A higher-order statistical theory of matching models against images is developed. The basic idea is to take into account how much of an object can be seen in the image, and what parts of it are jointly present. It is shown that this additional information can improve the specificity (i.e., reduce the probability of false positive matches) of a recognition algorithm. Higher-order statistics are derived from a physical world model and the minimum description length principle. Statistical information is used in a top-down way for the evaluation (verification) of specific model and pose hypotheses. >
['Thomas M. Breuel']
Higher-order statistics in object recognition