Segmentation by Local Binary Fitting Active Contour Model for Activated Carbon Fibers Material Microscopic Images

Abstract:

Many bubbles and pores are appeared on Activated Carbon Fibers (ACFs) material microscopic images. The morphology of ACFs surface image is complicated. Some widely used traditional methods are difficult to segment the object correctly. In this paper, an implicit active contour driven by local binary fitting energy is used to segment the objects for ACFs micro-images. This method is based on local image edge information to obtain optimal level set active contour model. Experimental results show that this active contour model is flexible for analyzing images with complex porous structure.

• Periodical: Advanced Materials Research (Volume 811)
• Pages: 370-374
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.811.370
• Online since: September 2013
• Authors: Jiang Kun Zhao, Yu Zhu, Jian Feng Yu
• Keywords: ACF Images, Active Contour Model, Image Segmentation, Level Set Function

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