Constrained Region-Growing and Edge Enhancement Towards Automated Semantic Video Object Segmentation

Abstract. Most existing object segmentation algorithms suffer from a so-called under-segmentation problem, where parts of the segmented object are missing and holes often occur inside the object region. This problem becomes even more serious when the object pixels have similar intensity values as that of backgrounds. To resolve the problem, we propose a constrained region-growing and contrast enhancement to recover those missing parts and fill in the holes in-side the segmented objects. Our proposed scheme consists of three elements: (i) a simple linear transform for contrast enhancement to enable stronger edge de-tection; (ii) an 8-connected linking regional filter for noise removal; and (iii) a constrained region-growing for elimination of those internal holes. Our experi-ments show that the proposed scheme is effective towards revolving the under-segmentation problem, in which a representative existing algorithm with edge-map based segmentation technique is used as our benchmark.