Computational vision deals with the underlying mathematical and computational models for how visual information is processed. Whether the processing is biological or machine, there are fundamental questions related to how the information is processed. How should information be represented? How should information be transduced in order to highlight features of interest while suppressing noise and other artefacts of the image capture process? Computational Vision in Neural and Machine Systems address these and other questions in 13 chapters, divided into three sections, which overlap between biological and computational systems: dynamical systems; attention, motion, and eye-movements; and stereovision. The editors have brought together the best and brightest minds in the field of computational vision, combining research from both biology and computing and enhancing the developing synergy between computational and biological visual modelling communities. Aimed at researchers and graduate students in computational or biological vision, neuroscience, and psychology.
Laurence Harris is professor of psychology at York University, Ontario, Canada. An expert in the brain's processing of information from multiple senses, especially regarding self-motion and orientation in normal and unusual environments, particularly space; his research involves work on the International Space Station and in environments simulated using virtual reality. Michael Jenkin is professor of computer science and engineering at York University, Ontario, Canada. His research interests include perception and guidance for autonomous robotic systems, and the development and analysis of virtual reality systems. In 2005 he was the recipient of the CIPPRS/ACTIRF award for research excellence and service to the Canadian Computer and Robot Vision research community.
Preface; 1. Computational vision in neural and machine systems Michael Jenkin and Laurence Harris; Part I. Dynamical Systems: 2. Exploring contrast-controlled adaptation processes in human vision (with help from Buffy the Vampire Slayer) Norma Graham and S. SabinaWolfson; 3. Image comparison and motion detection by a contrario methods Frederic Cao, Thomas Veit and Patrick Bouthemy; 4. Computer vision in the Mars Exploration Rover (MER) mission Larry Matthies, Mark Maimone, Yang Cheng, Andrew Johnson and Reg Willson; 5. Calibration and shape recovery from videos of dynamic scenes Marc Pollefeys, Sudipta Sinha and Jingyu Yan; 6. Specular planar target surface recovery via coded target stereopsis Arlene Ripsman, Piotr Jasiobedzki and Michael Jenkin; 7. Neural construction of objects from parts Charles E. Connor; Part II. Attention, Motion and Eye Movements: 8. Attention and action James J. Clark, Ziad M. Hafed and Li Jie; 9. Cueing visual search in clutter Preeti Verghese; 10. Transsaccadic memory of visual features Steven L. Prime, Matthias Niemeier and J. Douglas Crawford; 11. Modeling what attracts human gaze over dynamic natural scenes Laurent Itti and Pierre Baldi; Part III. Stereo: 12. Global stereo in polynomial time Carlo Tomasi; 13. Computational analysis of binocular half occlusions Mikhail Sizintsev and Richard P.Wildes; 14. Speed versus quality - measuring and optimizing Stereo for Telepresence Jane Mulligan; 15. Binocular combination: measurements and a model Jian Ding and George Sperling; Index.