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Classifying images of HEp-2 cells from indirect im-munofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The...
A standard paradigm to apply graph based representations to computer vision and pattern recognition is to construct a graph from the problem and then formulate the problem in terms of finding cliques in the graph. Many methods have been proposed to extract maximum clique, enumerate all cliques or a number of largest cliques. In this paper we present an approach to a new problem of target clique extraction,...
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