Computer Vision: From Surfaces to 3D Objects

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Computer Vision: From Surfaces to 3D Objects

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內容描述

The typical computational approach to object understanding derives shape information from the 2D outline of the objects. For complex object structures, however, such a planar approach cannot determine object shape; the structural edges have to be encoded in terms of their full 3D spatial configuration. Computer Vision: From Surfaces to 3D Objects is the first book to take a full approach to the challenging issue of veridical 3D object representation. It introduces mathematical and conceptual advances that offer an unprecedented framework for analyzing the complex scene structure of the world.
 
 
 
 
 
 
 
An Unprecedented Framework for Complex Object Representation
Presenting the material from both computational and neural implementation perspectives, the book covers novel analytic techniques for all levels of the surface representation problem. The cutting-edge contributions in this work run the gamut from the basic issue of the ground plane for surface estimation through mid-level analyses of surface segmentation processes to complex Riemannian space methods for representing and evaluating surfaces.
 
 
 
 
 
 
 
 
 
State-of-the-Art 3D Surface and Object Representation
This well-illustrated book takes a fresh look at the issue of 3D object representation. It provides a comprehensive survey of current approaches to the computational reconstruction of surface structure in the visual scene.

作者介紹

Christopher W. Tyler is the director of the Brain Imaging Center at the Smith-Kettlewell Eye Research Institute. His current research encompasses brain imaging studies and mathematical modeling of the mechanisms of human stereoscopic depth, motion, and face perception as well as higher cognitive processing. He and his team have developed new methods to determine the dynamics of the neural population responses underlying brain imaging signals. By designing stimuli to probe specific neural sub-populations, this new methodology can be used to explore neural properties in the human brain and the changes in neural dynamics during the learning process.