Attention and Performance in Computational Vision

Attention in Object and Scene Recognition.- Distributed Control of Attention.- Inherent Limitations of Visual Search and the Role of Inner-Scene Similarity.- Attentive Object Detection Using an Information Theoretic Saliency Measure.- Architectures for Sequential Attention.- A Model of Object-Based Attention That Guides Active Visual Search to Behaviourally Relevant Locations.- Learning of Position-Invariant Object Representation Across Attention Shifts.- Combining Conspicuity Maps for hROIs Prediction.- Human Gaze Control in Real World Search.- Biologically Plausible Models for Attention.- The Computational Neuroscience of Visual Cognition: Attention, Memory and Reward.- Modeling Attention: From Computational Neuroscience to Computer Vision.- Towards a Biologically Plausible Active Visual Search Model.- Modeling Grouping Through Interactions Between Top-Down and Bottom-Up Processes: The Grouping and Selective Attention for Identification Model (G-SAIM).- TarzaNN : A General Purpose Neural Network Simulator for Visual Attention Modeling.- Applications of Attentive Vision.- Visual Attention for Object Recognition in Spatial 3D Data.- A Visual Attention-Based Approach for Automatic Landmark Selection and Recognition.- Biologically Motivated Visual Selective Attention for Face Localization.- Accumulative Computation Method for Motion Features Extraction in Active Selective Visual Attention.- Fast Detection of Frequent Change in Focus of Human Attention.