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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| Other Authors / Creators: | Paletta, Lucas. editor. Tsotsos, John K. editor. Rome, Erich. editor. Humphreys, Glyn. editor. |
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| Other Corporate Authors / Creators: | SpringerLink (Online service) |
| Format: | Electronic eBook |
| Language: | English |
| Edition: | 1st ed. 2005. |
| Imprint: | Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005. |
| Series: | Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
3368 |
| Subjects: | |
| Online Access: | Available in Springer Computer Science eBooks 2005 English/International. |
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.