Programming Computer Vision with Python

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  • Edition: 1st
  • Format: Paperback
  • Copyright: 2012-06-26
  • Publisher: Oreilly & Associates Inc

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Supplemental Materials

What is included with this book?


If you want a basic understanding of computer vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. As a student, researcher, hacker, or enthusiast, you'll learn as you follow examples written in Python-the easy-to-learn language that has modules for handling images and mathematical computing and data mining on a par with commercial alternatives. Programming Computer Vision with Python teaches computer vision in broad terms that won't bog you down in theory. Instead, you'll find this book to be inspiring and motivating. You'll get all the code you need, with clear explanations on how to reproduce the book's examples and build upon them directly.

Author Biography

Jan Erik Solem is a Python enthusiast and computer vision researcher and entrepreneur. He is also an applied mathematician, and has worked as an associate professor, startup CTO, as well as an author.

Table of Contents

Prefacep. vii
Basic Image Handling and Processingp. 1
PIL-The Python Imaging Libraryp. 1
Matplotlibp. 3
NumPyp. 7
SciPyp. 16
Advanced Example: Image De-Noisingp. 23
Exercisesp. 26
Conventions for the Code Examplesp. 27
Local Image Descriptorsp. 29
Harris Corner Detectorp. 29
SIFT-Scale-Invariant Feature Transformp. 36
Matching Geotagged Imagesp. 44
Exercisesp. 51
Image to Image Mappingsp. 53
Homographiesp. 53
Warping Imagesp. 57
Creating Panoramasp. 70
Exercisesp. 77
Camera Models and Augmented Realityp. 79
The Pin-Hole Camera Modelp. 79
Camera Calibrationp. 84
Pose Estimation from Planes and Markersp. 86
Augmented Realityp. 89
Exercisesp. 98
Multiple View Geometryp. 99
Epipolar Geometryp. 99
Computing with Cameras and 3D Structurep. 107
Multiple View Reconstructionp. 113
Stereo Imagesp. 120
Exercisesp. 125
Clustering Imagesp. 127
K-Means Clusteringp. 127
Hierarchical Clusteringp. 133
Spectral Clusteringp. 140
Exercisesp. 145
Searching Imagesp. 147
Content-Based Image Retrievalp. 147
Visual Wordsp. 148
Indexing Imagesp. 151
Searching the Database for Imagesp. 155
Ranking Results Using Geometryp. 160
Building Demos and Web Applicationsp. 162
Exercisesp. 165
Classifying Image Contentp. 167
K-Nearest Neighborsp. 167
Bayes Classifierp. 175
Support Vector Machinesp. 179
Optical Character Recognitionp. 183
Exercisesp. 189
Image Segmentationp. 191
Graph Cutsp. 191
Segmentation Using Clusteringp. 200
Variational Methodsp. 204
Exercisesp. 206
OpenCVp. 209
The OpenCV Python Interfacep. 209
OpenCV Basicsp. 210
Processing Videop. 213
Trackingp. 216
More Examplesp. 223
Exercisesp. 226
Installing Packagesp. 227
NumPy and SciPyp. 227
Matplodibp. 228
PILp. 228
LibSVMp. 228
OpenCVp. 229
VLFeatp. 230
PyGamep. 230
PyOpenGLp. 230
Pydotp. 230
Python-graphp. 231
Simplejsonp. 231
PySQLitep. 232
CherryPyp. 232
Image Datasetsp. 233
Flickrp. 233
Panoramiop. 234
Oxford Visual Geometry Groupp. 235
University of Kentucky Recognition Benchmark Imagesp. 235
Otherp. 235
Image Creditsp. 237
Images from Flickrp. 237
Other Imagesp. 238
Illustrationsp. 238
Referencesp. 239
Indexp. 243
Table of Contents provided by Ingram. All Rights Reserved.

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