Automatic seeded region growing based on gradient vector flow for color image segmentation

Yuan He; Yupin Luo; Dongcheng Hu

doi:10.1117/1.2724876

1 April 2007 Automatic seeded region growing based on gradient vector flow for color image segmentation

Yuan He, Yupin Luo, Dongcheng Hu

Author Affiliations +

Optical Engineering, Vol. 46, Issue 4, 047003 (April 2007). https://doi.org/10.1117/1.2724876

Abstract

We propose a novel automatic seeded region growing method based on gradient vector flow (GVF) for color image segmentation. YC_bC_r color space is selected to avoid the high correlation of RGB color space. First, a GVF field is constructed from an edge map of the input image. Then a scaler force field is derived from it by minimizing an energy functional iteratively. From the scalar field, we can select a set of seeds and get an initial segmentation via a straightforward downstream process. Finally, a region adjacency graph–based region merging is applied to merge similar neighboring regions into true results. Experimental results demonstrate that this method is insensitive to noises and efficient to multiple objects segmentation in color images.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Yuan He, Yupin Luo, and Dongcheng Hu "Automatic seeded region growing based on gradient vector flow for color image segmentation," Optical Engineering 46(4), 047003 (1 April 2007). https://doi.org/10.1117/1.2724876

Published: 1 April 2007

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