 Radial basis functions and level set method for image segmentation using partial differential equationShuling Li and Xiaolin Li Applied Mathematics and Computation, 2016, vol. 286, issue C, 29-40 Abstract: Combining nonlinear evolution equations, which arise from image segmentation using partial differential equation-based level set method, using radial basis functions, a meshless numerical algorithm is presented for image segmentation in this paper. Both globally supported and compactly supported radial basis functions are used to interpolate the level set function of the evolution equation with a high level of accuracy and smoothness. The nonlinear evolution equation is finally cast into ordinary differential equations and Euler’s scheme is employed. Compared with traditional level set approaches, the presented algorithm is robust to initialization or even free of manual initialization, and avoids the complex and costly re-initialization of the level set function. The capability of the presented algorithm is demonstrated through some numerical experiments. Keywords: Image segmentation; Radial basis functions; Evolution equations; Level set function; Meshless; Partial differential equation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:286:y:2016:i:c:p:29-40 DOI: 10.1016/j.amc.2016.04.002 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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