Judging Whether Multiple Silhouettes Can Come from the Same Object

We consider the problem of recognizing an object from its silhouette. We focus on the case in which the camera translates, and rotates about a known axis parallel to the image, such as when a mobile robot explores an environment. In this case we present an algorithm for determining whether a new silhouette could come from the same object that produced two previously seen silhouettes. In a basic case, when cross-sections of each silhouette are single line segments, we can check for consistency between three silhouettes using linear programming. This provides the basis for methods that handle more complex cases. We show many experiments that demonstrate the performance of these methods when there is noise, some deviation from the assumptions of the algorithms, and partial occlusion. Previous work has addressed the problem of precisely reconstructing an object using many silhouettes taken under controlled conditions. Our work shows that recognition can be performed without complete reconstruction, so that a small number of images can be used, with viewpoints that are only partly constrained.