Computer Vision,A Modern Approach(中文版) 计算机视觉的现代方法！

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 Nei

A Guide to Convolutional Neural Networks for Computer Vision-Morgan & Claypool(2018).pdf

基本包括了所有出现于各种数字图像处理教材、论文的标准测试图片，共335张，classic，oldclassic，aerials，misc，sequences，textures，Kodak，special，additional，Public-Domain Test Images for Homeworks and Projects，Photos with lines & edges，Bright colours photos……

Mastering OpenCV with Practical Computer Vision Projects pdf

Key Features Written to the latest, gold-standard specification of OpenCV 3 Master OpenCV, the open source library of the computer vision community Master fundamental concepts in computer vision and image processing Learn about the important classes and functions of OpenCV with complete working examples applied to real images Book Description Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you'll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you'll also get acquainted with recent approaches in machine learning and object classification.

计算机视觉中的多视图几何第二版（英文）Multiple View Geometry in Computer Vision.2nd Edition.pdf

David A. Forsyth，Jean Ponce著，英文第二版

虽然书名是关于计算机视觉在视觉特效中的应用，但是这是机器视觉方面的一个很好的综述：Image Matting，Image Compositing and Editing，Features and Matching，Dense Correspondence and Its Applications，Matchmoving，Motion Capture，Three-Dimensional Data Acquisition ...

多视图几何的经典书籍