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Oil adulteration detection with multi-label learning vector quantization: Oil adulteration detection with multi-label learning vector quantization 基于多标签学习向量量化的油掺假检测:基于多标签学习向量量化的油掺假检测
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03141
Jingbo Chen
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引用次数: 0
Best viewpoints selection based on feature points detection: Best viewpoints selection based on feature points detection 基于特征点检测的最佳视点选择:基于特征点检测的最佳视点选择
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03172
Fan Zhu, Fenglei Yang
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引用次数: 0
Improved ant colony genetic optimization algorithm and its application: Improved ant colony genetic optimization algorithm and its application 改进蚁群遗传优化算法及其应用:改进蚁群遗传优化算法及其应用
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03111
Chuan-hui Liu
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引用次数: 1
Single instruction multiple data vectorization of non-normalized loops: Single instruction multiple data vectorization of non-normalized loops 非归一化循环的单指令多数据向量化:非归一化循环的单指令多数据向量化
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03149
Y. Hou, Rongcai Zhao, Wei Gao
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引用次数: 0
Identification of encrypted function in malicious software: Identification of encrypted function in malicious software 恶意软件加密功能识别:恶意软件加密功能识别
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03239
Jia Cai, Qiang Wei, Yuefei Zhu
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引用次数: 1
Multi-scale analysis for remote sensing target recognition 遥感目标识别的多尺度分析
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03190
Bo Shukui, Jing Yong-ju
To solve the problem that the features of a target change with the image resolution in target recognition, a multi-scale analysis of target recognition was provided in this paper. First, the multi-scale features of a target were analyzed based on mixed pixels in multi-resolution images. At different scale, the features of a target had different behaviors that were related to the proportion of mixed pixels in the target. Second, the structural differences of the target were analyzed and illustrated from the experiments of multi-scale target recognition. Last, the concept of "dominating class" was proposed based on the multi-scale analysis. The experimental results show that the shape and structural features change with scale of the image and large scale leads big change. This paper studies the target features from multi-scale images and provides guidance on image target recognition.
针对目标识别中目标特征随图像分辨率的变化而变化的问题,提出了目标识别的多尺度分析方法。首先,基于多分辨率图像中的混合像元,分析目标的多尺度特征;在不同尺度下,目标的特征表现出不同的行为,这些行为与目标中混合像元的比例有关。其次,通过多尺度目标识别实验分析和说明目标的结构差异;最后,在多尺度分析的基础上,提出了“统治阶级”的概念。实验结果表明,图像的形状和结构特征随图像的尺度而变化,且尺度越大变化越大。研究多尺度图像中的目标特征,为图像目标识别提供指导。
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引用次数: 0
Sparse Bayesian learning for credit risk evaluation: Sparse Bayesian learning for credit risk evaluation 用于信用风险评估的稀疏贝叶斯学习:用于信用风险评估的稀疏贝叶斯学习
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03094
Taiyong Li, Huijun Wang, Jiang Wu, Zhiling Zhang, Changjie Tang
{"title":"Sparse Bayesian learning for credit risk evaluation: Sparse Bayesian learning for credit risk evaluation","authors":"Taiyong Li, Huijun Wang, Jiang Wu, Zhiling Zhang, Changjie Tang","doi":"10.3724/SP.J.1087.2013.03094","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03094","url":null,"abstract":"","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69944537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Face recognition based on orthogonal and uncorrelated marginal neighborhood preserving embedding: Face recognition based on orthogonal and uncorrelated marginal neighborhood preserving embedding 基于正交和不相关边缘邻域保持嵌入的人脸识别:基于正交和不相关边缘邻域保持嵌入的人脸识别
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03097
Dayao Chen, Xiuhong Chen
{"title":"Face recognition based on orthogonal and uncorrelated marginal neighborhood preserving embedding: Face recognition based on orthogonal and uncorrelated marginal neighborhood preserving embedding","authors":"Dayao Chen, Xiuhong Chen","doi":"10.3724/SP.J.1087.2013.03097","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03097","url":null,"abstract":"","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69944574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Further study on algebraic structure of RSA algorithm: Further study on algebraic structure of RSA algorithm RSA算法代数结构的进一步研究:RSA算法代数结构的进一步研究
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03244
Donglin Pei, Xu Li
{"title":"Further study on algebraic structure of RSA algorithm: Further study on algebraic structure of RSA algorithm","authors":"Donglin Pei, Xu Li","doi":"10.3724/SP.J.1087.2013.03244","DOIUrl":"https://doi.org/10.3724/SP.J.1087.2013.03244","url":null,"abstract":"","PeriodicalId":61778,"journal":{"name":"计算机应用","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69946003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ultrasound image anisotropic diffusion de-speckling method based on Mallat-Zhong discrete wavelet transform wavelet 基于Mallat-Zhong离散小波变换的超声图像各向异性扩散去斑方法
Pub Date : 2013-11-26 DOI: 10.3724/SP.J.1087.2013.03201
Wu Shibin, Chen Bo, D. Wangli, G. Xiaoming
In view of speckle noise in ultrasound image, there are some disadvantages of traditional anisotropic diffusion methods, such as in-sufficient noise suppression and edge details preservation. A de-speckling method based on Mallat-Zhong Discrete Wavelet Transform( MZ-DWT) wavelet was proposed. The method used MZ-DWT wavelet and Expectation Maximization( EM) algorithm as the discrimination factor between homogeneous and edge regions, making it more accurately to control diffusion intensity and rate and achieving the noise suppression and details preservation. The experimental results show that, the proposed algorithm can better de-speckle while preserving image details and the performance of the method is better than the traditional anisotropic diffusion methods.
针对超声图像中存在的散斑噪声,传统的各向异性扩散方法存在噪声抑制不足、边缘细节保存不足等缺点。提出了一种基于Mallat-Zhong离散小波变换(MZ-DWT)小波的去斑方法。该方法采用MZ-DWT小波和期望最大化(EM)算法作为均匀区域和边缘区域的区分因子,更准确地控制扩散强度和扩散速率,实现了噪声抑制和细节保留。实验结果表明,该算法在保留图像细节的同时能较好地去斑,其性能优于传统的各向异性扩散方法。
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引用次数: 0
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