The MIT Press
2 total works
3D Model Recognition from Stereoscopic Cues
by John E.W. Mayhew and John P. Frisby
Published 4 January 1991
3D Model Recognition from Stereoscopic Cues provides a rich, integrated account of work done within a large-scale, multisite, Alvey-funded collaborative project in computer vision. It presents a variety of methods for deriving surface descriptions from stereoscopic data and for matching those descriptions to three-dimensional models for the purposes of object recognition, vision verification, autonomous vehicle guidance, and robot workstation guidance. State of the art vision systems are. described in sufficient detail to allow researchers to replicate the results.Partial ContentsThe PMF Stereo Algorithm Project * A Dynamic Programming Algorithm for Binocular Stereo Vision * Stereo Matching Using Intra- and Inter-Row Dynamic Programming * A Computational Theory of Stereo Vision * A Piplid Architecture for the Canny Edge Detector * Estimation of Stereo and Motion Parameters Using a Variational Principle * The 2.5D Sketch Project * Segmentation and Description of Binocularly Viewed Contours * Inferring Surface Shape by Specular Stereo * Surface Descriptions from Stereo and Shading * The 3D Model-Based Vision Project * Matching Geometrical Descriptions in ThreeSpace * Advances in 3D Model Indentification from Stereo Data * Dupin's Cyclide and the Cyclide Patch * Geometric Reasoning in a Parallel Network * SMS: A Suggestive Modelling System for Object Recognition * WPFM: The Workspace Prediction and Fast Matching System * The Design of the IMAGINE II Scene Analysis Program * Overview * TINA: A 3D Vision System for Pick and Place
Seeing has puzzled scientists and philosophers for centuries and it continues to do so. This new edition of a classic text offers an accessible but rigorous introduction to the computational approach to understanding biological visual systems. The authors of Seeing, taking as their premise David Marr's statement that "to understand vision by studying only neurons is like trying to understand bird flight by studying only feathers," make use of Marr's three different levels of analysis in the study of vision: the computational level, the algorithmic level, and the hardware implementation level. Each chapter applies this approach to a different topic in vision by examining the problems the visual system encounters in interpreting retinal images and the constraints available to solve these problems; the algorithms that can realize the solution; and the implementation of these algorithms in neurons.
Seeing has been thoroughly updated for this edition and expanded to more than three times its original length. It is designed to lead the reader through the problems of vision, from the common (but mistaken) idea that seeing consists just of making pictures in the brain to the minutiae of how neurons collectively encode the visual features that underpin seeing. Although it assumes no prior knowledge of the field, some chapters present advanced material. This makes it the only textbook suitable for both undergraduate and graduate students that takes a consistently computational perspective, offering a firm conceptual basis for tackling the vast literature on vision. It covers a wide range of topics, including aftereffects, the retina, receptive fields, object recognition, brain maps, Bayesian perception, motion, color, and stereopsis. MatLab code is available on the book's website, which includes a simple demonstration of image convolution.
Seeing has been thoroughly updated for this edition and expanded to more than three times its original length. It is designed to lead the reader through the problems of vision, from the common (but mistaken) idea that seeing consists just of making pictures in the brain to the minutiae of how neurons collectively encode the visual features that underpin seeing. Although it assumes no prior knowledge of the field, some chapters present advanced material. This makes it the only textbook suitable for both undergraduate and graduate students that takes a consistently computational perspective, offering a firm conceptual basis for tackling the vast literature on vision. It covers a wide range of topics, including aftereffects, the retina, receptive fields, object recognition, brain maps, Bayesian perception, motion, color, and stereopsis. MatLab code is available on the book's website, which includes a simple demonstration of image convolution.