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

Chapter 7. Extracting Image Features and Descriptors

In this chapter, we will discuss feature detectors and descriptors, along with various applications of different types of feature detectors/extractors in image processing. We will start by defining feature detectors and descriptors. We will then continue our discussion on a few popular feature detectors such as Harris Corner/SIFT and HOG, and then their applications in important image processing problems such as image matching and object detection, respectively, with scikit-image and python-opencv (cv2) library functions.

 The topics to be covered in this chapter are as follows:

  • Feature detectors versus descriptors, to extract features/descriptors from images
  • Harris Corner Detector and the application of Harris Corner features in image matching (with scikit-image)
  • Blob detectors with LoG, DoG, and DoH (with scikit-image)
  • Extraction of Histogram of Oriented Gradients features
  • SIFT, ORB, and BRIEF features and their application in image matching...