This week I came across a very hot topic — Augmented Reality, but before understanding that we need to have an intuition behind ArUco markers.
ArUco markers are similar to QR codes, the differentiating factor being that QR codes stores much more information than a ArUco marker and is therefore difficult to employ for what we are are about to do.
It is a binary matrix composed of a black border around it , it is a synthetic marker used to instantly locate it in an image or video.
This is what a ArUco Marker looks like-
In the previous tutorial we learnt about histograms in image processing and how it works, this time we are going to level up and see its implementation in feature extraction techniques and how this simple technique actually levels up your game!
Consider the image below, this image has irregular lighting conditions , which makes it difficult to detect objects in the image. So, you can apply a simple technique known as histogram equalization to improve the results of your detection or improve the lighting in this image.
Let’s see how can we do that in python! Lets start by importing…
If you are new to computer vision you must have faced some difficulty when the image contrast is not ideal or some information is not clearly visible!
Histogram equalization provides a solution to this problem, just applying this technique to your input data improves the performance of your machine learning model.
So , lets start!
In simple words it is nothing but the count of pixel intensities in an image. Lets say this is the image given to you
Computer vision enthusiast , learn and teach!