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...
Learn OpenCV 4 by Building Projects
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Learn OpenCV 4 by Building Projects
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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. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects.
You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module.
By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)
Preface
Free Chapter
Getting Started with OpenCV
An Introduction to the Basics of OpenCV
Learning Graphical User Interfaces
Delving into Histogram and Filters
Automated Optical Inspection, Object Segmentation, and Detection
Learning Object Classification
Detecting Face Parts and Overlaying Masks
Video Surveillance, Background Modeling, and Morphological Operations
Learning Object Tracking
Developing Segmentation Algorithms for Text Recognition
Text Recognition with Tesseract
Deep Learning with OpenCV
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