A Framework for Bean-Shape Contour Extraction
Qi Li · 2013
Contour extraction and object detection is one of fundamental problems in computer vision. Contour extraction can be guided by either global or local constraints. In this paper, we propose a local constraint based framework for bean-shape contour extraction. We propose a criterion to construct primal sketches based on connected components of Canny edge points in a channel-scale space. When a targeting object is surrounded by a complex background, a sketch token may be deficient (not closed), and it may also contain some faulty part (not on the boundary of a targeting object). We propose algorithms to detect and restore deficiencies and faults of primal sketch tokens. We present two case studies for the proposed framework: i) embryo localization, and ii) face localization. The case studies demonstrate the potential of the proposed framework.