3. Image Filters and Morphological Operators | OpenCV Computer Vision with Java

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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

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Smoothing

Just like in one-dimensional signals, we are always susceptible to receiving some noise in our images and we generally apply some preprocessing filters to them before we perform our main work on the images. We can consider noise as a random variation of color or brightness information that is not present in the imaged object, which can take place undesirably due to a sensor and circuitry of a digital camera or scanner. This section uses the ideas of low-pass filter kernels to smoothen our images. These filters remove high frequency content, such as edges and noises, although some techniques allow edges not to be blurred. We will cover the four main image filters available in OpenCV: averaging, Gaussian, median filtering, and bilateral filtering.

Note

2D Kernel Convolution is a form of mathematical convolution. An output image is calculated by sweeping each of the pixels of a given image and applying a kernel operator to them, yielding an output pixel for each resulting operation...

Visually different images

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Tech Concepts

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Programming languages

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