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.
voting model-based shape extraction pose cluster dynamic programming belief propagation geometric hashing object detection