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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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Describing and Matching Interest Points

In Chapter 8, Detecting Interest Points, we learned how to detect special points in an image with the objective of subsequently performing a local image analysis. These keypoints are chosen to be distinctive enough so that if a keypoint is detected on the image of an object, then the same point is expected to be detected in other images depicting the same object. We also described some more sophisticated interest point detectors that can assign a representative scale factor and/or an orientation to a keypoint. As we will see in this recipe, this additional information can be useful to normalize scene representations with respect to viewpoint variations.

In order to perform image analysis based on interest points, we now need to build rich representations that uniquely describe each of these keypoints. This chapter looks at the different...

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