Seeded region growing (SRG) segmentation is utilized frequently in image processing, computer vision, and machine intelligence applications. The accuracy of the segmentation produced by the fundamental SRG algorithm relies on the proper seed selection. In this paper, seeds are allocated for each color component of the input image using a keypoint detector. Two methods for obtaining seeds are examined; the first method uses the keypoints as the seeds, while the second method uses the centers of the triangles constructed using the keypoints as the seeds for the SRG algorithm. After that, each color plane is subjected to the SRG algorithm, and the result is then concatenated. Subsequently, this segmentation is enhanced by employing a statistical region-merging algorithm. Several traditional keypoint detectors, such as SIFT, SURF, KAZE, and Harris, are compared and examined using the well-known Berkeley segmentation dataset (BSD) images. Finally, the provided technique is compared with two other approaches for image segmentation: K-means and mean shift.

Region growing, seeds, image segmentation, keypoints detector, triangulations centers

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