Mordohai, Philippos.
Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning / [electronic resource] : by Philippos Mordohai, Gérard Medioni. - 1st ed. 2006. - IX, 126 p. online resource. - Synthesis Lectures on Image, Video, and Multimedia Processing, 1559-8144 . - Synthesis Lectures on Image, Video, and Multimedia Processing, .
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.
9783031022425
10.1007/978-3-031-02242-5 doi
Engineering.
Electrical engineering.
Signal processing.
Technology and Engineering.
Electrical and Electronic Engineering.
Signal, Speech and Image Processing.
T1-995
620
Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning / [electronic resource] : by Philippos Mordohai, Gérard Medioni. - 1st ed. 2006. - IX, 126 p. online resource. - Synthesis Lectures on Image, Video, and Multimedia Processing, 1559-8144 . - Synthesis Lectures on Image, Video, and Multimedia Processing, .
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.
9783031022425
10.1007/978-3-031-02242-5 doi
Engineering.
Electrical engineering.
Signal processing.
Technology and Engineering.
Electrical and Electronic Engineering.
Signal, Speech and Image Processing.
T1-995
620