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Tensor Voting [electronic resource] : A Perceptual Organization Approach to Computer Vision and Machine Learning / by Philippos Mordohai, Gérard Medioni.

By: Mordohai, Philippos [author.].
Contributor(s): Medioni, Gérard [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Image, Video, and Multimedia Processing: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2006Edition: 1st ed. 2006.Description: IX, 126 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031022425.Subject(s): Engineering | Electrical engineering | Signal processing | Technology and Engineering | Electrical and Electronic Engineering | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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