Abstract: Automatic image segmentation is one of the most Challenging problems in computer vision. This study presents a novel algorithm to partition an image with low Depth-of-Field (DOF) into focused Object-of-Interest (OOI) and defocused background. The proposed algorithm unfolds into three steps. In the first step, we transform the low-DOF image into an appropriate feature space, in which the spatial distribution of the high-frequency components is represented. This is conducted by computing Higher Order Statistics (HOS) for all pixels in the low-DOF image. Next, the obtained feature space, which is called HOS map in this paper, is simplified by removing small dark holes and bright patches using a morphological filter by reconstruction. Finally, the OOI is extracted by applying region merging to the simplified image and by thresholding. Unlike the previous methods that rely on sharp details of OOI only, the proposed algorithm complements the limitation of them by using morphological filters, which also allows perfect preservation of the contour information.
N. Santhi and K. Ramar , 2007. Image Segmentation Using Morphological Filters and Region Merging . Asian Journal of Information Technology, 6: 274-279.