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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Paletta, Lucas (Επιμελητής έκδοσης), Tsotsos, John K. (Επιμελητής έκδοσης), Rome, Erich (Επιμελητής έκδοσης), Humphreys, Glyn (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
• Σειρά: Lecture Notes in Computer Science, 3368
• Θέματα: Computer science.; Neurosciences.; Artificial intelligence.; Computer graphics.; Image processing.; Pattern recognition.; Control engineering.; Robotics.; Mechatronics.; Computer Science.; Image Processing and Computer Vision.; Artificial Intelligence (incl. Robotics).; Pattern Recognition.; Computer Graphics.; Control, Robotics, Mechatronics.

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

Πίνακας περιεχομένων:

• 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.