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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 2. Sampling, Fourier Transform, and Convolution FREE CHAPTER 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

Questions


  1. Use Hough transform to detect ellipses from an image with ellipses with scikit-image.
  2. Use scikit-image transform module's probabilistic_hough_line() function to detect lines from images. How is it different than the hough_line()?
  3. Use scikit-image filter module's try_all_threshold() function to compare different types of local thresholding techniques to segment a gray-scale image into a binary image.
  4. Use the ConfidenceConnected and VectorConfidenceConnected algorithms for the MRI-scan image segmentation using SimpleITK.
  5. Use the correct bounding rectangle around the foreground object to segment the whale image with the GrabCut algorithm.
  6. Use scikit-image segmentation module's random_walker() function to segment an image starting from a few marked locations defined by markers.
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