Tuesday, 2 August 2016

Particles explained using Gifs!!

Over the past few months, I have been reading, understanding and implementing a number of existing algorithms in Computer Vision domain. Implementing particles and particle based algorithms have really had me excited and almost on the edge of my seat. One may ask what makes particles so interesting?? Let me try to get the concept through.

Particles, just like most existing algorithms in computer science, are inspired by nature. Have you ever seen a beam of sunlight coming through a window and illuminate a bunch of floating particles (impossible in London though I have seen it before)? When you see these tiny particles, you notice that they are suspended in air and that it's very difficult to predict their motion unless you disturb the surrounding air. This simple concept is vital for many computer algorithms that model motion/dynamics of an object.

Particles, along with their randomness, can be simulated inside a computer program. The simplest of such algorithm is called Random Walk, where a particle is modelled with its current position/state alone and a random displacement/jump determines its next position in time. Here I have shown one Random Walk particle:

A Single Random Walk Particle

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

% 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');

% 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.