Two-dimensional object recognition by matching local properties of contour points

This paper describes a two-stage Hough procedure for two-dimensional object recognition based on matching local properties of a model set of contour points. The procedure makes use of three strategies: subdivision of the parameter space, classification by multiple properties, and non-maximum suppression. The first stage estimates likely values in a coarsely quantized space of position and orientation. The second stage verifies these estimates in a more precise space with a large set of model points. In both cases, matching of model points to image contour points is based on tangent, curvature and contrast. The evaluation of the parameter space is greatly simplified by simultaneous non-maximum suppression over neighborhoods of both parameters. Experiments show that these strategies are both efficient and robust, even in the case of complex and partial data. One example uses overlapping parts, such as might occur in industrial situations.