| Document Type
|
:
|
BL
|
| Record Number
|
:
|
1020642
|
| Doc. No
|
:
|
b775012
|
| Main Entry
|
:
|
Mordohai, Philippos.
|
| Title & Author
|
:
|
Tensor voting : : a perceptual organization approach to computer vision and machine learning /\ Philippos Mordohai and Gérard Medioni.
|
| Edition Statement
|
:
|
1st ed.
|
| Publication Statement
|
:
|
[San Rafael, Calif.] :: Morgan & Claypool Publishers,, ©2006.
|
| Series Statement
|
:
|
Synthesis lectures on image, video, and multimedia processing,; #8
|
| Page. NO
|
:
|
1 online resource (ix, 126 pages)
|
| ISBN
|
:
|
1598291009
|
|
|
:
|
: 1598291017
|
|
|
:
|
: 9781598291001
|
|
|
:
|
: 9781598291018
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references (pages 115-123).
|
| Contents
|
:
|
Introduction -- Tensor voting -- Stereo vision from a perceptual organization perspective -- Tensor voting in ND -- Dimensionality estimation manifold learning and function approximation -- Boundary inference -- Figure completion -- Conclusions -- References.
|
| Abstract
|
:
|
This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.
|
| Subject
|
:
|
Computer vision.
|
| Subject
|
:
|
Machine learning.
|
| Subject
|
:
|
Three-dimensional imaging.
|
| Subject
|
:
|
Computer vision.
|
| Subject
|
:
|
COMPUTERS-- Computer Vision Pattern Recognition.
|
| Subject
|
:
|
Machine learning.
|
| Subject
|
:
|
Three-dimensional imaging.
|
| Dewey Classification
|
:
|
006.3/7
|
| LC Classification
|
:
|
TA1634.M67 2006c Online only
|
| Added Entry
|
:
|
Medioni, Gérard.
|