Book Image

Hands-On Image Processing with Python

By : Sandipan Dey
Book Image

Hands-On Image Processing with Python

By: Sandipan Dey

Overview of this book

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

What is image segmentation?


Image segmentation is the partitioning of an image into distinct regions or categories that correspond to different objects or parts of objects. Each region contains pixels with similar attributes, and each pixel in an image is allocated to one of these categories. A good segmentation is typically one in which pixels in the same category have similar intensity values and form a connected region, whereas the neighboring pixels that are in different categories have dissimilar values. The goal of this is to simplify/change the representation of an image into something more meaningful and easier to analyze. 

If segmentation is done well, then all other stages in image analysis are made simpler. Hence, the quality and reliability of segmentation dictates whether an image analysis will be successful. But to partition an image into correct segments is often a very challenging problem.

Segmentation techniques can be either non-contextual (do not consider spatial relationships...