands University, Spain
Qiu Guoping, University of Nottingham, UK
Hanchuan Peng, Howard Hughes Medical Institute, USA
Pistori Hemerson, Dom Bosco Catholic University, Brasil
Yanovsky Igor, Jet Propulsion Laboratory, USA
Corso Jason, SUNY at Buffalo, USA
Maldonado Javier Melenchón , Open University of Catalonia, Spain
Marques Jorge S., Instituto Superior Técnico, Portugal
Aznar Jose M. García, University of Zaragoza, Spain
Vese Luminita, University of California at Los Angeles, USA
Reis Luís Paulo, University of Porto, Portugal
Thiriet Marc, Universite Pierre et Marie Curie (Paris VI), France
Mahmoud El-Sakka, The University of Western Ontario London, Canada
Hidalgo Manuel González, Balearic Islands University, Spain
Gurcan Metin N., Ohio State University, USA
Dubois Patrick, Institut de Technologie Médicale, France
Barneva Reneta P., State University of New York, USA
Bellotti Roberto, University of Bari, Italy
Tangaro Sabina, University of Bari, Italy
Silva Susana Branco, University of Lisbon, Portugal
Brimkov Valentin, State University of New York, USA
Zhan Yongjie, Carnegie Mellon University, USA

ST6: Unconstrained Biometrics: Advances and Trends

Organizers:
Proença Hugo, University of Beira Interior, Portugal
Du Yingzi, Indiana University-Purdue University Indianapolis, USA
Scharcanski Jacob, Federal University of Rio Grande do Sul Porto Alegre, Brazil
Ross Arun, West Virginia University, USA
Amayeh Gholamreza, EyeCom Corporation, USA

Committee:
Júnior Adalberto Schuck,, Federal University of Rio Grande do Sul, Brazil
Kwolek Bogdan,, Rzeszów University of Technology, Poland
Jung Cláudio R.,, Federal University of Rio Grande do Sul, Brazil
Alirezaie Javad, Ryerson University, Canada
Konrad Janusz,, Boston University, USA
Kevin Jia,, International Game Technologies, USA
Meyer Joceli,, Federal University of Santa Catarina, Brazil
Alexandre Luís A.,, University of Beira Interior, Portugal
Soares Luis,, ISCTE, Portugal
Coimbra Miguel,, University of Porto, Portugal
Fieguth Paul,, University of Waterloo, Canada
Xiao Qinghan,, Defense Research and Development Canada, Canada
Ives Robert,, United States Naval Academy, USA
Tamir Samir,, Ingersoll Rand Security, USA

ST7: Perceptual Organization

Organizers:
Parvin Bahram, Lawrence Berkeley National Laboratory, USA
Loss Leandro, Lawrence Berkeley National Laboratory, USA

Committee:
Sarkar Suddep, University of South Florida, USA
Ben-Shahar Ohad, Ben-Gurion University of the Negev, Israel
Skurikhin Alexei, Los Alamos National Laboratory, USA
Tavakkoli Alireza, University of Houston-Victoria, USA

ST8: Behavior Detection and Modeling

Organizers:
Miller Ron, Wright-Patterson Air Force Base, USA
Bebis George, University of Nevada, USA
Rosen Julie, Science Applications International Corporation, USA
Davis Jim, Ohio State University, USA
Lee Simon, Army Research Laboratory, USA
Zandipour Majid, BAE Systems, USA

ISVC09 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted thourgh the one-line submission management system and will go through the same review process. When submiting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: 3D Mapping, Modeling and Surface Reconstruction

Organizers:
Ara Nefian, Carnegie Mellon University/NASA Ames Research Center, USA
Michael Broxton, Carnegie Mellon University/NASA Ames Research Center, USA
Andres Huertas, NASA Jet Propulsion Lab, USA

Committee:
Matthew Hancher , NASA Ames Research Center, USA
Laurence Edwards, NASA Ames Research Center, USA
Gary Bradski, Willow Garage, USA
Avideh Zakhor, University of California at Berkeley, USA
Andrea Cavallaro, University Queen Mary, London, UK
Jean-Yves Bouguet, Google, USA

