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Tensor voting for salient feature in...

University of Southern California.

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Tensor voting for salient feature inference in computer vision.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Tensor voting for salient feature inference in computer vision./
作者:	Lee, Mi-Suen.
面頁冊數:	127 p.
附註:	Adviser: Gerard Medioni.
Contained By:	Dissertation Abstracts International60-02B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9919071
ISBN:	9780599181250

Tensor voting for salient feature inference in computer vision.
Lee, Mi-Suen.

Tensor voting for salient feature inference in computer vision. - 127 p.

Adviser: Gerard Medioni.

Thesis (Ph.D.)--University of Southern California, 1998.

In computer vision, we often face the problem of extracting salient and structured information from a noisy data set. As computer vision systems move from controlled laboratory settings to real applications, the need for robust techniques to perform the inference of such salient structures becomes more apparent. For a salient structure estimator to be useful in computer vision, it must handle the presence of multiple structures, and the interaction between them, in noisy, irregularly clustered data sets. The derivation of such an estimator relies on the proper implementation of constraints, particularly the continuity constraint.

ISBN: 9780599181250Subjects--Topical Terms:

626642
Computer Science.

Tensor voting for salient feature inference in computer vision.
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502 $a Thesis (Ph.D.)--University of Southern California, 1998.
520 $a In computer vision, we often face the problem of extracting salient and structured information from a noisy data set. As computer vision systems move from controlled laboratory settings to real applications, the need for robust techniques to perform the inference of such salient structures becomes more apparent. For a salient structure estimator to be useful in computer vision, it must handle the presence of multiple structures, and the interaction between them, in noisy, irregularly clustered data sets. The derivation of such an estimator relies on the proper implementation of constraints, particularly the continuity constraint.
520 $a We present a unified computational framework that makes use of the continuity constraint to generate descriptions in terms of surface, regions, curves, and labelled junctions, from sparse, noisy, binary data in 2-D or 3-D. Each input site can be a point, a point with an associated tangent direction, a point with an associated tangent vector, or any combination of the above. The methodology is grounded on two elements: tensor calculus for representation, and non-linear voting for communication: each input site communicates its information (a tensor) to its neighborhood through a predefined (tensor) field, and therefore casts a (tensor) vote. Each site collects all the votes cast at its location and encodes them into a new tensor. A local, parallel routine such as a modified marching squares process then simultaneously detects junctions, curves and region boundaries. The proposed approach is very different from traditional variational approaches, as it is non-iterative. Furthermore, the only free parameter is the size of the neighborhood, related to the scale.
520 $a We have developed several algorithms based on the proposed methodology to address a number of early vision problems, including perceptual grouping in 2-D and 3-D, shape from stereo, shape from shading, and motion grouping and segmentation, and the results are very encouraging.
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