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

Questions


  1. Use Hough transform to detect ellipses from an image with ellipses with scikit-image.
  2. Use scikit-image transform module's probabilistic_hough_line() function to detect lines from images. How is it different than the hough_line()?
  3. Use scikit-image filter module's try_all_threshold() function to compare different types of local thresholding techniques to segment a gray-scale image into a binary image.
  4. Use the ConfidenceConnected and VectorConfidenceConnected algorithms for the MRI-scan image segmentation using SimpleITK.
  5. Use the correct bounding rectangle around the foreground object to segment the whale image with the GrabCut algorithm.
  6. Use scikit-image segmentation module's random_walker() function to segment an image starting from a few marked locations defined by markers.