Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Theory and context


Facial landmark detection algorithms automatically find the locations of key landmark points on facial images. Those key points are usually prominent points locating a facial component, such as eye corner or mouth corner, to achieve a higher-level understanding of the face shape. To detect a decent range of facial expressions, for example, points around the jawline, mouth, eyes, and eyebrows are needed. Finding facial landmarks proves to be a difficult task for a variety of reasons: great variation between subjects, illumination conditions, and occlusions. To that end, computer vision researchers proposed dozens of landmark detection algorithms over the past three decades.

A recent survey of facial landmark detection (Wu and Ji, 2018) suggests separating landmark detectors into three groups: holistic methods, constrained local model (CLM) methods, and regression methods:

  • Wu and Ji pose the holistic methods as ones that model the complete appearance of the face's pixel intensities...