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Abstract

This paper proposes an energy-based segmentation method facilitated by the change point detection. We apply the Kullback-Leibler (KL) divergence to demonstrate the feasibility of our method for non-Gaussian noisy images. Notably, the algorithm automatically determines whether the model is solvable using a Gaussian approach and, if not, effortlessly switches to a non-Gaussian alternative. It can also automatically determine the optimal number of classifications. Furthermore, its iterative nature enables the detection and segmentation of small regions that other methods often fail to capture. Compared to the traditional maximum between-class variance technique and recent statistical approaches, this method provides improved thresholding accuracy for bimodal grayscale images. Moreover, in the context of multiple threshold identification, the proposed method outperforms Subtractive Clustering K-Means with Filtering, Sparse Graph Spectral Clustering, Gaussian mixture on Markov random field, and Adaptive Thresholding in segmenting multimodal grayscale images.