Monday, 6 June 2016

Expectation Maximization for Gaussian Mixture Model in OpenCV

I recently wrote code for Gaussian Mixture Model (GMM) based clustering in C++. As always, I found it much convenient to use OpenCV for manipulating matrices. Although there already exist an implementation of Expectation Maximization-based GMM, I tried to understand it by writing my own implementation.

The basic idea of GMM is to first randomly assign each sample to a cluster. This provides initial mixture model for clustering. This is then optimized using Expectation - or the probability/score of assigning each sample to each component in GMM - and Maximization - or updating the characteristics of each mixture component with the given probability/score . An attractive attribute of GMM is its ability to cluster data that does not have clear boundaries for clusters. This is achieved by having a probability/score for each sample from each cluster component.

Tuesday, 17 May 2016

A Random Walk

It is fascinating to see the use of the word 'random' and its resemblance to one of the most basic ingredients in some computer algorithms. One may ask what is it that makes something random?

- "So you just made a random deal?"
- "Students were randomly chosen to take part in a drama."
- "He figured out that he still had an hour to his departure, so he went for a random walk."

Monday, 9 May 2016

Matlab script for checking and deleting folders

Just putting this simple but extremely useful matlab script for my future self and anyone trying to handle folders using matlab. This script checks all the sub directory within the starting directory and then deletes the one that do not satisfy a given criteria. In my case this was the number of image samples within a folder.

% script for deleting folders with less than a certain number of files
close all
clear all
clc

% count the number of png files
D = dir(' ');


numFoldersOrFiles = size(D, 1);

thresholdFiles = 30;

% skipping the first two which are just . and ..
for i = 3: numFoldersOrFiles
    
    if D(i).isdir
        
        Ds = dir([D(i).name '\*.png']);
        numFiles = size(Ds, 1) / 3;
        if numFiles < thresholdFiles
            
            rmdir(D(i).name, 's');
            
        end
    end
end


% all done :)

Saturday, 16 April 2016

OpenCVKinect 2.0 - Acquiring Kinect depth stream in OpenCV

It has been almost two years since I first wrote the code for OpenCVKinect. It has been really good to know that it has been used by a number of other students/developers at GitHub for collecting and analysing Kinect depth streams in OpenCV. I have had some feedback about a possible bug and some students have asked how they can visualize the depth maps in a better way. So today, after a long time, I am releasing the first official update to this project.


Thursday, 14 April 2016

Particle Filtering - Survival of the fittest

I recently studied dynamic system models such as Kalman and Particle Filters.
For Kalman Filter I followed a Matlab demo that can be found here.

In this demo, the simple problem of tracking a ball is addressed using a Kalman Filter. The input sequence is of a ball, which is travelling at varying velocity and which is occluded in some frames by a box. I think this is a great example to demonstrate the power of dynamic system  models, especially the occluded frames can be used to test how good a dynamic model is. Here is the actual sequence:


As you can see the ball goes underneath the box and comes out of the other end. If our dynamic model is accurate it will be able to predict the state of the ball even when it is not visible, and should match the position when the ball comes out.