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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Identifying faces

Identifying faces within an image is useful in many situations. It can potentially classify an image as one containing people or find people in an image for further processing. We will use OpenCV 3.1 (http://opencv.org/opencv-3-1.html) for our examples.

OpenCV (http://opencv.org/) is an open source computer vision library that supports several programming languages, including Java. It supports a number of techniques, including machine learning algorithms, to perform computer vision tasks. The library supports such operations as face detection, tracking camera movements, extracting 3D models, and removing red eye from images. In this section, we will demonstrate face detection.

Using OpenCV to detect faces

The example that follows was adapted from http://docs.opencv.org/trunk/d9/d52/tutorial_java_dev_intro.html. Start by loading the native libraries added to your system when OpenCV was installed. On Windows, this requires that appropriate DLL files are available:

System.loadLibrary...
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