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Proceedings Paper

Coding theoretic approach to segmentation and robust CFAR-detection for ladar images
Author(s): Unoma Ndili; Richard G. Baraniuk; Hyeokho Choi; Robert D. Nowak; Mario A. T. Figueiredo
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Paper Abstract

In this paper, we present an unsupervised scheme aimed at segmentation of laser radar (LADAR) imagery for Automatic Target Detection. A coding theoretic approach implements Rissanen's concept of Minimum Description Length (MDL) for estimating piecewise homogeneous regions. MDL is used to penalize overly complex segmentations. The intensity data is modeled as a Gaussian random field whose mean and variance functions are piecewise constant across the image. This model is intended to capture variations in both mean value (intensity) and variance (texture). The segmentation algorithm is based on an adaptive rectangular recursive partitioning scheme. We implement a robust constant false alarm rate (CFAR) detector on the segmented intensity image for target detection and compare our results with the conventional cell averaging (CA) CFAR detector.

Paper Details

Date Published: 22 October 2001
PDF: 9 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445354
Show Author Affiliations
Unoma Ndili, Rice Univ. (United States)
Richard G. Baraniuk, Rice Univ. (United States)
Hyeokho Choi, Rice Univ. (United States)
Robert D. Nowak, Rice Univ. (United States)
Mario A. T. Figueiredo, Instituto Superior Tecnico (Portugal)


Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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