Learn how the famous SIFT keypoint detector works in the background. This paper led a mini revolution in the world of computer vision!

I decided to write a quick and fun project. The idea is simple - capture an image, identify the sudoku grid + digits and then solve the puzzle!

If you come from the land of Matlab, you might need some convincing to switch to the much harder programming language - C++. This article tries to do just that!

Recognize QR Codes in images from scratch. We'll do all the bit math to figure out the location markers and then read data from the black/white array.

An in-depth exploration of how the famous Canny edge detection system works. We'll implement our own after going through the theory.

Image moments help identify certain key characteristics in images - like the center, area of white pixels, etc. We'll look at how these are calculated mathematically.

Learn how to implement really fast thresholding - faster than OpenCV! This technique can be a useful addition to your arsenal of computer vision.

Edge detection is a fundamental image processing operation. Learn about how to calculate derivatives and find edges in your images using simple matrix operations.

Learn how to implement K-Nearest in OpenCV. We use the MNIST database of handwritten numbers.

Learn about how the K-nearest neighbours algorithm works and how performance varies as the size of the inputs are varied.