Learning OpenCV: Computer Vision with the OpenCV Library
"This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised."-William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.
Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.
Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:
- A thorough introduction to OpenCV
- Getting input from cameras
- Transforming images
- Segmenting images and shape matching
- Pattern recognition, including face detection
- Tracking and motion in 2 and 3 dimensions
- 3D reconstruction from stereo vision
- Machine learning algorithms
Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.
Why Read This Book
You will get a hands-on, example-driven tour of the OpenCV library that lets you quickly prototype and deploy computer-vision algorithms. The book emphasizes practical implementation details, making it ideal when you need working code and workflows rather than only theory.
Who Will Benefit
Engineers or developers who write vision or image-processing code and want pragmatic, implementation-focused guidance for building applications with OpenCV.
Level: Intermediate — Prerequisites: Basic C++ programming, familiarity with linear algebra and basic image concepts (pixels, color spaces, convolution).
Key Takeaways
- Build end-to-end image processing pipelines using OpenCV data structures and APIs
- Apply common filters, morphological operations, color-space conversions, and image transforms
- Detect and describe image features (edges, corners, descriptors) and match them across images
- Implement object detection, template matching, motion analysis, and basic tracking
- Calibrate cameras, perform stereo matching and reconstruct simple 3D scenes
- Integrate OpenCV with GUI, I/O and optimize code for near real-time performance
Topics Covered
- Introduction to OpenCV and computer vision
- Getting started: installation, build systems, and simple image I/O
- Core data structures: Mat, image formats, and memory management
- Image processing fundamentals: filtering, color spaces, and transformations
- Geometric image processing: warp, remap, and morphological operations
- Feature detection and description: edges, corners, SIFT/SURF-era methods
- Matching and recognition: template matching and descriptors
- Motion analysis and object tracking
- Camera calibration, stereo vision and 3D reconstruction
- Machine learning tools in OpenCV (classifiers, clustering)
- Performance considerations and optimization
- Building applications: GUI, video I/O, and deployment
Languages, Platforms & Tools
How It Compares
More application- and code-focused than Gonzalez & Woods' Digital Image Processing (which is heavier on theory); complements later OpenCV cookbooks/tutorials by giving foundational, creator-driven perspective.
