Abstract
A locally adaptive thresholding algorithm, concerning the extraction of targets from a given field of background, is proposed. Conventional histogram-based or global-type methods are deficient in detecting small targets of possibly low contrast as well. The present research is notable for solving the mentioned problems by introducing (1) shape connectivity measure based on co-occurrence statistics for threshold evaluation; and (2) no-target identification procedure for modeling a local-processing paradigm. In this manner, thresholds are determined adaptively even in the presence of space-varying noise or clutter. Experiments show that the results are reliable and even outperform those that manual operations can achieve for global thresholding.
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