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OpenCV Computer Vision with Java

By : Baggio

3.4 (5)

OpenCV Computer Vision with Java

3.4 (5)

By: Baggio

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Setting Up OpenCV for Java

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

2. Handling Matrices, Files, Cameras, and GUIs

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

3. Image Filters and Morphological Operators

3. Image Filters and Morphological Operators

Smoothing

Morphological operators

Flood filling

Image pyramids

Thresholding

Summary

4. Image Transforms

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

5. Object Detection Using Ada Boost and Haar Cascades

5. Object Detection Using Ada Boost and Haar Cascades

The boosting theory

Cascade classifier detection and training

Detection

Training

References

Summary

6. Detecting Foreground and Background Regions and Depth with a Kinect Device

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

7. OpenCV on the Server Side

7. OpenCV on the Server Side

Setting up an OpenCV web application

Mixed reality web applications

Image processing

Summary

Index

Index

Basic matrix manipulation

From a computer vision background, we can see an image as a matrix of numerical values, which represents its pixels. For a gray-level image, we usually assign values ranging from 0 (black) to 255 (white) and the numbers in between show a mixture of both. These are generally 8-bit images. So, each element of the matrix refers to each pixel on the gray-level image, the number of columns refers to the image width, as well as the number of rows refers to the image's height. In order to represent a color image, we usually adopt each pixel as a combination of three basic colors: red, green, and blue. So, each pixel in the matrix is represented by a triplet of colors.

Note

It is important to observe that with 8 bits, we get 2 to the power of eight (2⁸), which is 256. So, we can represent the range from 0 to 255, which includes, respectively the values used for black and white levels in 8-bit grayscale images. Besides this, we can also represent these levels as floating...

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Programming languages

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