Saurabh Kapur - Computer Vision with Python 3

BIRMINGHAM - MUMBAI

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First published: August 2017

Production reference: 1210817

ISBN 978-1-78829-976-3

www.packtpub.com

Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi.

His interests are in computer vision, numerical analysis, and algorithm design. He often spends time solving competitive programming questions. Saurabh also enjoys working on IoT applications and tinkering with hardware.

He likes to spend his free time playing or watching cricket. He can be reached at saurabhkapur96@gmail.com.

Will Brennan is a C++ and Python developer based in London, with experience of working on high-performance image processing and machine learning applications.

You can read more about Will at https://github.com//WillBrennan.

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Computer vision has gone through significant advancements over the last few years. These advancements have not only made a great impact of computer science but also of other fields, such as medicine, space exploration, and defense. From microscopic organisms to celestial particles light-years away, using computer vision techniques researchers are able to analyze their images to make progress in these fields. Computer vision is now being used as a tool to facilitate research and development in many other fields.

Going forward, the impact of computer vision will have an even greater impactthe most recent application being driverless cars. To be able to be a part of this ongoing revolution, it is important that we are able to understand and implement computer vision algorithms. This book will introduce the reader to three computer vision libraries written for PythonPillow, Scikit-image, and OpenCV. Through examples and code snippets, the book will help the reader understand the basics of image processing, morphological operations, and eventually, complex feature detection algorithms.

, Introduction to Image Processing, as the name suggests, introduces the reader to the basics of image processing. Starting with some common use cases of image processing, the chapter goes on to explain how to install different image processing libraries. The next few sections in the chapter explain how to read/write an image and perform basic image manipulation operations.

, Filters and Features, gives the reader an overview of what filters and features mean in the context of computer vision. We start with convolution, which forms the basis of applying any filter to an image. Then we look at some common filters, such as Gaussian Blur and Median Blur. The second half of the chapter explains the basic image features and how they are implemented using Python.

, Drilling Deeper into Features Object Detection, walks the reader through some of the sophisticated image feature extraction algorithms, such as Local Binary Pattern and ORB. These algorithms help to identify objects in an image and match them with other images that have the same objects in them. Such matching algorithms form the basis of the most complex computer vision algorithms.

, Segmentation Understanding Images Better, has a different theme than the last two chapters. This chapter looks at different image segmentation algorithms, namely, contour detection, superpixels, watershed, and normalized graph cut. These algorithms are fairly easy to implement and run in almost real time. Image segmentation can be use in real-world applications such as background subtraction, image understanding, and scene labeling. Recent advances in machine learning, especially deep learning, have enabled more sophisticated methods of image segmentation that involve almost no manual tuning of parameters.