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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

Image quilting


The image quilting algorithm is an algorithm used for texture synthesis and transfer in images, described in the SIGGRAPH 2001 paper by Efros and Freeman. In this section, we shall touch upon the main idea behind the quilting algorithms for implementing texture synthesis and transfer, and show a couple of results obtained with an implementation of the algorithm. The code is left for the reader to implement (refer to https://sandipanweb.wordpress.com/2017/10/24/some-computational-photography-image-quilting-texture-synthesis-with-dynamic-programming-and-texture-transfer-in-python/ for more information).

Texture synthesis

Texture synthesis refers to the creation of a larger texture image from a small sample. For texture synthesis, the main idea is to sample patches and lay them down in overlapping patterns, such that the overlapping regions are similar. The overlapping regions may not match exactly, which will result in noticeable artifacts around the edges. To fix this, we need...

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