title: Automatic image segmentation by dynamic region growth and multiresolution merging.
abstract: Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.
authors.affiliation: Zoran Corporation, Burlington, MA 01803, USA. luisgarciau@gmail.com
authors.forename: Luis Garcia
authors.initials: LG
authors.lastname: Ugarriza
authors.forename: Eli
authors.initials: E
authors.lastname: Saber
authors.forename: Sreenath Rao
authors.initials: SR
authors.lastname: Vantaram
authors.forename: Vincent
authors.initials: V
authors.lastname: Amuso
authors.forename: Mark
authors.initials: M
authors.lastname: Shaw
authors.forename: Ranjit
authors.initials: R
authors.lastname: Bhaskar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity