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A segmentation algorithm for noisy images: Design and evaluation
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DOI：10.1016/S0167-8655(98)00102-0.png)
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This paper presents a segmentation algorithm for gray-level images and addresses issues related to its performance on noisy images. It formulates an image segmentation problem as a partition of an image into (arbitrarily-shaped) connected regions to minimize the sum of gray-level variations over all partitioned regions, under the constraints that (1) each partitioned region has at least a specified number of pixels, and (2) two adjacent regions have significantly different average gray-levels. To overcome the computational difficulty of directly solving this problem, a minimum spanning tree representation of a gray-level image has been developed. With this tree representation, an image segmentation problem is effectively reduced to a tree partitionings problem, which can be solved efficiently. To evaluate the algorithm, we have studied how noise affects the performance of the algorithm. Three types of noise, transmission noise, Gaussian additive noise, and multiple sources of lightings, are considered, and their effects on the algorithm are studied. (C) 1998 Elsevier Science B.V. All rights reserved.
Keyword:
image segmentation
noisy image processing
minimum spanning tree application
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3.3
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1.6W
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