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	Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者: Gao X.; Yu P.; Yu P. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF) which is most widely used is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions a weight and mutation particle swam optimization algorithm was proposed in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.
	Features extraction and matching of teeth image based on the SIFT algorithm (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012, July 27, 2012 - July 29, 2012, Shenyang, China 作者: Wang X.; Wang X.; Wang X. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 Using of SIFT algorithm in the image of teeth model can detect the features of the teeth image effectively. In this approach first search over all scales and image locations by using a difference-of-Gaussian function to identify potential interest points that are invariant to scale and orientation. Second select keypoints based on measures of their stability and a detailed model is fit to determine location and scale at each candidate location. Third assign one or more orientations to each keypoint location based on local image gradient directions. Last measure the local image gradients at the selected scale in the region around each keypoint. And then use the KNN algorithm to match the features. Through lots of experiments and comparing with other feature extraction methods this method can detect the features of the teeth model effectively and offer some available parameters for 3D reconstruction of the teeth model. the authors.
	An image matching algorithm based on sub-block coding (EI CONFERENCE) 会议论文 OAI收割 2nd International Workshop on Computer Science and Engineering, WCSE 2009, October 28, 2009 - October 30, 2009, Qingdao, China 作者: Li S. 收藏 \| 浏览/下载：32/0 \| 提交时间：2013/03/25 In order to improve the speed of matching algorithm and simplify the processing of existing sub-block coding matching a new template matching method combined local gray value encoding matching and phase correlation is proposed. Matching process is divided into rough matching and fine matching. Rough matching divides the image into certain size blocks called R-block sums the gray value of each R-block pixel encodes the R-block according to the gray value distribution of R-block with the adjacent R-block and matches by step between the template and each search sub-image. Then fine matching results are obtained using phase correlation according to initial match parameters. The time complexity of the proposed method is (M2) .The new algorithm is faster than traditional algorithm by two orders of magnitude and the speed has improved twice compared with existing sub-block coding method. Experiments demonstrate that the new algorithm is robust to the linear transformation of pixel grey value and image noise and it also has the stability of small-angle rotation. 2009 IEEE.
	Optimal HP configurations of proteins by combining local search with elastic net algorithm 期刊论文 OAI收割 JOURNAL OF BIOCHEMICAL AND BIOPHYSICAL METHODS, 2007, 卷号: 70, 期号: 3, 页码: 335-340 作者: \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2018/07/30 protein folding problem 2D HP lattice model elastic net algorithm local search method