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

We propose a novel automatic seeded region growing method based on gradient vector flow (GVF) for color image segmentation. YCbCr 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. (C) 2007 Society of Photo-Optical Instrumentation Engineers.