Adaptive Threshold Image Segmentation Based on Definition Evaluation

doi:10.12068/j.issn.1005-3026.2020.09.003

Abstract

Abstract: Threshold is a widely used method for image segmentation. With the variance of the information in the image， this paper proposed a novel adaptive threshold segmentation method based on image definition evaluation. This method uses the definition evaluation function as a measure of the gray similarity change in the image after thresholding. Repeated iteration and Pearson correlation were combined until the optimal segmentation threshold was found. Test comparisons were performed using multiple sets of image data， especially low-contrast images， such as slight defects on the steel surface. The results showed that compared with the traditional threshold segmentation method and its improved algorithm， in the processing of low-contrast images， the proposed method can adaptively and accurately find a reasonable threshold value， and has an excellent performance of image segmentation.

Key words: computer vision, image segmentation, adaptive threshold, definition evaluation, low-contrast image

CLC Number:

TP391.4

ZHANG Tian， TIAN Yong， WANG Zi， WANG Zhao-dong. Adaptive Threshold Image Segmentation Based on Definition Evaluation[J]. Journal of Northeastern University Natural Science, 2020, 41(9): 1231-1238.

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