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)

What is filtering and how to use it?


Image filtering is always a powerful tool to use in your Computer Vision applications. It allows you to apply many exciting effects on your photos, such as image correction, noise reduction, embossing, and many more. Image filtering is actually a huge subpart of an image processing area. In this section, we will discuss the concepts of image filtering and talk about a basic operation—convolution, which is widely used in all Computer Vision applications. Furthermore, we will see how different effects, such as blurring, are achieved.

Image convolution

The core of most filtering operations is image convolution. With its understanding you will have the power to make your own image filters.

The image convolution idea is that you want to apply to each pixel of the original image a transformation which is based on neighboring pixels. For this, you have a kernel—a simple 2D matrix, this is our transformation matrix. For each pixel of the original image, we take...