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In this paper, we focus on unsupervised feature selection. As we have known, the combination of several feature units into a whole feature vector is broadly adopted for effective object representation, which may inevitably includes some irrelevant/redundant feature units or feature dimensions. Most of the traditional feature selection models can only select the feature dimensions without concerning...
In this paper, we develop a computer aided diagnosis algorithm to detect and classify the abnormalities in vision-based endoscopic examination. We focus on analyzing the traditional gastroscope data and help the medical experts improve the accuracy of medical diagnosis with our analysis tool. To achieve this, we first segment the image into superpixels, then extract various color and texture features...
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