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6 May 2019 Improving the convergence of local binary fitting energy for image segmentation
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Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110692E (2019) https://doi.org/10.1117/12.2524236
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China
Abstract
This paper explores a Riemannian steepest method for fast converging local binary fitting model. The proposed method takes advantage of intensity information in local regions, which solves the intensity inhomogeneous images with satisfactory results. Furthermore, the Riemannian steepest descent method can be employed to local binary fitting model from exponential family and achieves convergence fast. The main contribution of this paper is that presents a general closed-form expression for the manifold’s Riemannian metric tensor of local binary fitting model, which makes the computation of Riemannian gradient flow possible. In addition, to ensure the accuracy of the segmentation results, we regularize the level set function by Gaussian smooth operator. Experimental results for synthetic and real-life images show satisfactory performances of proposed method.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yangyang Song and Guohua Peng "Improving the convergence of local binary fitting energy for image segmentation", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692E (6 May 2019); https://doi.org/10.1117/12.2524236
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