Tensor Voting (Record no. 86043)
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fixed length control field | 03541nam a22005175i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-031-02242-5 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730165016.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 220601s2006 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783031022425 |
-- | 978-3-031-02242-5 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 620 |
100 1# - AUTHOR NAME | |
Author | Mordohai, Philippos. |
245 10 - TITLE STATEMENT | |
Title | Tensor Voting |
Sub Title | A Perceptual Organization Approach to Computer Vision and Machine Learning / |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2006. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | IX, 126 p. |
490 1# - SERIES STATEMENT | |
Series statement | Synthesis Lectures on Image, Video, and Multimedia Processing, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | 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. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
700 1# - AUTHOR 2 | |
Author 2 | Medioni, Gérard. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02242-5 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
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-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2006. |
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-- | online resource |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Electrical engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal processing. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Technology and Engineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Electrical and Electronic Engineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal, Speech and Image Processing. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 1559-8144 |
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