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Attention and Performance in Computational Vision Second International Workshop, WAPCV 2004, Prague, Czech Republic, May 15, 2004, Revised Selected Papers

Inrecentresearchoncomputervisionsystems,attentionhasbeenplayingacrucialrolein mediatingbottom-upandtop-downpathsofinformationprocessing. Inappliedresearch, the development of enabling technologies such as miniaturized mobile sensors, video surveillance systems, and ambient intelligence systems invol...

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Bibliographic Details
Other Authors: Paletta, Lucas (Editor), Tsotsos, John K. (Editor), Rome, Erich (Editor), Humphreys, Glyn (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005, 2005
Edition:1st ed. 2005
Series:Image Processing, Computer Vision, Pattern Recognition, and Graphics
Subjects:
Neuroscience
Computer Graphics
Control, Robotics, Automation
Neurosciences
Computer Vision
Artificial Intelligence
Control Engineering
Robotics
Automated Pattern Recognition
Automation
Pattern Recognition Systems
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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

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