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Hands-On Image Processing with Python

You're reading from   Hands-On Image Processing with Python Expert techniques for advanced image analysis and effective interpretation of image data

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Length 492 pages
Edition 1st Edition
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Author (1):
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Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
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Table of Contents (20) Chapters Close

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing FREE CHAPTER 2. Sampling, Fourier Transform, and Convolution 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. Other Books You May Enjoy Index

Chapter 9. Classical Machine Learning Methods in Image Processing

In this chapter, we will discuss the application of machine learning techniques in image processing. We will define machine learning and learn about two of its algorithms, supervised and unsupervised. Then, we will continue our discussion on the application of a few popular unsupervised machine learning techniques, such as clustering, and problems such as image segmentation.

We will also be looking at applications of supervised machine learning techniques for problems such as image classification and object detection. We will be using a very popular library, scikit-learn, along with scikit-image and Python-OpenCV (cv2) to implement machine learning algorithms for image processing. This chapter is going to give you insight into machine learning algorithms and the problems they solve.

 The topics to be covered in this chapter are as follows:

  • Supervised versus unsupervised learning 
  • Unsupervised machine learning—clustering, PCA,...
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