Programming Computer Vision with Python covers the foundations of computer vision along with numerous interesting and practical examples.
Topics covered include:
- Basic image handling and processing in Python
- Image descriptors and points of interest
- Image mapping and homographies
- Augmented reality
- 3D scene reconstruction
- Clustering, searching and classifying images
- Image segmentation and
- Interfacing to OpenCV
Python forms an integral part of this book and is used throughout the book. It is an ideal language for this purpose, being easy to understand and with excellent libraries. The scientific library numpy is used extensively.
This book is well-written, easy to understand and a lot of fun. There was a heavy emphasis on practicality which I appreciated.
Typically the theory would first be explained in text, then implemented in code. Finally a practical example would demonstrate how to apply the theory.
There are some great examples. This is where the book shines.
For instance, in the chapter on image classification, we are given an image of a Sudoku. The grid location is first determined, then the contents of each cell in the grid are classified into digits.
It was great to see some machine learning algorithms applied to real problems. PCA (Principal Component Analysis) is used extensively. SVM (Support Vector Machines) and the Naive Bayes Classifier are also used to solve real computer vision problems.
The author clearly knows his stuff, often pointing out pitfalls and demonstrating many handy tricks.
A very enjoyable and recommended introduction to the world of computer vision.
Note: This book was provided by O’Reilly Media as part of their blogger review program.