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We present a technique for automatic diagnosis of malignant melanoma based exclusively on local pattern analysis. The technique relies on local binary patterns in small sections in the image, and automatically selects the relevant texture features from those that discriminate best between benign and malignant skin lesions. The classification is performed using support vector machines, and the feature...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is an important problem in computer aided detection. To improve the performance of detection, we propose a bagging-based twin support vector machine (B-TWSVM) to detect MCs. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed...
A method of the recognition of electricity equipments operation state (EEOS) is put up based on support vector machine (SVM). First Chinese character or number operation state images of electricity equipments are segmented with C-mean clustering. Then, feature vector of operation state image of electricity equipments is extracted using K-L transform. At last, classification method of SVM for state...
Clustered microcalcification is an important signal for breast cancer in the early stages. In this paper, we propose a multiple kernel SVM with group features (GF-SVM) to tackle problems associated with heterogeneous features of clustered microcalcification and normal breast tissues in suspicious regions. Specifically, different types of features such as being gradient, geometric and textural are...
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