Automatic image segmentation by integrating color-edge extraction and seeded region growing

We propose a new automatic image segmentation method. Color edges in an ima ge are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained co lor edges have provided the major geometric structures in an image, the cen troids between these adjacent edge regions are taken as the initial seeds f or seeded region growing (SRG). These seeds are then replaced by the centro ids of the generated homogeneous image regions by incorporating the require d additional pixels step by step. Moreover, the results of color-edge extra ction and SRG are integrated to provide homogeneous image regions with accu rate and closed boundaries. We also discuss the application of our image se gmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.