Constrained Region-Growing and Edge Enhancement Towards Automated Semantic Video Object Segmentation
Main information
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 inside
the segmented objects. Our proposed scheme consists of three elements: (i)
a simple linear transform for contrast enhancement to enable stronger edge detection;
(ii) an 8-connected linking regional filter for noise removal; and (iii) a
constrained region-growing for elimination of those internal holes. Our experiments
show that the proposed scheme is effective towards revolving the undersegmentation
problem, in which a representative existing algorithm with edgemap
based segmentation technique is used as our benchmark.
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