Book Image

Mastering OpenCV with Practical Computer Vision Projects

By : Mora Saragih, Eugene Khvedchenia, Daniel L√É∆í¬©lis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih, Daniel Lelis Baggio, OpenCV Project, David Millán Escrivá, Roy Shilkrot, Naureen Mahmood
Book Image

Mastering OpenCV with Practical Computer Vision Projects

By: Mora Saragih, Eugene Khvedchenia, Daniel L√É∆í¬©lis Baggio, Shervin Emami, Khvedchenia Ievgen, Jason Saragih, Daniel Lelis Baggio, OpenCV Project, David Millán Escrivá, Roy Shilkrot, Naureen Mahmood

Overview of this book

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials.Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV's new C++ interface before migrating from the C API to the C++ API.Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you're most interested in.Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.
Table of Contents (15 chapters)
Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

ANPR algorithm


Before explaining the ANPR code, we need to define the main steps and tasks in the ANPR algorithm. ANPR is divided in two main steps: plate detection and plate recognition. Plate detection has the purpose of detecting the location of the plate in the whole camera frame. When a plate is detected in an image, the plate segment is passed to the second step—plate recognition—which uses an OCR algorithm to determine the alphanumeric characters on the plate.

In the next figure we can see the two main algorithm steps, plate detection and plate recognition. After these steps the program draws over the camera frame the plate's characters that have been detected. The algorithms can return bad results or even no result:

In each step shown in the previous figure, we will define three additional steps that are commonly used in pattern recognition algorithms:

  1. Segmentation: This step detects and removes each patch/region of interest in the image.

  2. Feature extraction: This step extracts from...