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Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint

4th International Workshop on Attention in Cognitive Systems, WAPCV 2007 Hyderabad, India, January 8, 2007 Revised Selected Papers

  • Conference proceedings
  • © 2007

Overview

Editors:
  1. Lucas Paletta

    1. Joanneum Research, Forschungsgesellschaft mbH, Computational Perception Group,, Institute of Digital Image Processing, Graz, Austria

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  2. Erich Rome

    1. Autonomous Intelligent Systems (AIS), Autonomous Robots Department, Fraunhofer Institute, Sankt Augustin, Germany

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4840)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: WAPCV 2007.

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Table of contents (32 papers)

  1. Front Matter

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  2. Embodiment of Attention

    1. The Embodied Dynamics of Emotion, Appraisal and Attention

      • Robert Lowe, Carlos Herrera, Anthony Morse, Tom Ziemke
      Pages 1-20

    2. The Role of Attention in Creating a Cognitive System

      • John G. Taylor
      Pages 21-41

    3. The Influence of the Body and Action on Spatial Attention

      • Catherine L. Reed, John P. Garza, Ralph J. Roberts Jr.
      Pages 42-58

    4. Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms

      • Luís Morgado, Graça Gaspar
      Pages 59-74

    5. Embodied Active Vision in Language Learning and Grounding

      • Chen Yu
      Pages 75-90

    6. Language Label Learning for Visual Concepts Discovered from Video Sequences

      • Prithwijit Guha, Amitabha Mukerjee
      Pages 91-105

  3. Cognitive Control of Attention

    1. Learning to Attend — From Bottom-Up to Top-Down

      • Hector Jasso, Jochen Triesch
      Pages 106-122

    2. An Attentional System Combining Top-Down and Bottom-Up Influences

      • Babak Rasolzadeh, Alireza Tavakoli Targhi, Jan-Olof Eklundh
      Pages 123-140

    3. The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images

      • Dietmar Heinke, Andreas Backhaus, Yarou Sun, Glyn W. Humphreys
      Pages 141-154

    4. A Bayesian Approach to Attention Control and Concept Abstraction

      • Saied Haidarian Shahri, Majid Nili Ahmadabadi
      Pages 155-170

  4. Modeling of Saliency and Visual Search

    1. An Information Theoretic Model of Saliency and Visual Search

      • Neil D. B. Bruce, John K. Tsotsos
      Pages 171-183

    2. An Experimental Comparison of Three Guiding Principles for the Detection of Salient Image Locations: Stability, Complexity, and Discrimination

      • Dashan Gao, Nuno Vasconcelos
      Pages 184-197

    3. A Proto-object Based Visual Attention Model

      • Francesco Orabona, Giorgio Metta, Giulio Sandini
      Pages 198-215

    4. Context Driven Focus of Attention for Object Detection

      • Roland Perko, Aleš Leonardis
      Pages 216-233

    5. Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention

      • Muhammad Zaheer Aziz, Bärbel Mertsching
      Pages 234-250

    6. I See What You See: Eye Movements in Real-World Scenes Are Affected by Perceived Direction of Gaze

      • Monica S. Castelhano, Mareike Wieth, John M. Henderson
      Pages 251-262

  5. Sequential Attention

    1. Selective Attention in the Learning of Viewpoint and Position Invariance

      • Muhua Li, James J. Clark
      Pages 263-276

    2. Generating Sequence of Eye Fixations Using Decision-Theoretic Attention Model

      • Erdan Gu, Jingbin Wang, Norman I. Badler
      Pages 277-292

    3. Reinforcement Learning for Decision Making in Sequential Visual Attention

      • Lucas Paletta, Gerald Fritz
      Pages 293-306
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  1. Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint

Keywords

About this book

Attentionhasbeenrepresentingacorescienti?ctopicinthedesignofAI-enabled systems within the last decades. Today, in the ongoing debate, design, and c- putationalmodelingofarti?cialcognitivesystems,attentionhasgainedacentral position as a focus of research. For instance, attentional methods are considered in investigating the interfacing of sensory and cognitive information processing, for the organization of behaviors, and for the understanding of individual and social cognition in re?ection of infant development. Whilevisualcognitionplaysacentralroleinhumanperception,?ndingsfrom neuroscience and experimental psychology have provided strong evidence about theperception-actionnatureofcognition.Theembodiednatureofsensory-motor intelligence requires a continuous and focused interplay between the control of motor activities and the interpretation of feedback from perceptual modalities. Decision making about the selection of information from the incoming sensory stream – in tune with contextual processing on a current task and an agent’s global objectives – becomes a further challenging issue in attentional control. Attention must operate at interfaces between bottom-up driven world int- pretation and top-down driven information selection, thus acting at the core of arti?cial cognitive systems. These insights have already induced changes in AI-related disciplines, such as the design of behavior-based robot control and the computational modeling of animats. Today, the development of enabling technologiessuch as autonomous robotic systems,miniaturizedmobile–evenwearable–sensors,andambientintelligence systems involves the real-time analysis of enormous quantities of data. These data have to be processed in an intelligent way to provide “on time delivery”of the required relevant information. Knowledge has to be applied about what needs to be attended to, and when, and what to do in a meaningful sequence, in correspondence with visual feedback.

Editors and Affiliations

  • Joanneum Research, Forschungsgesellschaft mbH, Computational Perception Group,, Institute of Digital Image Processing, Graz, Austria

    Lucas Paletta

  • Autonomous Intelligent Systems (AIS), Autonomous Robots Department, Fraunhofer Institute, Sankt Augustin, Germany

    Erich Rome

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