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

Scale-invariant feature transform


Scale-invariant feature transform (SIFT) descriptors provide an alternative representation for image regions. They are very useful for matching images. As demonstrated earlier, simple corner detectors work well when the images to be matched are similar in nature (with respect to scale, orientation, and so on). But if they have different scales and rotations, the SIFT descriptors are needed to be used to match them. SIFT is not only just scale invariant, but it still obtains good results when rotation, illumination, and viewpoints of the images change as well. 

 

 

Let's discuss the primary steps involved in the SIFT algorithm that transforms image content into local feature coordinates that are invariant to translation, rotation, scale, and other imaging parameters.

Algorithm to compute SIFT descriptors

  • Scale-space extrema detection: Search over multiple scales and image locations, the location and characteristic scales are given by DoG detector
  • Keypoint localization...