Thesis Defence: Energy-Based Segmentation Methods for Images with Non-Gaussian Noise

Jiatao Zhong, supervised by Dr. Xiaoping Shi, will defend their thesis titled “Energy-Based Segmentation Methods for Images with Non-Gaussian Noise” in partial fulfillment of the requirements for the degree of Master of Science in Mathematics.

An abstract for Jiatao Zhong’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Registration is not required for in-person defences.

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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.