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论文题目：Complex Opinion Network Correlation Clustering 作者：Fu Yan Wang * , Sha Qiu, Qing Li 期刊： Applied Mechanics and Materials (Volumes 644 - 650) doi: 10.4028/www.scientific.net/AMM.644-650.2846 |
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好吧，记得评分！ 第一篇 Accession number: 20140217182108 Title: The improved method for image edge detection based on wavelet transform with modulus maxima Authors: Wang, Fu Yan1 Email author 4626747@qq.com; Chen, Min2 Email author forestcm@163.com; Fei, Qing Shui2 Email author ynu_fei@yeah.net Author affiliation: 1 Information and Technology College, Kunming University, Kunming, China 2 Information College, Yunnan University, Kunming, China Source title: Advanced Materials Research Abbreviated source title: Adv. Mater. Res. Volume: 850-851 Monograph title: Advances in Applied Sciences and Manufacturing Issue date: 2014 Publication year: 2014 Pages: 897-900 Language: English ISSN: 10226680 ISBN-13: 9783037859537 Document type: Conference article (CA) Conference name: 2013 International Forum on Materials Analysis and Testing Technology, IFMATT 2013 Conference date: December 9, 2013 - December 10, 2013 Conference location: Qingdao, China Conference code: 101782 Publisher: Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland Abstract: The improved method for image edge detection based on wavelet transform modulus maxima included following steps: wavelet transform was adopted to compute local modulus maximum of edge and noise. Based on the differences between wavelet transforms of edge and noise, the separation of noise and edge was achieved by detecting local modulus maximum with quadratic discriminate method. Simulation results indicate that the inconsistency between high precision localization and high denoising ability existing in traditional edge detection algorithm could be resolved by means of the algorithm. © (2014) Trans Tech Publications, Switzerland. Number of references: 8 Main heading: Wavelet transforms Controlled terms: Edge detection - Testing Uncontrolled terms: De-noising - Edge detection algorithms - High-precision localization - Image edge detection - Local modulus maximum - Modulus maxima - Wavelet transform modulus maxima Classification code: 423.2 Non Mechanical Properties of Building Materials: Test Methods - 716 Telecommunication; Radar, Radio and Television - 921.3 Mathematical Transformations DOI: 10.4028/www.scientific.net/AMR.850-851.897 Database: Compendex Compilation and indexing terms, © 2014 Elsevier Inc. 第二篇 Accession number: 20134416937557 Title: Context quantization based on the modified K-means clustering Authors: Chen, Min1 Email author minkeychen@sina.cn; Wang, Fu Yan2 Email author 757441312@qq.com Author affiliation: 1 College of Information Science, Yunnan University, Kunming, 650091, China 2 College of Information and Technology, Kunming University, Kunming, 650214, China Source title: Advanced Materials Research Abbreviated source title: Adv. Mater. Res. Volume: 756-759 Monograph title: Information Technology Applications in Industry, Computer Engineering and Materials Science Issue date: 2013 Publication year: 2013 Pages: 4068-4072 Language: English ISSN: 10226680 ISBN-13: 9783037857700 Document type: Conference article (CA) Conference name: 3rd International Conference on Materials Science and Information Technology, MSIT 2013 Conference date: September 14, 2013 - September 15, 2013 Conference location: Nanjing, Jiangsu, China Conference code: 100389 Sponsor: Trans tech publications inc.; Computer Science and Electronic Technology; BITS Narsampet; Universitatea Politehnica Din Bucuresti Publisher: Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland Abstract: The context quantization for I-ary source based on the modified K-means clustering algorithm is present in this paper. In this algorithm, the adaptive complementary relative entropy between two conditional probability distributions, which is used as the distance measure for K-means instead, is formulated to describe the similarity of these two probability distributions. The rules of the initialized centers chosen for K-means are also discussed. The proposed algorithm will traverse all possible number of the classes to search the optimal one which is corresponding to the shortest adaptive code length. Then the optimal context quantizer is achieved rapidly and the adaptive code length is minimized at the same time. Simulations indicate that the proposed algorithm produces better coding result than the result of other algorithm. © (2013) Trans Tech Publications, Switzerland. Number of references: 8 Main heading: Information technology Controlled terms: Clustering algorithms - Materials science - Optimal systems - Optimization - Probability distributions Uncontrolled terms: Adaptive code lengths - Conditional probability distributions - Context quantization - Distance measure - K-means - Model contexts - Modified k-means clustering - Relative entropy Classification code: 721 Computer Circuits and Logic Elements - 903 Information Science - 921.5 Optimization Techniques - 922.1 Probability Theory - 951 Materials Science DOI: 10.4028/www.scientific.net/AMR.756-759.4068 Database: Compendex Compilation and indexing terms, © 2014 Elsevier Inc. |
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