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

Active contours, morphological snakes, and GrabCut algorithms


In this section, we will discuss some more sophisticated segmentation algorithms and demonstrate them with scikit-image or python-opencv (cv2) library functions. We will start with segmentation using the active contours. 

Active contours

The active contour model (also known as snakes) is a framework that fits open or closed splines to lines or edges in an image. A snake is an energy-minimizing, deformable spline influenced by constraint, image, and internal forces. Hence, it works by minimizing an energy that is partially defined by the image and partially by the spline's shape, length, and smoothness. The constraint and image forces pull the snake toward object contours and internal forces resist the deformation. The algorithm accepts an initial snake (around the object of interest) and to fit the closed contour to the object of interest, it shrinks/expands. The minimization is done explicitly in the image energy and implicitly...