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

咒語

Multispectral Image Classification Using a New Bayesian Approach with Weighted Markov Random Fieldsnnealing optimization algorithm is used to accomplish the maximum a posteriori classification. The proposed approach is compared with recently advanced multispectral image classification methods. The comparison results of classification suggested that the proposed approach outperformed other classifiers in overall accuracy and kappa coefficient.

Explicate

Structured Sparse Coding for Classification via Reweighted , Minimization, scheme. We evaluated our method by applying it to several image classification tasks. The experiments showed the improvement of the proposed structured sparse coding method over several existing discriminative sparse coding methods on tested data sets.

小教堂

rheumatology

Target Recognition Based on Feature Weighted Intuitionistic FCM,FCM algorithm is well applied to typical target recognition on air. Simulation experiments prove that the technique proposed is promising and effective, while satisfactory results verify their applicability greatly.

內(nèi)向者

甜食

Effective Facial Expression Recognition via the Boosted Convolutional Neural Network,ch expression to get better performance .The Extended Cohn-Kanade (CK+) database and the JAFFE database are used to evaluate the performance of the proposed Boosted-CNN method. Experimental results show that the proposed method can achieve better classification rates compared with other state-of-art methods.

MURAL

Conference proceedings 2015pers address issues such as computer vision, machine learning, pattern recognition, target recognition, object detection, target tracking, image segmentation, image restoration, face recognition, image classification..

constellation

Constitution

The Need for a Financial System?,with hypergraph Laplacian. Hypergraph Laplacian matrix is constructed with patch alignment framework. In this way, an automatic feature extractor for silhouettes is achieved. Experimental results on two datasets show that the recovery error has been reduced by 10% to 20%, which demonstrates the effectiveness of the proposed method.

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