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专门用Python讲解计算机视觉编程

Content Description

《Python计算机视觉编程》是计算机视觉编程的实践指南,依赖Python语言讲解了基础理论与算法,并通过大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。另外,书中附带的练习还能让读者巩固并学会应用编程知识。
《Python计算机视觉编程》适合的读者是:有一定编程与数学基础,想要了解计算机视觉的基本理论与算法的学生,以及计算机科学、信号处理、物理学、应用数学和统计学、神经生理学、认知科学等领域的研究人员和从业者。
Author Description

Jan Erik Solem,瑞典隆德大学副教授(数学成像小组),Polar Rose公司创始人兼CTO,计算机视觉研究者,Python爱好者,技术图书作家,经常出席各种计算机视觉、图像分析、机器智能等国际会议并发表演讲。他主要关注3D重建、变分问题与优化、图像分割与识别、形状分析,有多年Python计算机视觉教学、研究和行业应用经验,技术博客为https://www.janeriksolem.net。另著有Computing with Python: An Introduction to Python for Science and Engineering一书。
Comments

“本书介绍了各种图像分析工具,是了解计算机视觉编程的读物。”
——James A. Cox,美国《中西部书评》(Midwest Book Review)总编辑
Catalogue

推荐序XI
前言XIII

第1章基本的图像操作和处理1
1.1PIL:Python图像处理类库1
1.1.1转换图像格式2
1.1.2创建缩略图3
1.1.3复制和粘贴图像区域3
1.1.4调整尺寸和旋转3
1.2Matplotlib4
1.2.1绘制图像、点和线4
1.2.2图像轮廓和直方图6
1.2.3交互式标注7
1.3NumPy8
1.3.1图像数组表示8
1.3.2灰度变换9
1.3.3图像缩放11
1.3.4直方图均衡化11
1.3.5图像平均13
1.3.6图像的主成分分析(PCA)14
1.3.7使用pickle模块16
1.4SciPy17
1.4.1图像模糊18
1.4.2图像导数19
1.4.3形态学:对象计数22
1.4.4一些有用的SciPy模块23
1.5高级示例:图像去噪24
练习28
代码示例约定29

第2章局部图像描述子31
2.1Harris角点检测器31
2.2SIFT(尺度不变特征变换)39
2.2.1兴趣点39
2.2.2描述子39
2.2.3检测兴趣点40
2.2.4匹配描述子43
2.3匹配地理标记图像47
2.3.1从Panoramio下载地理标记图像47
2.3.2使用局部描述子匹配50
2.3.3可视化连接的图像52
练习54

第3章图像到图像的映射57
3.1单应性变换57
3.1.1直接线性变换算法59
3.1.2仿射变换60
3.2图像扭曲61
3.2.1图像中的图像63
3.2.2分段仿射扭曲67
3.2.3图像配准70
3.3创建全景图76
3.3.1RANSAC77
3.3.2稳健的单应性矩阵估计78
3.3.3拼接图像81
练习84

第4章照相机模型与增强现实85
4.1针孔照相机模型85
4.1.1照相机矩阵86
4.1.2三维点的投影87
4.1.3照相机矩阵的分解89
4.1.4计算照相机中心90
4.2照相机标定91
4.3以平面和标记物进行姿态估计93
4.4增强现实97
4.4.1PyGame和PyOpenGL97
4.4.2从照相机矩阵到OpenGL格式98
4.4.3在图像中放置虚拟物体100
4.4.4综合集成102
4.4.5载入模型104
练习106

第5章多视图几何107
5.1外极几何107
5.1.1一个简单的数据集109
5.1.2用Matplotlib绘制三维数据111
5.1.3计算F:八点法112
5.1.4外极点和外极线113
5.2照相机和三维结构的计算116
5.2.1三角剖分116
5.2.2由三维点计算照相机矩阵118
5.2.3由基础矩阵计算照相机矩阵120
5.3多视图重建122
5.3.1稳健估计基础矩阵123
5.3.2三维重建示例125
5.3.3多视图的扩展示例129
5.4立体图像130
练习135

第6章图像聚类137
6.1K-means聚类137
6.1.1SciPy聚类包138
6.1.2图像聚类139
6.1.3在主成分上可视化图像140
6.1.4像素聚类142
6.2层次聚类144
6.3谱聚类152
练习157

第7章图像搜索159
7.1基于内容的图像检索159
7.2视觉单词160
7.3图像索引164
7.3.1建立数据库164
7.3.2添加图像165
7.4在数据库中搜索图像167
7.4.1利用索引获取候选图像168
7.4.2用一幅图像进行查询169
7.4.3确定对比基准并绘制结果171
7.5使用几何特性对结果排序172
7.6建立演示程序及Web应用176
7.6.1用CherryPy创建Web应用176
7.6.2图像搜索演示程序176
练习179

