Best OpenCV books for security, medical imaging, pattern and face recognition, robotics and factory product inspection
OpenCV is for image processing. OpenCV has both python and C++ interfaces. The best language for computer vision is C++. We can do image processing operations, building GUI, video analysis, 3D reconstruction, feature extraction, object detection, machine learning, etc with OpenCV.
Here you will get some of the Best OpenCV books
Learning OpenCV 3 is a great reference book that provides a thorough introduction for developers, academics, roboticists, and hobbyists. After reading this OpenCV book you'll learn OpenCV data types, array types, and array operations. You can capture and store still and video images with HighGUI and explore pattern recognition, including face detection. Again you'll be able to reconstruct 3D images from stereo vision.
This book includes information about the following information
- The basics of OpenCV
- Users of OpenCV
- Computer vision
- The origin of OpenCV
- Downloading and installing OpenCV
- Getting the latest OpenCV via Git
- More OpenCV documentation
- OpenCV contribution repository.
You will learn to
- Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
- Create computer vision applications
- Image processing and machine learning for OpenCV with multiple examples.
- Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
- Learn feature extraction and feature matching for tracking arbitrary objects of interest
- Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
- Track visually salient objects by searching for and focusing on important regions of an image
- Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer perceptrons (MLPs)
- Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
- Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features.
Machine Learning for OpenCV is one of the best books for machine learning. It includes a great explanation of fundamental machine learning. This OpenCV book is the best guidance for ML and CV. It loads, store, edit, and visualize data using OpenCV and Python. Again evaluate, compare, and choose the right algorithm for any task. After reading this book you'll be able to grasp the fundamental concepts of classification, regression, and clustering.
Key contents are
- A test of machine learning
- Working with data in OpenCV and Python
- First steps in supervised learning
- Representing data and engineering features
- Using decision trees to make a medical diagnosis
- Detecting pedestrians with support vector machines
- Implementing a spam filter with Bayesian learning
- Discovering hidden structures with unsupervised learning
- Using deep learning to classify handwritten digits
- Combining different algorithms into an ensemble
- Selecting the right model with hyperparameter tuning
- Wrapping up.
- Detect car headlights, estimate distances to them, and provide feedback to the driver
- Spot and recognize human faces and cat faces as a means of controlling an alarm
- Amplify motion in a real-time video so that a person's heartbeat and breathing become clearly visible
- Integrate OpenCV with other libraries, as well as popular frameworks for GUI apps and games.
After reading this book you'll be able to
- Apply effects to images
- Detect basic features in images
- Perform image transformations such as changing color, space, resizing, applying filters like Gaussian blur, and likes
- Explore face detection, object detection, and image stitching in OpenCV Android programming
- Tracking objects in videos
- Work with image alignment and stitching
- Learn to debug applications and create optimal custom algorithms by understanding how data is stored internally
- Develop a document scanning app
- Build smarter applications by using machine learning algorithms.