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 with self-organizing maps

A self-organizing map (SOM) is a competitive learning network (an interesting class of unsupervised machine learning), and it is one of the most popular neural network models. In this network, only one neuron gets activated at a given time, so the output neurons compete among themselves to be activated. This activated neuron is called the winning neuron. When one neuron fires, its closest neighbors tend to get more excited than ones that are further away (defining a topological neighborhood with decaying distance). As a result, the neurons are forced to organize themselves (through an adaptive or learning process) and a feature map between inputs and outputs is created. That's why this network is known as a self-organizing map.

The adaptive process of the SOM algorithm takes place in the following two steps:

  1. Ordering (self-organizing...