Book Image

Computer Vision for the Web

By : Foat Akhmadeev
Book Image

Computer Vision for the Web

By: Foat Akhmadeev

Overview of this book

This book will give you an insight into controlling your applications with gestures and head motion and readying them for the web. Packed with real-world tasks, it begins with a walkthrough of the basic concepts of Computer Vision that the JavaScript world offers us, and you’ll implement various powerful algorithms in your own online application. Then, we move on to a comprehensive analysis of JavaScript functions and their applications. Furthermore, the book will show you how to implement filters and image segmentation, and use tracking.js and jsfeat libraries to convert your browser into Photoshop. Subjects such as object and custom detection, feature extraction, and object matching are covered to help you find an object in a photo. You will see how a complex object such as a face can be recognized by a browser as you move toward the end of the book. Finally, you will focus on algorithms to create a human interface. By the end of this book, you will be familiarized with the application of complex Computer Vision algorithms to develop your own applications, without spending much time learning sophisticated theory.
Table of Contents (13 chapters)

Image features


Color object detection and detection of changes in intensity of an image, is a simple Computer Vision method. It is a fundamental thing which every Computer Vision enthusiast should know. To get a better picture of Computer Vision capabilities, we will see how to find an object on a scene using a template. This topic includes several parts: feature extraction and descriptor matching. In this part, we will discuss feature detection and its application in Computer Vision.

Detecting key points

What information do we get when we see an object on an image? An object usually consists of some regular parts or unique points, which represent the particular object. Of course, we can compare each pixel of an image, but it is not a good idea in terms of computational speed. We can probably take unique points randomly, thus reducing the computation cost significantly. However, we will still not get much information from random points. Using the whole information, we can get too much noise...