Automatic building and supervised discrimination learning of appearance models of 3-D objects
Richard L. Delanoy, Jacques Verly, Dan E. Dudgeon · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Mechanisms for automatically building and refining appearance models (AMs) of 3-D objects are presented. AMs encode allowed ranges of values of target characteristics called attributes. Allowed values for each attribute of arbitrarily defined parts of a modeled object are determined by statistical analysis of an example set of known targets. Once models are built, the system learns which attributes are discriminating (important to making a correct identification) from mistakes made on a set of training data. In discrimination learning, a weight associated with an attribute is increased or decreased whenever a test for an attribute denies or supports an incorrect object identification, respectively. A consistently decreasing weight eventually results in the essential elimination of the associated attribute from the AM. We illustrate and evaluate this approach in the context of our work in automatic target recognition (ATR).