Industrial Smoke Image Segmentation Based on a New Algorithm of Cross-Entropy Model

Huang, Qian-Jing; Zou, Le; Wu, Zhize; Li, Huanyi; Chen, Yanping

doi:10.1007/978-3-030-60799-9_27

Cited by 1 publication

(1 citation statement)

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“…Based on Taylor jets and a corresponding discretization, Kiefer et al 34 presented a new efficient algorithm framework for second order Mumford‐Shah regularization. Huang et al 35 developed the ICTM algorithm of the cross entropy active contour image segmentation model. Çataloluk et al 36 developed a novel heuristic algorithm for the CV model.…”

Section: Introductionmentioning

confidence: 99%

A sweeping optimization algorithm for the global cosine fitting energy image segmentation model

Zou

Chen

et al. 2021

Concurrency and Computation

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Image segmentation plays a pivotal role in image processing. Level set model is a traditional variation image segmentation method. In order to achieve level set evolution equation, the level set energy functionals are minimized with the gradient descent methods and then the partial differential equation (PDE) was solved by finite difference scheme. Slow speed is one of its disadvantages. We propose a sweep optimization algorithm based on global cosine fitting (GCF) energy. Instead of calculating the PDE and the curvature, the proposed sweeping algorithm directly calculates the energy change when a pixel moves from one side of evolving contour to the other. It checks whether the GCF energy is decreased or not. The proposed algorithm has many advantages. For example, independent of initial level set contour positions and parameters, need not consider the Courant Friedrichs Lew condition and the regularization energy term. The proposed algorithm can be easily extended to high dimension image segmentation. The experiments on synthetic images, noise images and real images demonstrate the effectiveness of the proposed sweeping optimization algorithm.

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Section: Introductionmentioning

confidence: 99%