Shape point set matching based on oriented shape context in turbulence-cluttered scene

Abstract

The main challenges of shape point set matching in a long-distance imaging scene stem from the optical turbulence effects, which lead to shape object deformation, rotation, shifted object positions, and cluttered outliers. To address this problem, we propose an effective energy cost model with figural continuity constraint. We first construct an Oriented Shape Context (OSC) descriptor using attributes of shape edges’ length and direction, which represent rotation invariance, by adding the oriented model (prototype) edges point set. Then, inspired by the figural continuity prior between the model and target point set, we transform the continuity constraint into a matching energy cost model. Lastly, we develop a simple 2-tree graph to minimize the matching cost function using the Dynamic Program (DP) optimization algorithm. The extensive experiments on both synthetic and real data validate that the proposed method can effectively detect the desired shapes in the complex and highly turbulence-cluttered scenes.