ST2: Object Recognition

Organizers:
Andrea Selinger Salgian, The College of New Jersey, USA
Fabien Scalzo, University of Rochester, USA

Committee:
Robert Bergevin, University of Laval, Canada
Bastian Leibe, ETH Zurich, Switzerland
Vincent Lepetit, EPFL, Switzerland
Bogdan Matei, Sarnoff Corporation, USA
Raphael Maree, Universite de Liege, Belgium
Randal Nelson, University of Rochester, USA
Guo-Jun Qi, University of Science and Technology of China, China
Nicu Sebe, University of Amsterdam, The Netherlands
Tinne Tuytelaars, Katholieke Universiteit Leuven, Belgium
Andrea Vedaldi, Oxford University, UK
Michel Vidal-Naquet, RIKEN Brain Science Institute, Japan

ST3: Deformable Models: Theory and Applications

Organizers:
Demetri Terzopoulos, University of California, Los Angeles, USA
Gavriil Tsechpenakis, University of Miami, USA
Xiaolei Huang, Lehigh University, USA

Discussion Panel:
Dimitris Metaxas (Chair), Rutgers University, USA

Committee:
Elsa Angelini, Ecole Nationale Supérieure de Télécommunications, France
David E. Breen, Drexel University, USA
Ting Chen, Rutgers University, USA
Yunmei Chen, University of Florida, USA
Herve Delingette, INRIA, France
Patrice Delmas, University of Auckland, New Zealand
Ayman El-Baz, University of Louisville, USA
Aly A. Farag, University of Louisville, USA
Benjamin B. Kimia, Brown University, USA
Chandra Kambhamettu, University of Delaware, USA
Nadia Magnenat-Thalmann, University of Geneva, Switzerland
Tim McInerney, Ryerson University, Canada
Dimitris Metaxas, Rutgers University, USA
Kannappan Palaniappan, University of Missouri, USA
Nikos Paragios, Ecole Centrale de Paris, France
Hong Qin, Stony Brook University, USA
Mathieu Salzmann, UC Berkeley, USA
Eftychios Sifakis, University of California at Los Angeles, USA
Oskar Skrinjar, Georgia Tech, USA
Gabor Szekely, ETH Zurich, Switzerland
Joseph Teran, University of California at Los Angeles, USA
Daniel Thalmann, EPFL, Switzerland

ST4: Visualization Enhanced Data Analysis for Health Applications

Organizers:
Irene Cheng, University of Alberta, Canada
Anthony Maeder, University of Western Sydney, Australia

Committee:
Walter Bischof, University of Alberta, Canada
Pierre Boulanger, University of Alberta, Canada
Ross Brown, Queensland University of Technology, Australia
Jason Dowling, CSIRO, Australia
Pablo Figueroa, Universidad de los Andes, Columbia
Liwan Liyanage, University of Western Sydney, Australia
Tom Malzbender, HP Labs, USA
Mrinal Mandal, University of Alberta, Canada
Steven Miller, University of British Columbia, Canada
Quang Vinh Nguyen, University of Western Sydney, Australia
Hao Shi, Victoria University, Australia
Jiambo Shi, University of Pennsylvania, USA
Claudio Silva, University of Utah, USA
Simeon Simoff, University of Western Sydney, Australia
Lijun Yin, University of Utah, USA
Xenophon Zabulis, Institute of Computer Science-FORTH, Greece
Pietro Zanuttigh, University of Padova, Italy

ST5: Computational Bioimaging

Organizers:
João Manuel R. S. Tavares, University of Porto, Portugal
Renato Natal Jorge, University of Porto, Portugal
Alexandre Cunha, Caltech, USA

