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As the Kinect device has become a great tool for background segmentation, we have
covered it in this chapter as well.
Another hot topic that is commonly explored with computer vision is machine
learning, and in this book, you will nd useful information to create your own object
tracker and to use OpenCV's built-in face tracker as well.
Since Java has been widely used for web applications, we have covered computer
vision applications on the server side as well, explaining the details of image
uploading and integration with OpenCV.
By the end of this book, you will have a solid background in how to use Java with
OpenCV from setup to server side; a brief explanation of the basic computer vision
topics are covered in the book. Also, you'll get the source code of several complete
projects from which you can extend and add your own functionality.
What this book covers
Chapter 1, Setting Up OpenCV for Java, covers the setting up of a library and
development environment. This chapter covers Eclipse and NetBeans IDEs,
as well as explaining the Ant and Maven build tools conguration.
Chapter 2, Handling Matrices, Files, Cameras, and GUIs, shows how to access matrices at
the pixel level as well as how to load and display images from les and web cameras.
It also covers the Swing widget toolkit support and how to work with OpenCV.
Chapter 3, Image Filters and Morphological Operators, deals with the process of
removing noise from images as well as morphological operators. It also explains
image pyramids and topics such as ood ll and image thresholding.
Chapter 4, Image Transforms, explains important transformations to nd edges,
such as the Gradient and Sobel lters. Additionally, it also explains line and circle
Hough transforms, which are used to identify not only straight but also radial lines.
The Discrete Fourier analysis and some distance transforms are also explained in
this chapter.
Chapter 5, Object Detection Using Ada Boost and Haar Cascades, demonstrates how to
create your own classier to nd some objects, as well as how to use the famous
face detection classier.
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