Attention and Performance in Computational Vision

Second International Workshop, WAPCV 2004, Prague, Czech Republic, May 15, 2004, Revised Selected Papers

Häftad, Engelska, 2005

Av Lucas Paletta, John K. Tsotsos, Erich Rome, Glyn Humphreys

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This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Attention and Performance in Computational Vision, WAPCV 2004, held in Prague, Czech Republic in May 2004.The 16 revised full papers presented together with an invited paper were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on attention in object and scene recognition, architectures for sequential attention, biologically plausible models for attention, and applications of attentive vision.

Produktinformation

Utgivningsdatum2005-01-21
Mått155 x 235 x 14 mm
Vikt376 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor236
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540244219

Tillhör följande kategorier

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.