OpenCV 3.0 Computer Vision with Java

Create multiplatform computer vision desktop and web applications using the combination of OpenCV and Java

OpenCV 3.0 Computer Vision with Java

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Daniel Lélis Baggio

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Create multiplatform computer vision desktop and web applications using the combination of OpenCV and Java
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Book Details

ISBN 139781783283972
Paperback174 pages

Book Description

OpenCV 3.0 Computer Vision with Java is a practical tutorial guide that explains fundamental tasks from computer vision while focusing on Java development. This book will teach you how to set up OpenCV for Java and handle matrices using the basic operations of image processing such as filtering and image transforms. It will also help you learn how to use Haar cascades for tracking faces and to detect foreground and background regions with the help of a Kinect device. It will even give you insights into server-side OpenCV. Each chapter is presented with several projects that are ready to use. The functionality of these projects is found in many classes that allow developers to understand computer vision principles and rapidly extend or customize the projects for their needs.

Table of Contents

Chapter 1: Setting Up OpenCV for Java
Getting OpenCV for Java development
Building OpenCV from the source code
The Java OpenCV project in Eclipse
The NetBeans configuration
A Java OpenCV simple application
Building your project with Ant
The Java OpenCV Maven configuration
Summary
Chapter 2: Handling Matrices, Files, Cameras, and GUIs
Basic matrix manipulation
Pixel manipulation
Loading and displaying images from files
Displaying an image with Swing
Capturing a video from a camera
Video playback
Swing GUI's integration with OpenCV
Summary
Chapter 3: Image Filters and Morphological Operators
Smoothing
Morphological operators
Flood filling
Image pyramids
Thresholding
Summary
Chapter 4: Image Transforms
The Gradient and Sobel derivatives
The Laplace and Canny transforms
The line and circle Hough transforms
Geometric transforms – stretch, shrink, warp, and rotate
Discrete Fourier Transform and Discrete Cosine Transform
Integral images
Distance transforms
Histogram equalization
References
Summary
Chapter 5: Object Detection Using Ada Boost and Haar Cascades
The boosting theory
Cascade classifier detection and training
Detection
Training
References
Summary
Chapter 6: Detecting Foreground and Background Regions and Depth with a Kinect Device
Background subtraction
Frame differencing
Averaging a background method
The mixture of Gaussians method
Contour finding
Kinect depth maps
Summary
Chapter 7: OpenCV on the Server Side
Setting up an OpenCV web application
Mixed reality web applications
Image processing
Summary

What You Will Learn

  • Create powerful GUIs for computer vision applications with panels, scroll panes, radio buttons, sliders, windows, and mouse interaction using the popular Swing GUI widget toolkit
  • Stretch, shrink, warp, and rotate images, as well as apply image transforms to find edges, lines, and circles, and even use Discrete Fourier Transforms (DFT)
  • Detect foreground or background regions and work with depth images with a Kinect device
  • Learn how to add computer vision capabilities to rock solid Java web applications allowing you to upload photos and create astonishing effects
  • Track faces and apply mixed reality effects such as adding virtual hats to uploaded photos
  • Filter noisy images, work with morphological operators, use flood fill, and threshold the important regions of an image
  • Open and process video streams from webcams or video files

Authors

Table of Contents

Chapter 1: Setting Up OpenCV for Java
Getting OpenCV for Java development
Building OpenCV from the source code
The Java OpenCV project in Eclipse
The NetBeans configuration
A Java OpenCV simple application
Building your project with Ant
The Java OpenCV Maven configuration
Summary
Chapter 2: Handling Matrices, Files, Cameras, and GUIs
Basic matrix manipulation
Pixel manipulation
Loading and displaying images from files
Displaying an image with Swing
Capturing a video from a camera
Video playback
Swing GUI's integration with OpenCV
Summary
Chapter 3: Image Filters and Morphological Operators
Smoothing
Morphological operators
Flood filling
Image pyramids
Thresholding
Summary
Chapter 4: Image Transforms
The Gradient and Sobel derivatives
The Laplace and Canny transforms
The line and circle Hough transforms
Geometric transforms – stretch, shrink, warp, and rotate
Discrete Fourier Transform and Discrete Cosine Transform
Integral images
Distance transforms
Histogram equalization
References
Summary
Chapter 5: Object Detection Using Ada Boost and Haar Cascades
The boosting theory
Cascade classifier detection and training
Detection
Training
References
Summary
Chapter 6: Detecting Foreground and Background Regions and Depth with a Kinect Device
Background subtraction
Frame differencing
Averaging a background method
The mixture of Gaussians method
Contour finding
Kinect depth maps
Summary
Chapter 7: OpenCV on the Server Side
Setting up an OpenCV web application
Mixed reality web applications
Image processing
Summary

Book Details

ISBN 139781783283972
Paperback174 pages
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