Hi,

Last post we classified MNIST using our classifier. I was wondering how we could evaluate our algorithm with “real” data. What if we could evaluate it against others algorithms from other people?

Archive for the **Image processing** Category

Hello,

In the last post I explained briefly what are histograms and today I’ll continue a little more and show how it can be useful to process images. Some examples of how histograms can be used are: image enhancement, texture classification, image segmentation, etc. In this post I’ll cover the most common use for histogram which is histogram equalization. Continue reading Histogram equalization

Hello there,

Today I’ll cover a simple subject, image histogram. What is a histogram? Histogram is a graphical representation of a set of data separated in differents classes. It is represented by vertical bars where the base represents the class and the height represents the frequency/quantity of how many times it happened. Yes, it seems to be more complicated when we try to explain something really easy. Look at the figure 1 below Continue reading Image Histogram

Hello there! In this post, as promised, I’ll explain a little about vector quantization. I’ll try to explain more how it works and not focus in the math, ok?

So, what’s the main idea behind vector quantization? The idea is pretty simple. Continue reading Vector quantization

Today I’ll explain a little about scalar quantization. The goal of scalar quantization is to try to display an image using less quantization levels or less bit to represent each color level.

This method is very simple and very intuitive but the results aren’t that good. So, what’s the idea of scalar quantization? Imagine that we have an RGB image with color quantization from 0 to 255 (8 bits) and we want to represent the colors level only with 2 bits for each color. So instead of using 24 bits we’ll use only 6. Continue reading Scalar Quantization