第8章图像内容分类181
8.1K邻近分类法(KNN)181
8.1.1一个简单的二维示例182
8.1.2用稠密SIFT作为图像特征185
8.1.3图像分类:手势识别187
8.2贝叶斯分类器190
8.3支持向量机195
8.3.1使用LibSVM196
8.3.2再论手势识别198
8.4光学字符识别199
8.4.1训练分类器200
8.4.2选取特征200
8.4.3多类支持向量机201
8.4.4提取单元格并识别字符202
8.4.5图像校正205
练习206

第9章图像分割209
9.1图割(Graph Cut)209
9.1.1从图像创建图211
9.1.2用户交互式分割216
9.2利用聚类进行分割218
9.3变分法224
练习226

第10章OpenCV227
10.1OpenCV的Python接口227
10.2OpenCV基础知识228
10.2.1读取和写入图像228
10.2.2颜色空间228
10.2.3显示图像及结果229
10.3处理视频232
10.3.1视频输入232
10.3.2将视频读取到NumPy数组中234
10.4跟踪234
10.4.1光流235
10.4.2Lucas-Kanade算法237
10.5更多示例243
10.5.1图像修复243
10.5.2利用分水岭变换进行分割244
10.5.3利用霍夫变换检测直线245
练习246

附录A安装软件包247
A.1NumPy和SciPy247
A.1.1Windows247
A.1.2Mac OS X247
A.1.3Linux248
A.2Matplotlib248
A.3PIL248
A.4LibSVM249
A.5OpenCV249
A.5.1Windows和Unix249
A.5.2Mac OS X249
A.5.3Linux250
A.6VLFeat250
A.7PyGame250
A.8PyOpenGL250
A.9Pydot251
A.10Python-graph251
A.11Simplejson252
A.12PySQLite252
A.13CherryPy252

附录B图像集253
B.1Flickr253
B.2Panoramio254
B.3牛津大学视觉几何组255
B.4肯塔基大学识别基准图像255
B.5其他256
B.5.1Prague Texture Segmentation Datagenerator与基准256
B.5.2微软研究院Grab Cut数据集256
B.5.3Caltech 101256
B.5.4静态手势数据库256
B.5.5Middlebury Stereo数据集256

附录C图片来源257
C.1来自Flickr的图像257
C.2其他图像258
C.3插图258

参考文献259
索引263

Introduction

推荐序
计算机视觉是一门实践性很强的课程,虽然已经有了不少教科书,但大多数都只重视其中的理论和算法,很少有实践指导书。因而对于学习者而言,如果希望在实践中学习,往往需要编写大量的程序。在这方面,本书的出版可以算是一个有效的补充,相信本书将成为计算机视觉学习者的一个重要参考。
作为一本面向计算机视觉编程的书,本书涉及了这一学科中相对成熟并且被以往实践验证有效的部分典型算法,因而具有很好的实用性。例如第2 章描述子部分选择了Harris 角点检测器和SIFT 描述子及其实现加以介绍;第3 章则以全景图的创建为例,给出了RANSAC 的实现;第9 章图像分割中讨论了Graph Cut 的实现等。这些方法大多数具有很好的通用性,因而为读者提供了一种实现范例。
本书的另一个特点是对介绍的单一方法,通过综合运用提升学习者灵活应用这些方法的能力。例如第4 章给出的增强现实的例子,以及第8 章给出的数独图像校正的例子。这些例子能够帮助进一步提升学习者对前述方法的感性认识。
与早期计算机视觉领域多数程序都是由C/C++ 写就的情形不同。随着计算机硬件速度越来越快,研究者在考虑选择实现算法语言的时候会更多地考虑编写代码的效率和易用性,而不是像早年那样把算法的执行效率放在首位。这直接导致近年来越来越多的研究者选择Python 来实现算法。与此同时,Python 的开放性使不同领域的研究者能够有机会在Python 中加入他们需要的特性,甚至可以纳入Python 的标准库,这也大大吸引了众多研究者对Python 的参与。
本书的第三个特点是提供了与OpenCV 接口的介绍。这为利用OpenCV 中的资源提供了方便的途径。
今天在计算机视觉领域,越来越多的研究者使用Python 开展研究。本书中文版的出版一方面能够鼓励更多的研究者采用这一语言,另一方面则为Python 的学习者提供了一种尝试不同领域算法的机会。
中国计算机学会副秘书长陈熙霖
2014.4.21

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