Transition region-based single-object image segmentation

Existing transition region-based image thresholding is unsuitable for images with overlapping gray levels between object and background due to the essence of global thresholding. To alleviate this issue, we proposed an innovative transition region-based single-object image segmentation method. The proposed algorithm first extracted transition regions of an image by using local variance as the descriptor. Its second step, image thinning, was to skeletonize transition regions as single pixel edges. The third step, edge filtering, removed useless short edges and edge spikes. Subsequently, the step called edge linking connected interrupted edges to obtain closed object contours. The final step filled object regions confined by the object contours with black or white, and only the largest object region remained as the final image segmentation result. The proposed algorithm was compared with different types of image segmentation methods on a variety of real world images, and experimental results demonstrated its superiority.