Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code! The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Image averaging in OpenCV


While implementing the image transforms that we discussed in the previous chapter, we adopted an approach that was based on the fundamentals and involved quite a bit of reinventing the wheel. We could afford to do that because the traversals that we performed over the data matrix in our implementations were conceptually pretty straightforward. However, we will no longer do that for a couple of reasons:

  • The kind of transformations that we are discussing at the moment (averaging using a filtering-based approach) no longer involves a simple pixel-by-pixel traversal of the data matrix. Rather, they involve a two-tiered approach where we have to traverse the neighborhood for each pixel that we encounter in our usual traversal of the data matrix. Implementing such a non-trivial traversal every single time can become time-consuming and error-prone.

  • As we progress through the book, we want you to rely more and more on the functions and APIs provided by the OpenCV developers...