Committee:
Alberto De Santis, Università degli Studi di Roma “La Sapienza”, Italy
Alexandre Xavier Falcão, University of Campinas, Brazil
Ana Mafalda Reis, Instituto de Ciências Biomédicas Abel Salazar, Portugal
Arrate Muñoz Barrutia, University of Navarra, Spain
Begoña Calvo, University of Zaragoza, Spain
Constantine Kotropoulos, Aristotle University of Thessaloniki, Greece
Daniela Iacoviello, Università degli Studi di Roma “La Sapienza”, Italy
Denilson Laudares Rodrigues, PUC Minas, Brazil
Dinggang Shen, University of Pennsylvania, USA
Djemel Ziou, University of Sherbrooke, Canada
Eduardo Borges Pires, Instituto Superior Técnico, Portugal
Fiorella Sgallari, University of Bologna, Italy
Francisco Perales, Balearic Islands University, Spain
Gustavo Rohde, Carnegie Mellon University, USA
Hanchuan Peng, Howard Hughes Medical Institute, USA
Hélder C. Rodrigues, Instituto Superior Técnico, Portugal
Hemerson Pistori, Dom Bosco Catholic University, Brasil
Huiyu Zhou, Brunel University, UK
Igor Yanovsky, Jet Propulsion Laboratory, USA
Jason Corso, SUNY at Buffalo, USA
Javier Melenchón Maldonado, Open University of Catalonia, Spain
Jorge M. G. Barbosa, University of Porto, Portugal
Jorge S. Marques, Instituto Superior Técnico, Portugal
Jose M. García Aznar, University of Zaragoza, Spain
Jussi Tohka, Tampere University of Technology, Finland
Luminita Vese, University of California at Los Angeles, USA
Luís Paulo Reis, University of Porto, Portugal
Mahmoud El-Sakka, The University of Western Ontario London, Canada
Manuel González Hidalgo, Balearic Islands University, Spain
Maria Elizete Kunkel, Universität Ulm, Germany
Metin N. Gurcan, Ohio State University, USA
Michael Liebling, University of California at Santa Barbara, USA
Patrick Dubois, Institut de Technologie Médicale, France
Renato M. N. Jorge, University of Porto, Portugal
Reneta P. Barneva, State University of New York, USA
Roberto Bellotti, University of Bari, Italy
Sabina Tangaro, University of Bari, Italy
Shawn Newsam, University of California at Merced, USA
Susana Branco Silva, University of Lisbon, Portugal
Todd Pataky, University of Liverpool, UK
Valentin Brimkov, State University of New York, USA
Yongjie Zhan, Carnegie Mellon University, USA

ST6: Visual Computing for Robotics

Organizers:
Frederic CHAUSSE, Clermont Universite, France

Committee:
Didier Aubert, LIVIC, France
Thierry Chateu, Clermont Universite, France
Roland CHAPUIS, Clermont Université, France
Nicolas Hautiere, LCPC/LEPSIS, France
Eric Royer, Clermont Universite, France
Kostas Bekris, University of Nevada, Reno, USA

ST7: Optimization for Vision, Graphics and Medical Imaging: Theory and Applications

Organizers:
Nikos Komodakis, University of Crete, Greece
Georg Langs, University of Vienna, Austria

Committee:
Nikos Paragios, Ecole Centrale de Paris/INRIA Saclay Ile-de-France, France
Horst Bischof, Graz University of Technology, Austria
Daniel Cremers, University of Bonn, Germany
Leo Grady, Siemens Corporate Research, USA
Nassir Navab, Technical University of Munich, Germany
Dimitris Samaras, Stony Brook University, USA
Victor Lempitsky, Microsoft Research Cambridge, UK
Georgios Tziritas, University of Crete, Greece
Thomas Pock, Graz University of Technology, Austria
Branislav Micusik, Austrian Research Centers GmbH – ARC, Austria
Ben Glocker, Technical University of Munich, Germany

ST8: Semantic Robot Vision Challenge

Organizers:
Paul E. Rybski, Carnegie Mellon University, USA
Daniel DeMenthon, Johns Hopkins University, USA
Cornelia Fermuller, University of Maryland, USA
Pooyan Fazli, University of British Columbia, Canada
Ajay Mishra, National University of Singapore, Singapore
Luis Lopes, Universidade de Aveiro, Portugal
Florian Roehrbein, Universitaet Bremen, Germany
David Gustafson, Kansas State University, USA

ISVC08 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. For paper submission guidelines, please click here. Special track papers should be submitted thourgh the one-line submission management system and will go through the same review process. When submiting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: Semi-supervised Learning for Visual Computing: Theory and Applications

Organizers:
Yunqian Ma, Honeywell Labs, USA
Ming Dong, Wayne State University, USA

