Programming Computer Vision with Python: Tools and Algorithms For Analyzing Images

Programming Computer Vision with Python: Tools and Algorithms For Analyzing Images

by Jan Erik Solem

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Product Details

ISBN-13: 9781449316549
Publisher: O'Reilly Media, Incorporated
Publication date: 06/26/2012
Pages: 264
Sales rank: 568,392
Product dimensions: 6.90(w) x 9.10(h) x 0.40(d)

About the Author

Jan Erik Solem is a Python enthusiast and a computer vision researcher and entrepreneur. He is an applied mathematician and has worked as associate professor, startup CTO, and now also book author. He sometimes writes about computer vision and Python on his blog www.janeriksolem.net. He has used Python for computer vision in teaching, research and industrial applications for many years. He currently lives in San Francisco.

Table of Contents

Preface;
Prerequisites and Overview;
Introduction to Computer Vision;
Python and NumPy;
Notation and Conventions;
Using Code Examples;
How to Contact Us;
Safari® Books Online;
Acknowledgments;
Chapter 1: Basic Image Handling and Processing;
1.1 1.1 PIL—The Python Imaging Library;
1.2 1.2 Matplotlib;
1.3 1.3 NumPy;
1.4 1.4 SciPy;
1.5 1.5 Advanced Example: Image De-Noising;
1.6 Exercises;
1.7 Conventions for the Code Examples;
Chapter 2: Local Image Descriptors;
2.1 2.1 Harris Corner Detector;
2.2 2.2 SIFT—Scale-Invariant Feature Transform;
2.3 2.3 Matching Geotagged Images;
2.4 Exercises;
Chapter 3: Image to Image Mappings;
3.1 3.1 Homographies;
3.2 3.2 Warping Images;
3.3 3.3 Creating Panoramas;
3.4 Exercises;
Chapter 4: Camera Models and Augmented Reality;
4.1 4.1 The Pin-Hole Camera Model;
4.2 4.2 Camera Calibration;
4.3 4.3 Pose Estimation from Planes and Markers;
4.4 4.4 Augmented Reality;
4.5 Exercises;
Chapter 5: Multiple View Geometry;
5.1 5.1 Epipolar Geometry;
5.2 5.2 Computing with Cameras and 3D Structure;
5.3 5.3 Multiple View Reconstruction;
5.4 5.4 Stereo Images;
5.5 Exercises;
Chapter 6: Clustering Images;
6.1 6.1 K-Means Clustering;
6.2 6.2 Hierarchical Clustering;
6.3 6.3 Spectral Clustering;
6.4 Exercises;
Chapter 7: Searching Images;
7.1 7.1 Content-Based Image Retrieval;
7.2 7.2 Visual Words;
7.3 7.3 Indexing Images;
7.4 7.4 Searching the Database for Images;
7.5 7.5 Ranking Results Using Geometry;
7.6 7.6 Building Demos and Web Applications;
7.7 Exercises;
Chapter 8: Classifying Image Content;
8.1 8.1 K-Nearest Neighbors;
8.2 8.2 Bayes Classifier;
8.3 8.3 Support Vector Machines;
8.4 8.4 Optical Character Recognition;
8.5 Exercises;
Chapter 9: Image Segmentation;
9.1 9.1 Graph Cuts;
9.2 9.2 Segmentation Using Clustering;
9.3 9.3 Variational Methods;
9.4 Exercises;
Chapter 10: OpenCV;
10.1 10.1 The OpenCV Python Interface;
10.2 10.2 OpenCV Basics;
10.3 10.3 Processing Video;
10.4 10.4 Tracking;
10.5 10.5 More Examples;
10.6 Exercises;
Installing Packages;
A.1 NumPy and SciPy;
A.2 Matplotlib;
A.3 PIL;
A.4 LibSVM;
A.5 OpenCV;
A.6 VLFeat;
A.7 PyGame;
A.8 PyOpenGL;
A.9 Pydot;
A.10 Python-graph;
A.11 Simplejson;
A.12 PySQLite;
A.13 CherryPy;
Image Datasets;
B.1 Flickr;
B.2 Panoramio;
B.3 Oxford Visual Geometry Group;
B.4 University of Kentucky Recognition Benchmark Images;
B.5 Other;
Image Credits;
C.1 Images from Flickr;
C.2 Other Images;
C.3 Illustrations;
References;
About the Author;
Colophon;

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