In order to extract these Haar features, we will have to calculate the sum of the pixel values enclosed in many rectangular regions of the image. To make it scale-invariant, we are required to compute these areas at multiple scales (for various rectangle sizes). Implemented naively, this would be a very computationally-intensive process; we would have to iterate over all the pixels of each rectangle, including reading the same pixels multiple times if they are contained in different overlapping rectangles. If you want to build a system that can run in real-time, you cannot spend so much time in computation. We need to find a way to avoid this huge redundancy during the area computation because we iterate over the same pixels multiple times. To avoid it, we can use something called integral images. These images can be initialized at a linear time (by iterating only twice over the image) and then provide the sum of the pixels inside any rectangle of any size by reading...
Building Computer Vision Projects with OpenCV 4 and C++
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Building Computer Vision Projects with OpenCV 4 and C++
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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.
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
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Building Computer Vision Projects with OpenCV 4 and C++
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Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
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
Cartoonifier and Skin Color Analysis on the RaspberryPi
Explore Structure from Motion with the SfM Module
Face Landmark and Pose with the Face Module
Number Plate Recognition with Deep Convolutional Networks
Face Detection and Recognition with the DNN Module
Android Camera Calibration and AR Using the ArUco Module
iOS Panoramas with the Stitching Module
Finding the Best OpenCV Algorithm for the Job
Avoiding Common Pitfalls in OpenCV
Other Books You May Enjoy
Index
Customer Reviews