A Voting Scheme for Partial Object Extraction under Cluttered Environment

Shape extraction aims to detect and localize objects via the shape information. The paper presents a novel voting scheme that can extract partially occluded objects under cluttered environments using a single shape. It works by jointly figuring out the boundaries and resolving the geometric configurations. To model the missing part lead by occlusion, we discretize the shape template into a set of its subpart, named portions. Our representation of shape template is through a set of portion together with their interconnections. Instead of forming a fully connected network, our interconnections make the portions consistent with the chain along the boundary of shape template. Based on the representation, we formulate an auto-locked objective function that contains both the unary and pairwise terms and balances the effects of missing parts. Min-sum voting scheme with strategy driven by bottom–up information is then proposed to minimize the objective function. Conducted experiments show that proposed algorithm is promising for shape extraction with occlusion and noisy backgrounds and allows the non-rigid deformations.

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