Preface
Living in times when self-driving vehicles are becoming a reality might trigger curious minds as to how could computers' incipient vision works. Having a face recognized for access control, getting our pictures automatically organized by a subject or person, and having characters automatically recognized from paper scans are tasks that have become common in our lives. All these aforementioned actions have been enlisted in the so-called study area of computer vision.
As a scientific discipline, the theory behind systems that can extract information from images can be described as computer vision, and it has been adopted to extract valuable measurements from medical images, as well as to help humans delineate the boundaries of important image areas in the so-called semi-automatic procedures.
In the context of providing a simple-to-use computer vision infrastructure to help people rapidly build sophisticated vision applications, an open source library was created: OpenCV. It was designed for real-time applications and is written in C++, containing several hundred computer vision algorithms.
Although OpenCV had its debut alpha release back in January 1999, it was only in February 2013 that it officially supported desktop Java through bindings. As this is one of the most popular introductory teaching languages adopted in computer science departments as well as K-12 computer-related courses, it is important to have a good reference for how to build vision apps in a Java environment.
This book covers the basic OpenCV computer vision algorithms and their integration with Java. As the Swing GUI widget toolkit is widely adopted to build GUIs in Java, in this book, you will benefit from the chapters that deal with this topic as well as come to know how to set up your development environment that deals with native code bindings. Besides, operations such as stretching, shrinking, warping, and rotating, as well as finding edges, lines, and circles are all covered through interesting and practical sample projects in this book.
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 find 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.
