Image segmentation is a fundamental task in many computer vision applications. We present a novel unsupervised color image segmentation algorithm named GSEG, which exploits the information obtained from detecting edges in color images. By using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify the image content. Elements that contain higher gradient density are included by a dynamic generation of clusters as the segmentation progresses. By quantizing the colors in the image and extracting texture information from the neighborhood entropy of each pixel, the proposed method obtains accurate models of texture that are highly effective to merge regions that belong to the same object. Experimental results for various image scenarios in comparison with state-of-the-art segmentation techniques demonstrate the performance advantages of the proposed method.
Library of Congress Subject Headings
Computer vision--Mathematical models; Image processing--Digital techniques
Electrical Engineering (MS)
Department, Program, or Center
Electrical Engineering (KGCOE)
Garcia Ugarriza, Luis Enrique, "Automatic image segmentation by dynamic region growth and multiresolution merging" (2007). Thesis. Rochester Institute of Technology. Accessed from
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