Book Image

OpenCV 3.x with Python By Example - Second Edition

By : Gabriel Garrido Calvo, Prateek Joshi
Book Image

OpenCV 3.x with Python By Example - Second Edition

By: Gabriel Garrido Calvo, Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Scale-invariant feature transform (SIFT)

Even though corner features are interesting, they are not good enough to characterize the truly interesting parts. When we talk about image content analysis, we want the image signature to be invariant to things such as scale, rotation and illumination. Humans are very good at these things. Even if I show you an image of an apple upside down that's dimmed, you will still recognize it. If I show you a really enlarged version of that image, you will still recognize it. We want our image recognition systems to be able to do the same.

Let's consider the corner features. If you enlarge an image, a corner might stop being a corner, as follows:

In the second case, the detector will not pick up this corner. And, since it was picked up in the original image, the second image will not be matched with the first one. It's basically the same image, but the corner features-based method will totally miss it. This means that a corner detector is not exactly scale-invariant...