High‐Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise | Zendy

Haiping Jiang | Zendy; Salah Bourennane | Zendy; Caroline Fossati | Zendy

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High‐Resolution Algorithm for Image Segmentation in the Presence of Correlated Noise

Author(s) -

Haiping Jiang,

Salah Bourennane,

Caroline Fossati

Publication year - 2010

Publication title -

journal of electrical and computer engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.318

H-Index - 25

eISSN - 2090-0155

pISSN - 2090-0147

DOI - 10.1155/2010/630768

Subject(s) - subspace topology , image processing , higher order statistics , computer science , gaussian noise , artificial intelligence , algorithm , gaussian , pattern recognition (psychology) , noise (video) , segmentation , computer vision , image (mathematics) , signal processing , telecommunications , radar , physics , quantum mechanics

Multiple line characterization is a most common issue in image processing. A specific formalism turns the contour detection issue of image processing into a source localization issue of array processing. However, the existing methods do not address correlated noise. As a result, the detection performance is degraded. In this paper, we propose to improve the subspace-based high-resolution methods by computing the fourth-order slice cumulant matrix of the received signals instead of second-order statistics, and we estimate contour parameters out of images impaired with correlated Gaussian noise. Simulation results are presented and show that the proposed methods improve line characterization performance compared to second-order statistics

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