What?! Averaging? You mean blurring? No. This method does not use any type of blurring to reduce noise in a particular image. Infact, the result is the opposite blurring: you get sharp images! In the averaging method, it is assumed that you have several images of the same object… each with a different “noise pattern”. [...]
Models?! Where?!?!! Technically, it is possible to “represent” random noise as a mathematical function. And that is exactly what a model is. The “distribution” of noise is based on probability. Hence the model is called a Probability Density Function (PDF). Once noise has been quantified, creating filters to get rid of it becomes a lot [...]
Uniform Noise Uniform noise means the different “values” of noise are equally probably. Its probability density function looks something like this: If you don’t understand what this graph (probability distribution function) means, you might want to have a look at the article on Noise Models. Generating uniform random numbers Here the plan of action to [...]
Introduction Images are stored in memory in various different colour spaces. You might have heard of one, the RGB colour space. That is the one Windows uses a lot. For image processing purposes, one often needs other colour spaces that better suit the purpose of the application. In this article, I’ll go through the RGB [...]
Introduction Thresholding is one of the most basic techniques for what is called Image Segmentation. When you threshold an image, you get segments inside the image… each representing something. For example… complex segmentation algorithms might be able to segment out “house-like” structures in an image. With thresholding, you can segment the image based on colour. [...]
Introduction In this tutorial, you’ll get to know how to use contours. You can think of contours as a boundary. Consider the following thresholded image: Using contours, the computer can create a list of points for each “patch” or “blob” of white in the above image. Then you can do whatever you want with these [...]
My name is Utkarsh Sinha, and I'm an undergraduate student, pursuing B.E. Computer Science + M.Sc. Mathematics. Here, I help you understand ideas in Artificial Intelligence, using a not so techy and mathematical language. And in the process, learn more about Artificial Intelligence myself.
