Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Length 492 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
Arrow right icon
View More author details
Toc

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

Point-wise intensity transformations – pixel transformation


As discussed in the Chapter 1Getting Started with Image Processing, the point-wise intensity transformation operation applies a transfer function, T, to each pixel, f(x,y), of the input image to generate a corresponding pixel in the output image. The transformation can be expressed as g(x,y) = T(f(x,y)) or, equivalently, s = T(r), where r is the gray-level of a pixel in the input image and s is the transformed gray-level of the same pixel in the output image. It's a memory-less operation, and the output intensity at the location,(x, y), depends only on the input intensity at the same point. Pixels of the same intensity get the same transformation. This does not bring in new information and may cause loss of information, but can improve the visual appearance or make features easier to detect—that is why these transformations are often applied at the pre-processing step in the image processing pipeline. The following screenshot...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image