The combinatorics of heuristic search termination for object recognition in cluttered environments
Earlier work on using constrained search to locate objects in cluttered scenes showed that the expected search is quadratic in the number of features, if all the data comes from one object, but is exponential if spurious data is included. Consequently, many methods terminate search once a “good” interpretation is found. Here, we show that correct termination procedures can reduce the exponential search to quartic. This analysis agrees with empirical data for cluttered object recognition. These results imply that one must select subsets of the data likely to have come from one object, before finding a correspondence between data and model features.
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