多尺度变换及其在图像纹理分类中的应用董永生著科学 pdf下载

基本信息

书名:多尺度变换及其在图像纹理分类中的应用（英文版）

定价：178元

作者:董永生 著

出版社：科学出版社

出版日期：2021-06-01

ISBN：9787030690579

字数：

页码：315

版次：

装帧：平装

开本：16开

商品重量：

内容提要

《多尺度变换及其在图像纹理分类中的应用(英文)》在归纳分析外相关研究的基础上，从小波变换，轮廓变换，剪切波等多尺度变换，以及多尺度变换的子带选择等全新角度研究了图像纹理分类理论和方法，并且还对大数据图像纹理分析和分类问题进行了研究。主要内容包括 n
（1）研究背景，对早期多尺度变换和图像纹理分类理论和方法给出一个概述性的总结； n
（2）对当前主要多尺度变换的理论框架进行总结性介绍 n
（3）研究小波域直方图比对的纹理分类理论和方法 n
（4）研究轮廓波域泊松混合模型，及其基于该模型的纹理分类方法； n
（5）研究基于轮廓波域聚类的纹理分类理论和方法 n
（6）研究剪切波子带依赖性的线性回归模型，以及基于该模型的的纹理分类方法 n
（7）研究轮廓波子带的统计特征提取方法，以及基于轮廓波域统计特征的纹理分类方法 n
（8）研究了多尺度变换的子带选择理论，以及基于子带选择的图像纹理分类方法 n
（9）针对当前视觉大数据分析的重要性和难题，研究了大数据图像纹理的分类理论和方法

目录

Contents n
Preface n
Chapter 1 Introductio1 n
1.1 Multiscale Methods 1 n
1.2 Texture Databases 4 n
References 11 n
Chapter 2 Local Energy Histograms iWavelet Domains for Texture Classificatio13 n
2.1 Introductio13 n
2.2 Proposed Texture ClassificatioMethod 14 n
2.3 Experimental Results 17 n
2.4 AEfficient Histogram-Based Texture ClassificatioMethod with Weighted Symmetrized Kullback-Leibler Divergence 22 n
2.5 Experimental Results 25 n
References 30 n
Chapter 3 PoissoMixture Model iContourlet Domains for Texture Classificatio33 n
3.1 Introductio33 n
3.2 Contourlet Transform 35 n
3.3 PoissoMixtures and its BYY Harmony Learning 36 n
3.4 Proposed BayesiaTexture Classifier 38 n
3.5 Experimental Results 45 n
3.6 Conclusions 54 n
References 55 n
Chapter 4 Product Bernoulli Distributions iContourlet Domains for Texture Classificatio58 n
4.1 Introductio58 n
4.2 Contourlet Transform 59 n
4.3 Proposed Texture ClassificatioMethod 60 n
4.4 Experimental Results 62 n
4.5 Statistical Contourlet Subband Characterizatiofor Texture Image Retrieval 67 n
References 73 n
Chapter 5 Subband Clutering iContourlet Domains for Texture Classificatio76 n
5.1 Introductio76 n
5.2 Contourlet Transform 77 n
5.3 New Texture ClassificatioMethod 78 n
5.4 Experimental Results 84 n
5.5 Conclusions 91 n
References 92 n
Chapter 6 Linear RegressioModel iShearlet Domains for Texture Classificatioand Retrieval 95 n
6.1 Introductio95 n
6.2 Shearlet Transform 97 n
6.3 Texture ClassificatioBased oLinear RegressioModeling the Dependence BetweeShearlet Subbands 98 n
6.4 Texture Retrieval Based oLinear RegressioModeling 107 n
6.5 Experimental Results 111 n
6.6 Conclusions 118 n
References 118 n
Chapter 7 Heterogeneous and Incrementally Generated Histogram iWavelet Domains for Texture Classificatio122 n
7.1 Introductio122 n
7.2 Related Work 124 n
7.3 Nonnegative MultiresolutioRepresentatioof Texture 125 n
7.4 HessiaRegularized Discriminative Nonnegative Matrix Factorizatio129 n
7.5 NMV-based Texture Classificatiovia HNMF 134 n
7.6 Experimental Results 135 n
7.7 Conclusions 144 n
References 145 n
Chapter 8 Multiscale Sampling iWavelet Domains for Texture Classificatio150 n
8.1 Introductio150 n
8.2 Multiscale Rotation-invariant Texture RepresentatioFramework 151 n
8.3 Experiments 155 n
8.4 Conclusions 159 n
References 160 n
Chapter 9 Multi-scale Counting and Difference Representatiofor Texture Classificatio163 n
9.1 Introductio163 n
9.2 Proposed Multi-scale Counting and Difference Representatioof Texture Images 166 n
9.3 Experiments 172 n
9.4 Conclusions 179 n
References 180 n
Chapter 10 Jumping and Refined Local Patterfor Texture Classificatio183 n
10.1 Introductio183 n
10.2 Related Work 185 n
10.3 Jumping and Refined Local Patter186 n
10.4 Experimental Results 195 n
10.5 Conclusions 202 n
References 203 n
Chapter 11 Locally Directional and Extremal Patterfor Texture Classificatio207 n
11.1 Introductio207 n
11.2 Related Work 209 n
11.3 Locally Directional and Extremal Patter210 n
11.4 Experimental Results 217 n
11.5 Conclusions 225 n
References 225 n
Chapter 12 Multiscale Symmetric Dense Micro-block Difference for Texture Classificatio230 n
12.1 Related Work 232 n
12.2 Texture Classicatio234 n
12.3 Experiments 242 n
12.4 Conclusions 249 n
References 249 n
Chapter 13 Completed Extremely Nonnegative DMD for Color Texture Classificatio255 n
13.1 Introductio255 n
13.2 Related Work 258 n
13.3 Our Proposed Completed Extremely Nonnegative DMD Color Texture RepresentatioMethod 259 n
13.4 Experimental Results 267 n
13.5 Conclusions 277 n
References 278 n
Chapter 14 Compact Interchannel Sampling Difference Descriptor for Color Texture Classificatio283 n
14.1 Background 283 n
14.2 The Proposed Compact Interchannel Sampling Difference Descriptor 286 n
14.3 Experimental Results 295 n
14.4 Conclusions 307 n
References 307 n
Chapter 15 Conclusions and Future Work 313 n
15.1 Conclusions 313 n
15.2 Future Work 315 n
Colourful Figures