Trainable program for recognizing remote-probing data
F. R. Gal'yano Sizasko · Journal of Optical Technology · 2010
This article discusses the use of an invariant image representation to create a composite system of segmentation algorithms. The representation is called invariant since it is independent of scale transformations of the image brightness that maintain the number and order of the histogram bins. The operation of the system in the classification and clustering regime is considered. In the first case, an expert incrementally trains the system, improving the composition of the training selection on the basis of the feedback formed by the system. In the training process, the system automatically eliminates from the initial set of segmentation algorithms a subset of algorithms that cannot be used to distinguish the pixels of the training selection. The structure of the software implementation is described. An example is given of the processing of remote-probing data.