Dear All,
Indian Edition Released kindly place your order.
Programming Computer Vision with Python
Tools and algorithms for analyzing images
ISBN: 9789350237663
Paperback
Pages: 280
Price: Rs 425.00
| Size: 7 X 9 |
| Shroff/O'Reilly (2012) |
| Arrival Date: July 17, 2012 |
Description
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.
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
Chapter 1 Basic Image Handling and Processing
1.1 PIL—The Python Imaging Library
1.2 Matplotlib
1.3 NumPy
1.4 SciPy
1.5 Advanced Example: Image De-Noising
Exercises
Conventions for the Code Examples
Chapter 2 Local Image Descriptors
2.1 Harris Corner Detector
2.2 SIFT—Scale-Invariant Feature Transform
2.3 Matching Geotagged Images
Exercises
Chapter 3 Image to Image Mappings
3.1 Homographies
3.2 Warping Images
3.3 Creating Panoramas
Exercises
Chapter 4 Camera Models and Augmented Reality
4.1 The Pin-Hole Camera Model
4.2 Camera Calibration
4.3 Pose Estimation from Planes and Markers
4.4 Augmented Reality
Exercises
Chapter 5 Multiple View Geometry
5.1 Epipolar Geometry
5.2 Computing with Cameras and 3D Structure
5.3 Multiple View Reconstruction
5.4 Stereo Images
Exercises
Chapter 6 Clustering Images
6.1 K-Means Clustering
6.2 Hierarchical Clustering
6.3 Spectral Clustering
Exercises
Chapter 7 Searching Images
7.1 Content-Based Image Retrieval
7.2 Visual Words
7.3 Indexing Images
7.4 Searching the Database for Images
7.5 Ranking Results Using Geometry
7.6 Building Demos and Web Applications
Exercises
Chapter 8 Classifying Image Content
8.1 K-Nearest Neighbors
8.2 Bayes Classifier
8.3 Support Vector Machines
8.4 Optical Character Recognition
Exercises
Chapter 9 Image Segmentation
9.1 Graph Cuts
9.2 Segmentation Using Clustering
9.3 Variational Methods
Exercises
Chapter 10 OpenCV
10.1 The OpenCV Python Interface
10.2 OpenCV Basics
10.3 Processing Video
10.4 Tracking
10.5 More Examples
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