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Computer Vision Algorithms and Applications

Computer Vision Algorithms and Applications是一本不错的教材,里面讲解了图像处理的有关内容:边缘检测,特征提取,计算机视觉的主要内容:标定,重建等。
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Computer Vision Algorithms and Applications英文原版

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

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Computer Vision Algorithms and Applications.pdf

Computer Vision 计算机视觉经典书籍; 《计算机视觉-算法与应用》(Computer Vision & Algorithms and Applications )是一本质量非常高的计算机视觉教材;

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计算机视觉-算法与应用-中文版-完整书签- Computer Vision & Algorithms and Applications

内容为为中文影印版,还算比较清楚; 资源来自于CSDN,为了方便使用,添加了完整书签;如有问题,请留言或者发邮件到zhaopengju1988@163.com。

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Numerical Algorithms Methods for Computer Vision Machine Learning and 无水印pdf

Numerical Algorithms Methods for Computer Vision Machine Learning and Graphics 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除

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Stereo vision: Algorithms and Applications. 立体视觉:算法和应用

Stefano大牛的经典ppt,关于3D stereo讲的很透彻很详细。对于学习Stereo的学者来说是必读的材料。希望能有所帮助。

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Concise Computer Vision - An Introduction into Theory and Algorithm

Concise Computer Vision - An Introduction into Theory and Algorithm

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Computer and Machine Vision - Theory, Algorithm and Practicalities

Computer and Machine Vision - Theory, Algorithm and Practicalities (4th Edition E.R Davies) Original edition clear pdf document

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Computer&Machine Vision:Theory,Algorithms,Practicalities

计算机和机器视觉:理论、算法与实践,经典之作!

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Modern Mathematics and Applications in Computer Graphics and Vision

Modern Mathematics andApplications in Computer Graphics and Vision (2014) Hongyu Guo

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Machine Learning_Algorithms and Applications (True PDF)(2017).pdf

Machine Learning_Algorithms and Applications (True PDF)(2017).pdf Machine Learning_Algorithms and Applications (True PDF)(2017).pdf Machine Learning_Algorithms and Applications (True PDF)(2017).pdf

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Stereo Vision:Algorithms and Applications.pdf

Stereo Vision:Algorithms and Applications

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Programming Computer Vision with Python.pdf

Table of Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1. Basic Image Handling and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 PIL—The Python Imaging Library 1 1.2 Matplotlib 3 1.3 NumPy 7 1.4 SciPy 16 1.5 Advanced Example: Image De-Noising 23 Exercises 26 Conventions for the Code Examples 27 2. Local Image Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1 Harris Corner Detector 29 2.2 SIFT—Scale-Invariant Feature Transform 36 2.3 Matching Geotagged Images 44 Exercises 51 3. Image to Image Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.1 Homographies 53 3.2 Warping Images 57 3.3 Creating Panoramas 70 Exercises 77 4. Camera Models and Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.1 The Pin-Hole Camera Model 79 4.2 Camera Calibration 84 4.3 Pose Estimation from Planes and Markers 86 4.4 Augmented Reality 89 Exercises 98 iii 5. Multiple View Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.1 Epipolar Geometry 99 5.2 Computing with Cameras and 3D Structure 107 5.3 Multiple View Reconstruction 113 5.4 Stereo Images 120 Exercises 125 6. Clustering Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.1 K-Means Clustering 127 6.2 Hierarchical Clustering 133 6.3 Spectral Clustering 140 Exercises 145 7. Searching Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 7.1 Content-Based Image Retrieval 147 7.2 VisualWords 148 7.3 Indexing Images 151 7.4 Searching the Database for Images 155 7.5 Ranking Results Using Geometry 160 7.6 Building Demos andWeb Applications 162 Exercises 165 8. Classifying Image Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 8.1 K-Nearest Neighbors 167 8.2 Bayes Classifier 175 8.3 Support Vector Machines 179 8.4 Optical Character Recognition 183 Exercises 189 9. Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 9.1 Graph Cuts 191 9.2 Segmentation Using Clustering 200 9.3 Variational Methods 204 Exercises 206 10. OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 10.1 The OpenCV Python Interface 209 10.2 OpenCV Basics 210 10.3 Processing Video 213 10.4 Tracking 216 10.5 More Examples 223 Exercises 226 iv | Table of Contents A. Installing Packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 A.1 NumPy and SciPy 227 A.2 Matplotlib 228 A.3 PIL 228 A.4 LibSVM 228 A.5 OpenCV 229 A.6 VLFeat 230 A.7 PyGame 230 A.8 PyOpenGL 230 A.9 Pydot 230 A.10 Python-graph 231 A.11 Simplejson 231 A.12 PySQLite 232 A.13 CherryPy 232 B. Image Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 B.1 Flickr 233 B.2 Panoramio 234 B.3 Oxford Visual Geometry Group 235 B.4 University of Kentucky Recognition Benchmark Images 235 B.5 Other 235 C. Image Credits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 C.1 Images from Flickr 237 C.2 Other Images 238 C.3 Illustrations 238 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

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An Introduction to 3D Computer Vision Techniques and Algorithms

3D计算机视觉算法导论。, John Wiley & Sons, 2009 Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model.

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Computer Vision--Algorithm and Application(全英文高清完整版ebook)

Computer Vision--Algorithm and Application 计算机视觉--算法与应用 824页全英文高清完整版 电子书ebook非扫描版

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Computer Vision: Principles, Algorithms, Applications, Learning 5th

Computer Vision: Principles, Algorithms, Applications, Learning 第五版,E.R.Davies

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Computer Vision: Algorithms and Applications

Computer Vision: Algorithms and Applications 作者Richard Szeliski

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Computer Vision : Algorithms and Applications

一本讲述机器视觉算法和应用的经典书籍。作者是Richard Szeliski。是本不错的入门书籍。

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computer vision(Algorithms and Applications)

Computer Vision: Algorithms and Applications Richard Szeliski August 18, 2010 draft

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computer vision : Algorithms and Applications

computer vision. microsoft research Richard Szeliski 刚写的关于computer vision的书,概括的很全,而且连今年的最新进展也有。

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Computer Vision algorithms and Applications

计算机视觉的经典入门教材,Richard Szeliski

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