Committee:
William Grosky , University of Michigan at Dearborn, USA
Jing Hua, Wayne State University, USA
Manjeet Rege, Rochester Institute of Technology, USA
Zhanping Liu, Mississippi State University, USA
Haiyang Liu, Honeywell Labs, USA

ST2: Object Recognition

Organizers:
Andrea Selinger Salgian, The College of New Jersey, USA
Fabien Scalzo, University of Rochester, USA

Committee:
Boris Epshtein, The Weizmann Institute of Science, Israel
Svetlana Lazebnik, University of North Carolina at Chapel Hill, USA
Bastian Leibe, ETH Zurich, Switzerland
Vincent Lepetit, EPFL, Switzerland
Ales Leonardis, University of Ljubljana, Slovenia
Bogdan Matei, Sarnoff Corporation, USA
Raphael Maree, Universite de Liege, Belgium
Randal Nelson, University of Rochester, USA
Justus Piater, Universite de Liege, Belgium
Bill Triggs, INRIA, France
Tinne Tuytelaars, Katholieke Universiteit Leuven, Belgium
Michel Vidal-Naquet, RIKEN Brain Science Institute, Japan

ST3: Real-time Vision Algorithm Implementation and Application

Organizers:
D. J. Lee, Brigham Young University, USA
James Archibald, Brigham Young University, USA
Brent Nelson, Brigham Young University, USA
Doran Wilde, Brigham Young University, USA

Committee:
Jiun-Jian Liaw, Chaoyang University of Technology, Taiwan
Che-Yen Wen, Central Police University, Taiwan
Yuan-Liang Tang, Chaoyang University of Technology, Taiwan
Hsien-Chou Liao, Chaoyang University of Technology, Taiwan

ST4: Visualization and Simulation on Immersive Display Devices

Organizers:
Daniel Coming, Desert Research Institute, USA
Darko Koracin, Desert Research Institute, USA
Laura Monroe, Los Alamos National Lab, USA
Rachael Brady, Duke University, USA

Committee:
Andy Forsberg, Brown University, USA
Bernd Hamann, University of California, Davis, USA
Arie Kaufman, Stony Brook University (SUNY), USA
Phil McDonald, Desert Research Institute, USA
Dave Modl, LANL/LAVA/Worldscape, USA
Patrick O’Leary, Desert Research Institute, USA
Dirk Reiners, LITE, USA
Bill Sherman, Desert Research Institute, USA
Steve Smith, LANL/LAVA/Worldscape, USA
Oliver Staadt, University of Rostock, Germany

ST5: Analysis and Visualization of Biomedical Visual Data

Organizers:
Irene Cheng, University of Alberta, Canada
Anthony Maeder, University of Western Sydney, Australia

Committee:
Walter Bischof, University of Alberta, Canada
Pierre Boulanger, University of Alberta, Canada
Ross Brown, Queensland University of Technology, Australia
Pablo Figueroa, Universidad de los Andes, Columbia
Carlos Flores, University of Alberta, Canada
Paul Jackway, CSIRO, Australia
Shoo Lee, iCARE, Capital Health, Canada
Tom Malzbender, HP Labs, USA
Mrinal Mandal, University of Alberta, Canada
Steven Miller, University of British Columbia, Canada
Jiambo Shi, University of Pennsylvania, USA
Claudio Silva, University of Utah, USA
Dimitris Gramenos, Institute of Computer Science-FORTH, Greece
Lijun Yin, University of Utah, USA
Xenophon Zabulis, Institute of Computer Science-FORTH, Greece
Jeffrey Zou, University of Western Sydney, Australia

ST6: Soft Computing in Image Processing and Computer Vision

Organizers:
Gerald Schaefer, Nottingham Trent University, UK
Mike Nachtegael, Ghent University, Belgium
Aboul-Ella Hassanien, Cairo University, Egypt

Committee:
Hüseyin Çakmak, Forschungszentrum Karlsruhe, Germany
Emre Celebi, Louisiana State University, USA
Kevin Curran, University of Ulster, Northern Ireland
Mostafa A. El-Hosseini, Mubarak City for Science and Technology, Egypt
Hajime Nobuhara, Tokyo Institute of Technology, Japan
Samuel Morillas, Technical University of Valencia, Spain
Daniel Sanchez, University of Granada, Spain
Mayank Vatsa, University of Virginia, USA
Ioannis Vlachos, Aristotle University of Thessaloniki, Greece
Huiyou Zhou, Brunel University, UK

