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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

Questions


The following are the questions:

  1. Implement down-sampling with anti-aliasing using the Gaussian LPF (hint: reduce the house grayscale image four times, first by applying a Gaussian filter and then by filtering every other row and column. Compare the output images with and without pre-processing with LPF before down-sampling). 
  2. Use the FFT to up-sample an image: first double the size of the lena grayscale image by padding zero rows/columns at every alternate positions, then use the FFT followed by an LPF and then by the IFFT to get the output image. Why does it work?
  3. Try to apply the Fourier transform and image reconstruction with a color (RGB) image. (Hint: apply the FFT for each channel separately).
  4. Show (mathematically and with a 2D kernel example) that the Fourier transform of a Gaussian kernel is another Gaussian kernel.
  1. Use the lena image and the asymmetric ripple kernel to generate images with correlation and convolution. Show that output images are different. Now, flip the kernel...
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