The purpose of object matching is to decide the similarity between two objects. This paper introduces 24 possible distance m e asures based on the Hausdor distance b etween two point sets. These measures can be used to match two sets of edge points extracted f r om any two objects. Based on our experiments on synthetic images containing various levels of noise, we determined that one of these distance m e asures, called the modi ed Hausdor distance (MHD) has the best performance for object matching. The advantages of MHD over other distances are also demonstrated o n several edge maps of objects extracted f r om real images.

We present an efficient multi stage approach to detection of deformable objects in real, cluttered images given a single or few hand drawn examples as models. The method handles deformations of the object by first breaking the given model into segments at high curvature points. We allow bending at these points as it has been studied that deformation typically happens at high curvature points. The broken segments are then scaled, rotated, deformed and searched independently in the gradient image. Point maps are generated for each segment that represent the locations of the matches for that segment. We then group k points from the point maps of k adjacent segments using a cost function that takes into account local scale variations as well as inter-segment orientations. These matched groups yield plausible locations for the objects. In the fine matching stage, the entire object contour in the localized regions is built from the k-segment groups and given a comprehensive score in a method that uses dynamic programming. An evaluation of our algorithm on a standard dataset yielded results that are better than published work on the same dataset. At the same time, we also evaluate our algorithm on additional images with considerable object deformations to verify the robustness of our method.

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