ST7: Computational Bioimaging and Visualization

Organizers:
João Manuel R. S. Tavares, University of Porto, Portugal
Renato Natal Jorge, University of Porto, Portugal
Goswami Samrat, University of Texas at Austin, USA

Committee:
Alberto De Santis, Università degli Studi di Roma “La Sapienza”, Italy
Ana Mafalda Reis, Instituto de Ciências Biomédicas Abel Salazar, Portugal
Arrate Muñoz Barrutia, University of Navarra, Spain
Chang-Tsun Li, University of Warwick, UK
Christos E. Constantinou, Stanford University School of Medicine, USA
Mrinal Mandal, University of Alberta, Canada
Daniela Iacoviello, Università degli Studi di Roma “La Sapienza”, Italy
Dinggang Shen, University of Pennsylvania, USA
Eduardo Borges Pires, Instituto Superior Técnico, Portugal
Enrique Alegre Gutiérrez, University of León, Spain
Filipa Sousa, University of Porto, Portugal
Gerhard A. Holzapfel, Royal Institute of Technology, Sweden
Hélder C. Rodrigues, Instituto Superior Técnico, Portugal
Hemerson Pistori, Dom Bosco Catholic University, Brasil
Jorge M. G. Barbosa, University of Porto, Portugal
Jorge S. Marques, Instituto Superior Técnico, Portugal
Jose M. García Aznar, University of Zaragoza, Spain
Luís Paulo Reis, University of Porto, Portugal
Manuel González Hidalgo, Balearic Islands University, Spain
Michel A. Audette, University of Leipzig, Germany
Patrick Dubois, Institut de Technologie Médicale, France
Reneta P. Barneva, State University of New York, USA
Roberto Bellotti, University of Bari, Italy
Sabina Tangaro, University of Bari, Italy
Sónia I. Gonçalves-Verheij, VU University Medical Centre, The Netherlands
Valentin Brimkov, State University of New York, USA
Yongjie Zhan, Carnegie Mellon University, USA
Xavier Roca Marvà, Autonomous University of Barcelona, Spain

ST8: Focus of Attention in Vision Systems

Organizers:
Frederic CHAUSSE, Clermont Universite, France
Roland CHAPUIS, Clermont Université, France

Committee:
Laurent Itti, University of Southern California, USA
Noel Trujillo, Clermont Université, France

ST9: Discrete and Computational Geometry and their Applications in Visual Computing

Organizers:
Valentin Brimkov, State University of New York, USA
Reneta Barneva, State University of New York, USA

Committee:
K. Joost Batenburg, University of Antwerp, Belgium
Bedrich Benes, Purdue University, USA
Isabelle Debled-Rennesson, Institut Univ de Formation des Maitres de Lorraine, France
Christophe Fiorio, LIRMM, University Montpellier II, France
Gisela Klette, University of Auckland, New Zealand
Reinhard Klette, University of Auckland, New Zealand
Kostadin Koroutchev, Universidad Autónoma de Madrid, Spain
Benedek Nagy, University of Debrecen, Hungary
Kalman Palagyi, University of Szeged, Hungary
Arun Ross, West Virginia University, USA
K.G. Subramanian, Universiti Sains, Malaysia
João Manuel R. S. Tavares, University of Porto, Portugal

ST10: Image Analysis for Remote Sensing Data

Organizers:
Jose A. Malpica, Alcala University, Spain
Maria A. Sanz, Technical University of Madrid, Spain
Maria C. Alonso, Alcala University, Spain

Committee:
Hossein Arefi, Stuttgart University of Applied Sciences, Germany
Manfred Ehlers, University of Osnabrueck, Germany
María J. García-Rodríguez , University of Madrid, Spain
John L.van Genderen, ITC, The Netherlands
Radja Khedam, Technology and Sciences University, Algeria
José L. Lerma, Technical University of Valencia, Spain
Qingquan Li, Wuhan University, China
Dimitris Manolakis, MIT Lincoln Laboratory, USA
Farid Melgani, University of Trento, Italy
Jon Mills, University of Newcastle, UK
Francisco Papí, IGN, Spain
Karel Pavelka, Technical University in Prague, Czech Republic
William D. Philpot, Cornell University, USA
Daniela Poli, Swiss Federal Institute of Technology, Switzerland
Mohammad-Reza Saradjian, University of Tehran, Iran
Sriparna Saha, Indian Statistical Institute, India

