Patent ID: 7133560

Claim:
An object detection learning method comprises the following steps: (a) inputting a reference CAT model containing the basic structure and component feature values of an ideal subject and its tolerance ranges wherein each node of the reference CAT model specifies its expected CAT component types, expected CAT component features and expected CAT component relations and reference mask; (b) inputting at least one learning image; (c) inputting a detection algorithm database containing multiple detection algorithms for incorporation into the detection sequence of a CAT node; (d) performing candidate algorithm generation using the detection algorithm database based on the type of component, component features and component relations stored in the reference CAT model having an candidate algorithm output; (e) performing candidate algorithm evaluation using the candidate algorithm output to select the appropriate detection algorithm for the component of interest having a detection algorithm output wherein the candidate algorithm evaluation step further comprises the following steps: (i) performing candidate algorithm application using the at least one learning image and the algorithm candidate output having an algorithm result output containing detected component mask; (ii) performing candidate algorithm scoring using the reference CAT model and the algorithm result output containing detected component feature values having an algorithm score output; (iii) performing detection algorithm selection using the algorithm score output having a detection algorithm output.