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 Segmentation

Image segmentation refers to the partitioning of an image into distinct regions or categories, with each region containing pixels with similar attributes and each pixel in an image being allocated to one of these categories.

Image segmentation is usually done to simplify the representation of an image into segments that are more meaningful and easier to analyze. If segmentation is done well, then all other stages in image analysis are made simpler, which means that the quality and reliability of segmentation dictates whether the analysis of an image will be successful. But to partition an image into correct segments is often a very challenging problem.

In this chapter, we will look at the following recipes:

  • Thresholding with Otsu and Riddler–Calvard
  • Image segmentation with self-organizing maps
  • RandomWalk segmentation with scikit-image
  • Skin color segmentation...