Book Image

Python Image Processing Cookbook

By : Sandipan Dey
Book Image

Python Image Processing Cookbook

By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)

Image Registration

Image registration refers to an image processing task where the objective is to align a target image with a source image. In more general terms, it aims to compute the spatial transform (function) that maps (some) points from one image to the corresponding points in the other image. Often, finding a transformation from one image to another is referred to as alignment and then actually performing the image warping procedure using the estimated transform is called registration. There are three general philosophies to compare to determine alignment:

  • Intensity-based (compare actual pixel values from one image to another, for example, with mutual information)
  • Segmentation-based (register the binary segmentation)
  • Landmark (or feature)-based (mark key points in both images and derive a transform that makes every pair of landmarks match)

The transformations that...