Image segmentation via an iterative algorithm of the mean shift filtering for different values of the stopping threshold

Image segmentation is a fundamental process of many image, video, and computer vision applications. Image segmentation is a critical step towards visual pattern recognition and image understanding. In this work we carried out a study on standard images of a segmentation algorithm based on iterative algorithm of the mean shift filtering for different values of the stopping threshold. A comparison of the obtained results for different values of the stopping threshold was carried out, taking into consideration the number of iterations and the degree of homogenization of the segmented images. With this study it was possible to obtain an optimum value of the stopping threshold for images with a content of low frequencies and high frequencies.