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

Haar-like features


Haar-like features are very useful image features used in object detection. They were introduced in the first real-time face detector by Viola and Jones. Using integral images, Haar-like features of any size (scale) can be efficiently computed in constant time. The computation speed is the key advantage of a Haar-like feature over most other features. These features are just like the convolution kernels (rectangle filters) introduced in Chapter 3, Convolution and Frequency Domain Filtering. Each feature corresponds to a single value computed by subtracting a sum of pixels under a white rectangle from a sum of pixels under a black rectangle. The next diagram shows different types of Haar-like features, along with the important Haar-like features for face detection:

The first and the second important feature for face detection shown here seems to focus on the fact that the region of the eyes is often darker than the region of the nose and cheeks, and that the eyes are darker...