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.
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