ISVC07 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. Special track papers should be submitted thourgh the one-line submission management system and will go through the same review process. When submiting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: Intelligent Algorithms for Smart Monitoring of Complex Environments

Organizers:
Paolo Remagnino, DIRC, Kingston University, UK
Dorothy Monekosso, DIRC, Kingston University, UK
How-Lung Eng, IIR, Singapore
Yau Wei Yun, IIR, Singapore
Guoliang Fan, Oklahoma State University, USA
Yunqian Ma, Honeywell Labs, USA

Committee:
Andrea Prati, University of Modena, Italy
Christian Micheloni, University of Udine, Italy
Yoshinori Kuno, Saitama University, Japan
Mohan Trivedi, University of California, San Diego, USA
Daniele Nardi, La Sapienza, Rome, Italy
Andrea Cavallaro, Queen Mary, University of London, UK
Luca Iocchi, La Sapienza, Rome, Italy
Monique Thonnat, INRIA, Sophia Antipolis, France
James Ferryman, Reading University, UK
Klaus Diepold, University of Technology in Munich, Germany
Peter Sturm, INRIA, Grenoble, France

ST2: Object Recognition

Organizers:
Andrea Selinger Salgian, The College of New Jersey, USA
Fabien Scalzo, University of Nevada, Reno, USA

Committee:
Boris Epshtein, The Weizmann Institute of Science, Israel
Bastian Leibe, ETH Zurich, Switzerland
Bogdan Matei, Sarnoff Corporation, USA
Raphael Maree, Universite de Liege, Belgium
Randal Nelson, University of Rochester, USA
Justus Piater, Universite de Liege, Belgium
Nicu Sebe, University of Amsterdam, The Netherlands
Bill Triggs, INRIA, France
Tinne Tuytelaars, Katholieke Universiteit Leuven, Belgium

ST3: Image Databases

Organizers:
Sanjiv K. Bhatia, University of Missouri-St. Louis, USA
Ashok Samal, University of Missouri-St. Louis, USA
Bedrich Benes, Purdue University, USA
Sharlee Climer, Washington University in St. Louis, USA

ST4: Algorithms for the Understanding of Dynamics in Complex and Cluttered Scenes

Organizers:
Paolo Remagnino, DIRC, Kingston University, UK
Fatih Porikli, MERL, USA
Larry Davis, University of Maryland, USA
Massimo Piccardi, University of Technology Sydney, Australia

Committee:
Rita Cucchiara, University of Modena, Italy
Gian Luca Foresti, University of Udine, Italy
Yoshinori Kuno, Saitama University, Japan
Mohan Trivedi, University of California, San Diego, USA
Andrea Prati, University of Modena, Italy
Carlo Regazzoni, University of Genoa, Italy
Graeme Jones, Kingston University, UK
Steve Maybank, Birkbeck University of London, UK
Ram Nevatia, University of Southern California, USA
Sergio Velastin, Kingston University, USA
Monique Thonnat, INRIA, Sophia Antipolis, France
Tieniu Tan, National Lab of Pattern Recognition, China
James Ferryman, Reading University, UK
Andrea Cavallaro, Queen Mary, University of London, UK
Klaus Diepold, University of Technology in Munich, Germany

ST5: Mutli-dimensional Medical Data Analysis, Visualization and Transmission

Organizers:
Irene Cheng, University of Alberta, Canada
Guido Gortelazzo, University of Padova, Italy
Kostas Daniilidis, University of Pennsylvania, USA
Pablo Figueroa, Universidad de los Andes, Colombia
Tom Malzbender, Hewlett Packard Lab., USA
Mrinal Mandal, University of Alberta, USA
Lijun Yin, State University of New York at Binghamton, USA
Karel Zuiderveld, Vital Images Inc., USA

