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

Morphological image processing


As we discussed earlier, background subtraction methods are affected by many factors. Their accuracy depends on how we capture the data and how it's processed. One of the biggest factors that affects these algorithms is the noise level. When we say noise, we are talking about things such as graininess in an image and isolated black/white pixels. These issues tend to affect the quality of our algorithms. This is where morphological image processing comes into play. Morphological image processing is used extensively in a lot of real-time systems to ensure the quality of the output. Morphological image processing refers to processing the shapes of features in the image; for example, you can make a shape thicker or thinner. Morphological operators rely not on how the pixels are ordered in an image, but on their values. This is why they are really well suited to manipulating shapes in binary images. Morphological image processing can be applied to grayscale images...