Computer Vision: From Surfaces to 3D Objects
Author:   Christopher W. Tyler
Publisher:   Taylor & Francis Ltd
ISBN:  

9780367383091


Pages:   292
Publication Date:   18 October 2019
Format:   Paperback
Availability:   In Print   Availability explained
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Computer Vision: From Surfaces to 3D Objects


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Full Product Details

Author:   Christopher W. Tyler
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.453kg
ISBN:  

9780367383091


ISBN 10:   0367383098
Pages:   292
Publication Date:   18 October 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Introduction. Scene Statistics and 3D Surface Perception. The Theory of Swirling Fields: Segmenting a Scene into Surfaces. Mechanisms for Propagating Surface Information in 3D Reconstruction. 3D Surface Representation Using Ricci Flow. Cue Interpretation and Propagation: Flat versus Nonflat Visual Surfaces. Symmetry, Shape, Surfaces, and Objects. Noncommutative Field Theory in the Primary Visual Cortex. Contour-, Surface-, and Object-Related Coding in the Visual Cortex. From Surfaces to Objects: A Neuroanalytic Approach. 3D and Spatiotemporal Interpolation in Object and Surface Formation. The Perceptual Representation of 3D Shape. References. Index.

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Author Information

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.

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