Programming Computer Vision with Python.pdf下载

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