From Living Eyes to Seeing Machines
Mandyam V. Srinivasan, Svethan Venkatesh
OUP Oxford, May 1, 1997 - Technology & Engineering - 288 pages
Many creatures with small brains and simple nervous systems - such as insects - are astonishingly good at coping with the world around them. A fly, for example, can deftly evade a swat, manoeuvre perfectly well in a cluttered world, and execute a flawless landing on the rim of a teacup. Do such creatures use clever short-cuts to vision and navigation, and if so, can these tricks be exploited to create new kinds of robots? These questions are explored in this book, which contains articles by experimental biologists as well as computer scientists, in this newly emerging multidisciplinary field. This is a fresh approach to an area of research that has traditionally been dominated by engineering methods, and the book is written in a style in which technical jargon is kept to a minimum.
1 page matching efferent in this book
Results 1-1 of 1
What people are saying - Write a review
We haven't found any reviews in the usual places.
List of contributors
A survey of active vision in invertebrates
Active acquisition of depth information by honeybees
10 other sections not shown
active algorithm Aloimonos analysis angle angular animal artificial axis bars bees Bees trained behaviour biological camera centre circles Collett collision colour Comparative Physiology compound eye computational Computer Vision cones corridor cues detect detectors direction discrimination distance edge egomotion parameters environment estimates evolved excitation excitatory Experimental filters flight flow field flying frontal Frost honeybees horizontal Horridge image motion image velocity inhibition inhibitory input insect Journal of Comparative lateral inhibition layer Lehrer LGMD locations locust machine vision mantis shrimp measure mechanisms mobile robot motion field movement moving Nalbach navigation neural network neurones optic flow orientation output panels pathway patterns photoreceptors plane polarization position processing radial range real robot receptive field relative response retina rotation scanning sensors shown in Fig shows simulation spatial speed Srinivasan stimulus strategy symmetry target task tested tion trained translation unit vector visual field visual motion visual system Wehner