MESH-Based Active Monte Carlo Recognition (MESH-AMCR)

Abstract

In this paper we extend Active Monte Carlo Recognition (AMCR), a recently proposed framework for object recognition. The approach is based on the analogy between mobile robot localization and object recognition. Up to now AMCR was only shown to work for shape recognition on binary images. In this paper, we significantly extend the approach to work on realistic images of real world objects. We accomplish recognition under similarity transforms and even severe non-rigid and non-affine deformations. We show that our approach works on databases with thousands of objects, that it can better discriminate between objects than state-of-the art approaches and that it has significant conceptual advantages over existing approaches: It allows iterative recognition with simultaneous tracking, iteratively guiding attention to discriminative parts, the inclusion of feedback loops, the simultaneous propagation of multiple hypotheses, multiple object recognition and simultaneous segmentation and recognition. While recognition takes place triangular meshes are constructed that precisely define the correspondence between input and prototype object, even in the case of strong non-rigid deformations.