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OpenCV Computer Vision with Java

You're reading from   OpenCV Computer Vision with Java Create multiplatform computer vision desktop and web applications using the combination of OpenCV and Java

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783283972
Length 174 pages
Edition 1st Edition
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Author (1):
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Daniel Lelis Baggio Daniel Lelis Baggio
Author Profile Icon Daniel Lelis Baggio
Daniel Lelis Baggio
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Table of Contents (9) Chapters Close

Preface 1. Setting Up OpenCV for Java FREE CHAPTER 2. Handling Matrices, Files, Cameras, and GUIs 3. Image Filters and Morphological Operators 4. Image Transforms 5. Object Detection Using Ada Boost and Haar Cascades 6. Detecting Foreground and Background Regions and Depth with a Kinect Device 7. OpenCV on the Server Side Index

The Laplace and Canny transforms

Another quite useful operator to find edges is the Laplacian transformation. Instead of relying on the first order derivatives, OpenCV's Laplacian transformation implements the discrete operator for the following function:

The Laplace and Canny transforms

The matrix can be approximated to the convolution with the following kernel when using finite difference methods and a 3x3 aperture:

The Laplace and Canny transforms

The signature for the preceding function is as follows:

Laplacian(Mat source, Mat destination, int ddepth)

While source and destination matrices are simple parameters, ddepth is the depth of the destination matrix. When you set this parameter to -1, it will have the same depth as the source image, although you might want more depth when you apply this operator. Besides this, there are overloaded versions of this method that receive an aperture size, a scale factor, and an adding scalar.

Besides using the Laplacian method, you can also use the Canny algorithm, which is an excellent approach that was proposed...

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