Shape Context and Chamfer Matching in Cluttered Scenes

This paper compares two methods for object localization from contours: shape context and chamfer matching of templates. In the light of our experiments, we suggest improvements to the shape context: Shape contexts are used to find corresponding features between model and image. In real images it is shown that the shape context is highly influenced by clutter, furthermore even when the object is correctly localized, the feature correspondence may be poor. We show that the robustness of shape matching can be increased by including a figural continuity constraint. The combined shape and continuity cost is minimized using the Viterbi algorithm on features sequentially around the contour, resulting in improved localization and correspondence. Our algorithm can be generally applied to any feature based shape matching method. Chamfer matching correlates model templates with the distance transform of the edge image. This can be done efficiently using a coarse-to-fine search over the transformation parameters. The method is robust in clutter, however multiple templates are needed to handle scale, rotation and shape variation. We compare both methods for locating hand shapes in cluttered images, and applied to word recognition in EZ-Gimpy images.