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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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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science FREE CHAPTER 2. Data Acquisition 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

Cleaning images

While image processing is a complex task, we will introduce a few techniques to clean and extract information from an image. This will provide the reader with some insight into image processing. We will also demonstrate how to extract text data from an image using Optical Character Recognition (OCR).

There are several techniques used to improve the quality of an image. Many of these require tweaking of parameters to get the improvement desired. We will demonstrate how to:

  • Enhance an image's contrast
  • Smooth an image
  • Brighten an image
  • Resize an image
  • Convert images to different formats

We will use OpenCV (http://opencv.org/), an open source project for image processing. There are several classes that we will use:

  • Mat: This represents an n-dimensional array holding image data such as channel, grayscale, or color values
  • Imgproc: Possesses many methods that process an image
  • Imgcodecs: Possesses methods to read and write image files

The OpenCV Javadocs is found at http://docs.opencv...

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