Committee:
Walter Bischof, University of Alberta, Canada
Anup Basu, University of Alberta, Canada
Paul Major, University of Alberta, Canada
Tarek El-Bialy, University of Alberta, Canada
Jana Carlos Flores, University of Alberta, Canada
Randy Goebel, University of Alberta, Canada
David Hatcher, DDI Central Corp., USA
Shoo Lee, iCARE, Capital Health, Canada
Jiambo Shi, University of Pennsylvania, USA
Garnette Sutherland, University of Calgary, Canada

ST6: Soft Computing in Image Processing and Computer Vision

Organizers:
Gerald Schaefer, Nottingham Trent University, UK
Mike Nachtegael, Ghent University, Belgium
Lars Nolle, Nottingham Trent University, UK
Etienne Kerre, Ghent University, Belgium

ST7: Trends In Development of Visual Computing Applications

Organizers:
Pablo Figueroa, Universidad de los Andes, Colombia
Laura Monroe, Los Alamos National Lab, USA
Laura Arns, Purdue University, USA

ISVC06 Special Tracks

Papers submitted to a special track must not have been previously published, and must not be currently under consideration for publication elsewhere. Special track papers should be submitted thourgh the one-line submission management system and will go through the same review process. When submiting a special track paper, please choose the appropriate special track from the list of topics. All special track papers will be published in the symposium proceedings. Authors contributing to a special track would be required to register for the symposium.

ST1: Intelligent Environments: Algorithms and Applications

Organizers:
Paolo Remagnino, DIRC, Kingston University, UK
How-Lung Eng, IIR, Singapore
Wei-Yun Yau, IIR, Singapore
Guoliang Fan, Oklahoma State University, USA
Yunqian Ma, Honeywell Labs, USA
Monique Thonnat, INRIA, France

* Selected papers from this special track will be considered for publication in a special issue
of the International Journal of Knowledge-Based and Intelligent Engineering Systems.

ST2: Multimodal Data Understanding and Visualization for Industrial Applications

Organizers:
Fatih Porikli, MERL, USA
Andrea Cavallaro, Queen Mary, University of London, UK

Committee:
Rama Chellapa, University of Maryland, USA
Yuri Ivanov, MERL, USA
Swarup Medasani, HRL, USA
Ron Miller, Ford Motor Company, USA
Chris Wren, MERL, USA

ST3: Pattern Analysis and Recognition Applications in Biometrics

Organizers:
Ali Erol, University of Nevada, Reno, USA
Mark Nixon, University of Southampton, UK
Salil Prabhakar, DigitalPersona, USA
Arun Abraham Ross, West Virginia University, USA

ST4: Analysis and Synthesis of Multimodal Human Communications for Perceptual Interface

Organizers:
Mohammed Yeasin, University of Memphis, USA
Max M. Louwerse, University of Memphis, USA
Kuntal Sengupta, AuthenTec Inc., USA
S. Kettebekov, Keane Inc., USA

ST5: Biomedical Image Analysis

Organizers:
Tao Ju, Washington University, USA
Ioannis Kakadiaris, University of Houston, USA
Shi Pengcheng, Hong Kong University of Science and Technology, China
Tomas Gustavsson, Chalmers University of Technology, Sweden

ST6: Understanding and Imitating Nature: Analysis, Interpretation, Rendering and Inspiration of Biological Forms

Organizers:
Paolo Remagnino, DIRC, Kingston University, UK
Richard Boyle, NASA Ames, USA
Paul Wilkin, The Royal Botanic Gardens, UK
Jonathan Clark, University of Surrey, UK
Sarah Barman, Kingston University, UK

ST7: Visual Computing and Biological Vision

Organizers:
Jeff Mulligan, NASA Ames, USA
Michael Webster, University of Nevada, Reno, USA
Alice O’Toole, University of Texas at Dallas, USA

ST8: 4D Medical Data Modeling, Visualization and Measurement

Organizers:
Irene Cheng, University of Alberta, Canada
Randy Goebel, University of Alberta, Canada
Lijun Yin, State University of New York, USA

