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Segmenting Images Corrupted by Correlated Noise
May 1998 (vol. 20 no. 5)
pp. 481-492

Abstract—Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of nonoverlapping homogeneous regions. The main contribution of this paper is the development of a new segmentation procedure which is designed to segment images corrupted by correlated noise. This new segmentation procedure is based on Rissanen's minimum description length (MDL) principle and consists of two components: i) an MDL-based criterion in which the "best" segmentation is defined as its minimizer and ii) a merging algorithm which attempts to locate this minimizer. The performance of this procedure is illustrated via a simulation study, with promising results.

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Index Terms:
Correlated noise, image segmentation, merging algorithm, minimum description length.
Citation:
Thomas C.M. Lee, "Segmenting Images Corrupted by Correlated Noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 481-492, May 1998, doi:10.1109/34.682178
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