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

Summary


In this chapter, we discussed image segmentation and demonstrated different algorithms with Python libraries such as scikit-image, opencv (cv2) and SimpleITK. We started with line and circle detection in an image with Hough transform and also showed an example of how it can be used for image segmentation. Next, we discussed Otsu's thresholding algorithm to find the optimal threshold for segmentation. Then edge-based and region-based segmentation algorithms were demonstrated along with the morphological watershed algorithm for image segmentation. In the next section, some more segmentation algorithms such as Felzenszwalb's graph-based algorithm, region growing, SLIC, and QuickShift were discussed, along with the implementations using scikit-image. Finally, we discussed some more sophisticated segmentation algorithms, such as GrabCut, active contours, and morphological snakes. 

In the next chapter, we shall discuss machine learning techniques in image processing, and we will discuss...