Committee:
Walter Bischof, University of Alberta, Canada
Pierre Boulanger, University of Alberta, Canada
Paul Major, University of Alberta, Canada
Brian Maraj, University of Alberta, Canada
Jana Rieger, Misericordia Community Hospital, Canada
Carol Boliek, University of Alberta, Canada

ST9: Discrete and Computational Geometry and their Applications in Visual Computing

Organizers:
Valentin Brimkov, State University of New York, USA
Reneta Barneva, State University of New York, USA

Committee:
Eric Andres, Universite de Poitiers, France
David Coeurjolly, Universite Claude Bernand Lyon, France
Isabelle Debled-Rennesson, IUFM de Lorraine, France
Guillaume Damiand, Universite de Poitiers, France
Christophe Fiorio, Ecole Polytechnique Universitaire de Montpellier, France
Atushi Imiya, Chyba University, Japan
Reinhard Klette, Auckland University, New Zealand

ST10: Soft Computing in Image Processing and Computer Vision

Organizers:
Gerald Schaefer, Nottingham Trent University, UK
Muhammad Sarfraz, King Fahd University of Petroleum and Minerals, Saudi Arabia
Lars Nolle, Nottingham Trent University, UK

ST11: Energy Minimization Approaches in Image Processing and Computer Vision

Organizers:
Jose M. Bioucas-Dias, Instituto Superior Tecnico Torre Norte, Portugal
Antonin Chambolle, CMAP Ecole Polytechnique, France
Jerome Darbon, EPITA Research and Development Laboratory, France

ISVC’15 Special Tracks

Papers submitted to a special track should be submitted thourgh the one-line ISVC05 conference management system. For each paper, please send a brief message to admin@isvc05.net including the title of the paper, the authors, and the special track where the paper is being submitted.

Computer Vision Methods for Ambient Intelligence

Organizers:
Paolo Remagnino, DIRC, Kingston University, UK
Gian Luca Foresti, DIMI, Universita` di Udine, Italy
Ndedi D. Monekosso, DIRC, Kingston Univeristy, UK
Sergio Velastin, DIRC, Kingston University, UK

* Selected papers from this special track will be considered for publication in a special issue
of the Image and Vision Computing which is scheduled to appear in the first quarter of 2007.

Intelligent Vehicles and Autonomous Navigation

Organizers:
Fatih Porikli, MERL, USA
Ara Nefian, Intel, USA
Swarup Medasani, HRL Laboratories, USA
Riad Hammoud, Delphi Electronics and Safety, USA

Pattern Analysis and Recognition Applications in Biometrics

Organizers:
Nello Cristianini, University of California, Davis, USA
Salil Prabhakar, DigitalPersona, USA
Kostas Veropoulos, University of Nevada, Reno USA

* Invited Speaker: Tieniu Tan, CAS Institute of Automation, China
* IEEE Trans on PAMI has scheduled a special issue on Biometrics: Progress and Directions (to appear in May 2007). Please, note that the PAMI special issue and this ISVC05 special track are independent from each other.

Visual Surveillance in Challenging Environments

Organizers:
Wei-Yun Yau, Institute for Infocomm Research, Singapore
How-Lung Eng, Institute for Infocomm Research, Singapore
Anastasios N. Venetsanopoulos, University of Toronto, Canada
Monique Thonnat, INRIA Sophia Antipolis, France
Tieniu Tan, CAS Institute of Automation, China

Virtual Reality and Medicine

Organizers:
Fabio Ganovelli, VCG ISTI-CNR, Italy
Cesar Mendoza, University Politécnica de Madrid, Spain
Min-Hyung Choi, Computer Science and Engineering University of Colorado at Denver
John Dingliana, Image Synthesis Group. Trinity College, Dublin

Mediated Reality

Organizers:
Reinhold Behringer, Rockwell Scientific, USA
Steve Feiner, Columbia University, USA
Steve Mann, University of Toronto, USA
Jose Molineros, Rockwell Scietific, USA
Mohammed Yeasin, University of Memphis, USA

Visualization Techniques Applied to Geophysical Sciences Research

Organizers:
Darko Koracin, Desert Research Institute, USA
Robert Rabin, NOAA/National Severe Storms Laboratory, USA
Joseph Scire, Earth Tech, USA
William Sherman, Desert Research Institute, USA