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Titlebook: Computer Vision; CCF Chinese Conferen Honbin Zha,Xilin Chen,Qiguang Miao Conference proceedings 2015 Springer-Verlag Berlin Heidelberg 2015

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Finance and Capital Markets Seriesresentation (DSR) graph for semi-supervised learning (SSL) to address this problem. For graph-based methods, how to construct a graph among the pixels is the key to a successful classification. Our graph construction method contains two steps. Sparse representation (SR) method is first employed to e
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The Credit Risk of Complex Derivatives FER via the Boosted Convolutional Neural Network (Boosted-CNN). First, we use the Convolutional Neural Network to train a strong classifier to classify different facial expressions. Based on the classification accuracy of each expression, we use random sampling methods for imbalanced learning on ea
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1865-0929 uter Vision, CCCV 2015, held in Xi‘an, China, in September 2015. .The total of 89 revised full papers presented in both volumes were carefully reviewed and selected from 176 submissions. The papers address issues such as computer vision, machine learning, pattern recognition, target recognition, obj
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The Term Structure of Interest Rates,el can be used to segment the conventional images. The modified Mumford-Shah model can dispose the textures well. What’s more, in order to improve the computation efficiency, this paper designs the Split-Bregman algorithm. At last, our performance is demonstrated by segmenting many real texture images.
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