When a scene is observed by a camera, the observed brightness pattern is projected on the image sensor and thus forms an image. In a video sequence, we are often interested in capturing the motion pattern, that is, the projection of the 3D motion of the different scene elements on an image plane. This image of projected 3D motion vectors is called the motion field. However, it is not possible to directly measure the 3D motion of scene points from a camera sensor. All we observe is a brightness pattern that is in motion from frame to frame. This apparent motion of the brightness pattern is called the optical flow. You might think that the motion field and optical flow should be equal, but this is not always true. An obvious case can be the observation of a uniform object; for example, if a camera moves in front of a white wall, then no optical flow is...
OpenCV 4 Computer Vision Application Programming Cookbook
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OpenCV 4 Computer Vision Application Programming Cookbook
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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)
Preface
Free Chapter
Playing with Images
Manipulating the Pixels
Processing Color Images with Classes
Counting the Pixels with Histograms
Transforming Images with Morphological Operations
Filtering the Images
Extracting Lines, Contours, and Components
Detecting Interest Points
Describing and Matching Interest Points
Estimating Projective Relations in Images
Reconstructing 3D Scenes
Processing Video Sequences
Tracking Visual Motion
Learning from Examples
OpenCV Advanced Features
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