A fundamental problem in image analysis is the integration of information across scale to detect and classify objects. We have developed, within a machine learning framework, two classes of multiresolution models for integrating scale information for object detection and classification-a discriminative model called the hierarchical pyramid neural network and a generative model called a hierarchical image probability model. Using receiver operating characteristic analysis, we show that these models can significantly reduce the false positive rates for a well-established computer-aided diagnosis system.
Hierarchical, multi-resolution models for object recognition: applications to mammographic computer-aided diagnosis
01.01.2000
718086 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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