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)

Morphological pattern matching

In this recipe, you will learn how to use the morphological compound operation, hit-or-miss-transform, to find patterns from a binary image. Hit-or-miss transform is a morphological operation that is used to detect a given pattern in a binary image. It uses a pair of disjointed structuring elements to define the pattern to be matched and the morphological erosion operator to implement the pattern matching. The hit-or-miss transform returns a binary image as output in which only those sets of positions are non-zero where the first SE matches and the second SE completely misses the foreground, respectively, in the input binary image.

Getting ready

In this recipe, we will use an image of a script...