Jan Erik Solem

Programming Computer Vision with Python

Tools and Algorithms for Analyzing Images

• Broschiertes Buch

Produktbeschreibung

If you want a basic understanding of computer vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. You'll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Programming Computer Vision with Python explains computer vision in broad terms that won't bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you've learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 260
• Erscheinungstermin: 24. Juli 2012
• Englisch
• Abmessung: 231mm x 179mm x 17mm
• Gewicht: 436g
• ISBN-13: 9781449316549
• ISBN-10: 1449316549
• Artikelnr.: 35117150

Autorenporträt

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.

Inhaltsangabe

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