Segmenting focused objects using morphological filters and region merging

Changick Kim

Abstract: We propose 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 consists of three steps. In the first step, we transform the low DOF image into an appropriate feature space for the partition. 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, is simplified by removing small dark holes and bright patches using a <i>morphological filter by reconstruction</i>. Finally, the OOI is extracted by applying region merging and adaptive thresholding to the simplified image. Experimental results show that the proposed method yields more accurate segmentation results than the previous methods.