Finding Multiple Object Instances with Occlusion

In this paper we provide a framework of detection and localization of multiple similar shapes or object instances from an image based on shape matching. There are three challenges about the problem. The first is the basic shape matching problem about how to find the correspondence and transformation between two shapes; second how to match shapes under occlusion; and last how to recognize and locate all the matched shapes in the image. We solve these problems by using both graph partition and shape matching in a global optimization framework. A Hough-like collaborative voting is adopted, which provides a good initialization, data-driven information, and plays an important role in solving the partial matching problem due to occlusion. Experiments demonstrate the efficiency of our method.