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

Felzenszwalb, SLIC, QuickShift, and Compact Watershed algorithms 


In this section, we will discuss four popular low-level image segmentation methods and then compare the results obtained by those methods with an input image. The definition of good segmentation often depends on the application, and thus it is difficult to obtain a good segmentation. These methods are generally used for obtaining an over-segmentation, also known as superpixels. These superpixels then serve as a basis for more sophisticated algorithms such as merging with a region adjacency graph or conditional random fields.

Felzenszwalb's efficient graph-based image segmentation

Felzenszwalb's algorithm takes a graph-based approach to segmentation. It first constructs an undirected graph with the image pixels as vertices (the set to be segmented) and the weight of an edge between the two vertices being some measure of the dissimilarity (for example, the difference in intensity). In the graph-based approach, the problem of partitioning...