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Proceedings Paper

Level set segmentation using image second order statistics
Author(s): Bo Ma; Yuwei Wu; Pei Li
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Paper Abstract

This paper proposes a novel level set based image segmentation method by use of image second statistics and logarithmic Euclidean metric. Different from previous tensor based image segmentation approaches, the proposed method adopts covariance feature as region-level descriptor rather than pixel-level one. On the basis of feature image, we utilize second order statistics of image feature, i.e., covariance matrix, to model image region representation, which is of low dimension, invariant to uniform illumination change, insensitive to noise, and more importantly provide a natural mechanism of incorporating different types of image features by modeling their correlations. We model image segmentation problem as one finding the optimal segmentation that maximizes the covariance distance between foreground region and background region. Typically, covariance matrices do not lie on Euclidean space. Our solution to this is to exploit logarithmic Euclidean distance as a metric to compute the similarity between two matrices. The experimental results show that covariance matrix as region descriptor do form an effective representation for image segmentation problems, and the proposed image energy can be used to segment images and extract object boundaries reliably and accurately.

Paper Details

Date Published: 8 December 2011
PDF: 7 pages
Proc. SPIE 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis, 80030Z (8 December 2011); doi: 10.1117/12.902005
Show Author Affiliations
Bo Ma, Beijing Institute of Technology (China)
Yuwei Wu, Beijing Institute of Technology (China)
Pei Li, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 8003:
MIPPR 2011: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Nong Sang, Editor(s)

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