Adaptive image segmentation algorithm under the constraint of edge posterior probability. Issue 8 (25th September 2017)

• Record Type: Journal Article
• Authors: Gong, Meiling; Lan, Jinhui; Yang, Changlin; Wu, Hongtao; Zhi, Tao
• Is Part Of: IET computer vision. Volume 11:Issue 8(2017)
• Page Start: 702
• Page End: 709
• Publication Date: 2017-09-25
• Subjects: image segmentation -- probability; adaptive image segmentation algorithm -- image processing -- contemporary segmentation algorithm -- antinoise performance -- watershed algorithm -- edge threshold segmentation -- edge posterior probability model; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0213
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti‐noise performance, over‐segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over‐segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti‐noise performance, highly precise segmentation result, and are useful in effectively segmenting low‐contrast images.