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

Cross-validation


Now that we know overfitting is a serious issue in designing and running machine learning-based systems, let's look at a way in which we can mitigate its effects. Remember that we need to ensure that our learning algorithm doesn't start overfitting on the training data; instead, it should maintain a good enough generalization power to predict labels on unseen data.

How can we enforce such a behavior? Let's go back to our classroom example. To make sure that the students are actually understanding the concepts and not merely overfitting by memorizing the classroom problems, the teacher hands over certain assignments. These assignments contain questions that are similar in concept to what has been taught in the classroom but at the same time, also give the students an idea of the type of questions to expect in the actual exam. In machine learning parlance, the assignments are analogous to the validation set. The students are expected to periodically check their level of understanding...