Programming Computer Vision with Python

Today, images and video are everywhere. Online photo sharing sites and social networks have them in the billions. Search engines will produce images of just about any
conceivable query. Practically all phones and computers come with built in cameras.
It is not uncommon for people to have many gigabytes of photos and videos on their
devices.
Programming a computer and designing algorithms for understanding what is in
these images is the field of computer vision. Computer vision powers applications like
image search, robot navigation, medical image analysis, photo management and many
more.
The idea behind this book is to give an easily accessible entry point to hands-on
computer vision with enough understanding of the underlying theory and algorithms
to be a foundation for students, researchers and enthusiasts. The Python programming
language, the language choice of this book, comes with many freely available powerful
modules for handling images, mathematical computing and data mining.
When writing this book I have had the following principles as a guideline. The book
should:
• be written in an exploratory style. Encourage readers to follow the examples on
their computers as they are reading the text.
• promote and use free and open software with a low learning threshold. Python
was the obvious choice.
• be complete and self-contained. Not complete as in covering all of computer vision (this book is far from that!) but rather complete in that all code is presented
and explained. The reader should be able to reproduce the examples and build
upon them directly.
• be broad rather than detailed, inspiring and motivational rather than theoretical.
In short: act as a source of inspiration for those interested in programming computer
vision applications.

Chapter 1- Introduces the basic tools for working with images and the central Python
modules used in the book. This chapter also covers many fundamental examples
needed for the remaining chapters.
Chapter 2- Explains methods for detecting interest points in images and how to use
them to find corresponding points and regions between images.
Chapter 3 -Describes basic transformations between images and methods for computing them. Examples range from image warping to creating panoramas.
Chapter 4- Introduces how to model cameras, generate image projections from 3D
space to image features and estimate the camera viewpoint.
Chapter 5 Explains how to work with several images of the same scene, the fundamentals of multiple-view geometry and how to compute 3D reconstructions from
images.
Chapter 6 Introduces a number of clustering methods and shows how to use them
for grouping and organizing images based on similarity or content.
Chapter 7 Shows how to build efficient image retrieval techniques that can store
image representations and search for images based on their visual content.
Chapter 8 Describes algorithms for classifying image content and how to use them
recognizing objects in images.
Chapter 9 Introduces different techniques for dividing an image into meaningful
regions using clustering, user interactions or image models.
Chapter 10 Shows how to use the Python interface for the commonly used OpenCV
computer vision library and how to work with video and camera input.