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

Summary


In this chapter, we have continued our journey from the previous one. Filtering operations have been the primary focus of our study. The chapter started off by describing an image averaging operation and then went on to explain how such an operation may be conceptualized by visualizing the same in terms of filters. We then continued to generalize our concept of filters by demonstrating the basics of how any form of filtering is performed on images. By now, you must have realized that we are no longer dealing with simple pixel transformations similar to the ones that we discussed in the last chapter (grayscale transformations). When we talk of filtering operations, the computations at each pixel become much more sophisticated and involve a neighborhood around the pixel.

We learnt about a couple of different filtering techniques: box filtering and Gaussian filtering. Box filtering assumes an equal contribution from all the neighboring pixels in computing the weighted average. This assumption...