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  • Book Overview & Buying OpenCV Computer Vision with Java
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OpenCV Computer Vision with Java

OpenCV Computer Vision with Java

By : Baggio
3.4 (5)
Buy this Book
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OpenCV Computer Vision with Java

OpenCV Computer Vision with Java

3.4 (5)
By: Baggio
Buy this Book

Overview of this book

If you are a Java developer, student, researcher, or hobbyist wanting to create computer vision applications in Java then this book is for you. If you are an experienced C/C++ developer who is used to working with OpenCV, you will also find this book very useful for migrating your applications to Java. All you need is basic knowledge of Java, with no prior understanding of computer vision required, as this book will give you clear explanations and examples of the basics.
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Setting Up OpenCV for Java
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1. Setting Up OpenCV for Java
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Getting OpenCV for Java development
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Building OpenCV from the source code
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The Java OpenCV project in Eclipse
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The NetBeans configuration
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A Java OpenCV simple application
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Building your project with Ant
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The Java OpenCV Maven configuration
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Summary
2
2. Handling Matrices, Files, Cameras, and GUIs
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2. Handling Matrices, Files, Cameras, and GUIs
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Basic matrix manipulation
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Pixel manipulation
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Loading and displaying images from files
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Displaying an image with Swing
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Capturing a video from a camera
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Video playback
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Swing GUI's integration with OpenCV
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Summary
3
3. Image Filters and Morphological Operators
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3. Image Filters and Morphological Operators
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Smoothing
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Morphological operators
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Flood filling
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Image pyramids
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Thresholding
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Summary
4
4. Image Transforms
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4. Image Transforms
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The Gradient and Sobel derivatives
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The Laplace and Canny transforms
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The line and circle Hough transforms
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Geometric transforms – stretch, shrink, warp, and rotate
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Discrete Fourier Transform and Discrete Cosine Transform
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Integral images
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Distance transforms
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Histogram equalization
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References
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Summary
5
5. Object Detection Using Ada Boost and Haar Cascades
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5. Object Detection Using Ada Boost and Haar Cascades
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The boosting theory
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Cascade classifier detection and training
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Detection
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Training
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References
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Summary
6
6. Detecting Foreground and Background Regions and Depth with a Kinect Device
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6. Detecting Foreground and Background Regions and Depth with a Kinect Device
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Background subtraction
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Frame differencing
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Averaging a background method
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The mixture of Gaussians method
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Contour finding
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Kinect depth maps
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Summary
7
7. OpenCV on the Server Side
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7. OpenCV on the Server Side
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Setting up an OpenCV web application
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Mixed reality web applications
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Image processing
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Summary
8
Index
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Index

Chapter 5. Object Detection Using Ada Boost and Haar Cascades

This chapter shows a very interesting feature of OpenCV—detecting faces in an image or a video stream. In the latter case, we call it face tracking. In order to do so, this chapter dives into machine-learning algorithms, specifically supervised learning with boosting. We will cover the Viola-Jones classifier and its theory as well as the details on how to use the face-trained classifiers that are bundled with OpenCV.

In this chapter, we will be covering the following topics:

  • The boosting theory
  • Viola-Jones classifier
  • Detecting faces
  • Learning new objects

By the end of this chapter, you will be able to understand the theory behind face classifiers through boosting, and the Viola-Jones classifier. You will also know how to use straightforward face classifiers. Besides, you will be able to create your own object classifier for different objects.

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Tech Concepts
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Programming languages
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OpenCV Computer Vision with Java
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