A robust gross-to-fine pattern recognition system

This paper presents a model-based vision recognition engine for planar contours that are scale invariant of known models. Features are obtained by using a constant-curvature criterion and used to carry out efficient coarse-to-fine recognition. A robust shape matching is proposed for comparing contour fragments from scenes with partial occluding. In order to carry out an early pruning of a large portion of the models, hypotheses are only generated for a subset of contours with enough discriminative information. Poor scene contours are used later in validating or invalidating a relatively small set of hypotheses. Since hypotheses are selectively verified, blocking is avoided by extending current matching through pairing of hypotheses, predictive matching, and retrieving the next weighted hypotheses. This avoids the processing of a large number of initial hypotheses. Our evaluation shows that a high recognition error results from the use of too small a bucket size because the indexes may fall at random, producing nonrepeatable results. We use a multidimensional hashing scheme with space separation between dense parameter areas to create additional hashing tables. The robustness of the recognition is based on engineering a coarse bucket size to the best tolerance with respect to various sources of noise. Partially occluded scenes having three objects can be recognized with a success rate of 84%. The results are reproducible against changes in scale, rotation, and translation. Due to the selection of robust initial hypotheses and the structure of the selective matching system, the processing time essentially depends on scene complexity with a marginal dependence on data-base size.