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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

By : Millán Escrivá, Robert Laganiere
5 (1)
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OpenCV 4 Computer Vision Application Programming Cookbook

OpenCV 4 Computer Vision Application Programming Cookbook

5 (1)
By: Millán Escrivá, Robert Laganiere

Overview of this book

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work with recipes to implement a variety of tasks. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs. This book begins by guiding you through setting up OpenCV, and explaining how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection. By the end of this book, you'll have the skills you need to confidently implement a range of computer vision algorithms to meet the technical requirements of your complex CV projects.
Table of Contents (17 chapters)
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Detecting scale-invariant features

The view invariance of feature detection was presented as an important concept in the introduction of this chapter. While orientation invariance, which is the ability to detect the same points even if an image is rotated, has been relatively well handled by the simple feature point detectors that have been presented so far, changes for invariance to scale are more difficult to achieve. To address this problem, the concept of scale-invariant features has been introduced in computer vision.

The idea here is to not only have a consistent detection of keypoints no matter at which scale an object is pictured, but to also have a scale factor associated with each of the detected feature points. Ideally, for the same object point featured at two different scales on two different images, the ratio of the two computed scale factors should correspond to...

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