Book Image

Mastering OpenCV 4 - Third Edition

By : Roy Shilkrot, David Millán Escrivá
Book Image

Mastering OpenCV 4 - Third Edition

By: Roy Shilkrot, David Millán Escrivá

Overview of this book

Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4.
Table of Contents (12 chapters)

Which algorithm is best?

Computer vision is a world of knowledge and a decades-long research pursuit. Unlike many other disciplines, computer vision is not strongly hierarchical or vertical, which means new solutions for given problems are not always better and may not be based on preceding work. Being an applied field, computer vision algorithms are created with attention to the following aspects, which may explain the non-vertical development:

  • Computation resources: CPU, GPU, embedded system, memory footprint, network connectivity.
  • Data: Size of images, number of images, number of image stream (cameras), data type, sequentiality, lighting conditions, types of scenes, and so on.
  • Performance requirements: Real-time output or another timing constraint (for example, human perception), accuracy and precision.
  • Meta-algorithmic: Algorithm simplicity (cross-reference Occam's Razor...