Exploratory Vision

1. Front Matter

   Pages i-xviii

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2. Active Human Vision

   1. Front Matter

      Pages 1-1

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   2. Moveo Ergo Video: Natural Retinal Image Motion and its Effect on Vision

      • Robert M. Steinman

      Pages 3-49

   3. Cogito Ergo Moveo: Cognitive Control of Eye Movement

      • Eileen Kowler

      Pages 51-77

3. Solving Visual Problems

   1. Front Matter

      Pages 79-79

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   2. Robust Computational Vision

      • Brian G. Schunck

      Pages 81-99

   3. Eye Movements and the Complexity of Visual Processing

      • Misha Pavel, Helen A. Cunningham

      Pages 101-120

   4. Exploratory Vision: Some Implications for Retinal Sampling and Reconstruction

      • Laurence T. Maloney

      Pages 121-156

   5. Calibration of a Visual System with Receptor Drop-out

      • Albert J. Ahumada Jr., Kathleen Turano

      Pages 157-168

   6. Peripheral Visual Field, Fixation and Direction of Heading

      • Inigo Thomas, Eero Simoncelli, Ruzena Bajcsy

      Pages 169-189

   7. Local Qualitative Shape from Active Shading

      • Michael S. Langer, Steven W. Zucker

      Pages 191-202

4. Robots that Explore

   1. Front Matter

      Pages 203-203

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   2. The Synthesis of Vision and Action

      • Cornelia Fermüller, Yiannis Aloimonos

      Pages 205-240

   3. A Framework for Information Assimilation

      • Arun Katkere, Ramesh Jain

      Pages 241-256

   4. Task-Oriented Vision

      • Katsushi Ikeuchi, Martial Hebert

      Pages 257-277

5. Human and Machine: Telepresence and Virtual Reality

   1. Front Matter

      Pages 279-279

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   2. Active Vision and Virtual Reality

      • Brian C. Madden, Hany Farid

      Pages 281-318

   3. A Novel Environment for Situated Vision and Behavior

      • Trevor Darrell, Pattie Maes, Bruce Blumberg, Alex P. Pentland

      Pages 319-331

6. Back Matter

   Pages 333-344

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Advances in sensing, signal processing, and computer technology during the past half century have stimulated numerous attempts to design general-purpose ma­ chines that see. These attempts have met with at best modest success and more typically outright failure. The difficulties encountered in building working com­ puter vision systems based on state-of-the-art techniques came as a surprise. Perhaps the most frustrating aspect of the problem is that machine vision sys­ tems cannot deal with numerous visual tasks that humans perform rapidly and effortlessly. In reaction to this perceived discrepancy in performance, various researchers (notably Marr, 1982) suggested that the design of machine-vision systems should be based on principles drawn from the study of biological systems. This "neuro­ morphic" or "anthropomorphic" approach has proven fruitful: the use of pyramid (multiresolution) image representation methods in image compression is one ex­ ample of a successful application based on principles primarily derived from the study of biological vision systems. It is still the case, however, that the perfor­ of computer vision systems falls far short of that of the natural systems mance they are intended to mimic, suggesting that it is time to look even more closely at the remaining differences between artificial and biological vision systems.

• Computer Vision
• Performance
• Picking
• Shading
• Simulation
• Stereo
• machine vision

• Department of Psychology and Center for Neural Science, New York University, New York, USA

  Michael S. Landy, Laurence T. Maloney

• Department of Electrical Engineering and Applied Physics, Oregon Graduate Institute, Portland, USA

  Misha Pavel

• Department of Computer Science and Engineering, Oregon Graduate Institute, Portland, USA

  Misha Pavel

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