Book Image

OpenCV 3 Blueprints

By : Joseph Howse, Puttemans, Sinha
Book Image

OpenCV 3 Blueprints

By: Joseph Howse, Puttemans, Sinha

Overview of this book

Computer vision is becoming accessible to a large audience of software developers who can leverage mature libraries such as OpenCV. However, as they move beyond their first experiments in computer vision, developers may struggle to ensure that their solutions are sufficiently well optimized, well trained, robust, and adaptive in real-world conditions. With sufficient knowledge of OpenCV, these developers will have enough confidence to go about creating projects in the field of computer vision. This book will help you tackle increasingly challenging computer vision problems that you may face in your careers. It makes use of OpenCV 3 to work around some interesting projects. Inside these pages, you will find practical and innovative approaches that are battle-tested in the authors’ industry experience and research. Each chapter covers the theory and practice of multiple complementary approaches so that you will be able to choose wisely in your future projects. You will also gain insights into the architecture and algorithms that underpin OpenCV’s functionality. We begin by taking a critical look at inputs in order to decide which kinds of light, cameras, lenses, and image formats are best suited to a given purpose. We proceed to consider the finer aspects of computational photography as we build an automated camera to assist nature photographers. You will gain a deep understanding of some of the most widely applicable and reliable techniques in object detection, feature selection, tracking, and even biometric recognition. We will also build Android projects in which we explore the complexities of camera motion: first in panoramic image stitching and then in video stabilization. By the end of the book, you will have a much richer understanding of imaging, motion, machine learning, and the architecture of computer vision libraries and applications!
Table of Contents (9 chapters)
8
Index

Obtaining rotation invariance object detection


A large downside to the current OpenCV cascade classifier implementation is that it only supports multiscale single rotation object detection. Many industrial applications that could actually use object detection do not know the orientation of the object beforehand and thus rotation invariant multiscale object detection would be much more interesting. Therefore, I will guide you through some techniques for applying multiscale rotation invariant object detection, by simply using the provided functionality in OpenCV.

Note

OpenCV 3 also provides other techniques that are able to perform multiscale rotation invariant object categorization like the Bag of Visual Words approach. A good tutorial on this technique can be found at https://gilscvblog.wordpress.com/2013/08/23/bag-of-words-models-for-visual-categorization/.

There are three main ideas when trying to achieve rotation invariant object detection:

  • Train a single object model with all possible orientations...