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Opening |
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| 8:50 | ||
Invited Talk 1 |
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| 9:00 | Distributed Saliency Computations Solve the Feature Binding Problem Computational vision has a long history of proposing methods for decomposing a visual signal into its components. For example, many good strategies have appeared for decomposing visual motion signals. What has been far more elusive is how to recombine those components into a whole. This problem has even merited its own name - the binding problem. To date no realizable process has appeared to solve the binding problem, even in part, although several proposals are being studied. This presentation will focus on a new strategy utilizing a novel distributed saliency computation mechanism that solves at least one aspect of the binding problem, namely the binding of features from separate representations into a whole. Several examples will be drawn from a new, biologically realistic, motion analysis system, one that attends to complex motion patterns. An example of how this approach even yields Treisman-style illusory conjunctions is included. The entire process is implemented and operates on real image sequences. The implications for the neurobiology of visual attention will round out the presentation. |
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Session 1: Attention in Object and Scene Recognition |
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| 9:45 | Object Based Visual Attention: Searching for Objects Defined by Size Ola Ramstrom and Henrik I. Christensen Royal Institute of Technology, Sweden |
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| Inherent Limitations of Visual Search and the Role of Inner-Scene Similarity
Tamar Avraham and Michael Lindenbaum Technion Haifa, Israel |
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| 10:45 | coffee break | |
Session 2: Architectures for Sequential Attention |
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| 11:00 | Selective Attention for Identification Model (SAIM): Simulating Different
Types of Visual Neglect Dietmar Heinke and Glyn W. Humphreys University of Birmingham, UK |
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| A Model of Object-Based Attention That Guides Active Visual Search to
Behaviourally Relevant Locations Linda J. Lanyon and Susan L Denham University of Plymouth, UK |
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| Learning of Position and Attention-Shift Invariant Recognition across
Attention Shifts Muhua Li and James J. Clark McGill University, Canada |
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| 12:15 | lunch | |
Invited Talk 2 |
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| 13:45 | The Computational Neuroscience of Visual Cognition: Attention, Memory
and Reward Cognitive behaviour requires complex context-dependent processing of information that emerges from the links between attentional perceptual processes, working memory and reward-based evaluation of the performed actions. We describe a computational neuroscience theoretical framework which shows how an attentional state held in a short term memory in the prefrontal cortex can by top-down processing influence ventral and dorsal stream cortical areas using biased competition to account for many aspects of visual attention. We also show how within the prefrontal cortex an attentional bias can influence the mapping of sensory inputs to motor outputs, and thus play an important role in decision making. We also show how the absence of expected rewards can switch the attentional bias signal, and thus rapidly and flexibly alter cognitive performance. This theoretical framework incorporates spiking and synaptic dynamics which enable single neuron responses, fMRI activations, psychophysical results, the effects of pharmacological agents, and the effects of damage to parts of the system, to be explicitly simulated and predicted. This computational neuroscience framework provides an approach for integrating different levels of investigation of brain function, and for understanding the relations between them. The models also directly address how bottom-up and top-down processes interact in visual cognition, and show how some apparently serial processes reflect the operation of interacting parallel distributed systems. |
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Session 3: Biologically Plausible Models for Attention |
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| 14:30 | Modeling Attention: From Computational Neuroscience to Computer Vision
Fred H. Hamker Westfälische Wilhelms-Universität, Germany |
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| Towards a Biologically Plausible Active Visual Search Model Andrei Zaharescu, Albert L. Rothenstein and John K. Tsotsos York University, Canada |
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| 15:20 | coffee break | |
Session 4: Applications of Attentive Vision |
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| 15:50 | Visual Attention for Object Recognition in Spatial 3D Data Simone Frintrop, Andreas Nüchter, and Hartmut Surmann Fraunhofer AIS Institute, Germany |
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| AttentiRobot: A Visual Attention-based Landmark Selection Approach for
Mobile Robot Navigation Nabil Ouerhani and Heinz Hügli University of Neuchatel, Switzerland |
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| Detection of Frequent Change in Focus of Human Attention from Videos Nan Hu, Weimin Huang, Surendra Ranganath Institute for Infocomm Research, Singapore |
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Poster Session |
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| 17:05 | On the Usefulness of Attention for Object Recognition Dirk Walther, Ueli Rutishauser, Christof Koch, and Pietro Perona California Institute of Technology, CA |
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| Combining Conspicuity Maps for hROIs Prediction Claudio M. Privitera, Orazio Gallo, Giorgio Grimoldi, Toyomi Fujita, Lawrence W. Stark University of California, Berkeley, CA |
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| A General Purpose Neural Network Simulator for Visual Attention Modeling Albert L. Rothenstein, Andrei Zaharescu, and John K. Tsotsos York University, Canada |
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| Biologically Motivated Selective Attention for Face Localization Minho Lee and Sang-Woo Ban Kyungpook National University, South Korea |
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| Accumulative Computation Method for Motion Features Extraction in Dynamic
Selective Visual Attention Antonio Fernandez-Caballero, María T. López, Miguel A. Fernández, José Mira, Ana E. Delgado and José M. López-Valles Universidad de Castilla-La Mancha, Spain |
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| Attentive Object Detection Using an Information Theoretic Saliency Measure Gerald Fritz, Christin Seifert, Lucas Paletta, and Horst Bischof JOANNEUM RESEARCH, Austria |
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| 18:05 | finish | |