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.

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.

Monday, 28 March 2016

Designing an algorithm - from ideas to code

I had always been interested in solving sudoku puzzles, partly because there are too many combinations that make each Sudoku unique. Since my work involves writing and using programming in different scenarios, I thought why not try using my skills on Sudoku. So there I was on a London Underground train to Barbican - looking at a Sudoku puzzle at the back of a morning newspaper, wondering how I can write an algorithm to solve it. I figured out a few simple tricks that I have always used in algorithm design. Here I explain what thoughts I had while designing my very own Sudoku solver and how I transformed those ideas into a working prototype.

First of all lets have a look at a typical Sudoku puzzle and some basic rules:

Sudoku Puzzle
Yes - it has got everything to do with numbers!! lots of numbers!

A Sudoku puzzle typically has 81 boxes where each box can have a number between 1 to 9. However, all these boxes follow some rules that make it all interesting. You may have noticed 3x3 squares grouping the number boxes. A correct solution of Sudoku ensures no repetition of numbers from 1 to 9 inside each of the 3x3 squares, in each horizontal line and each vertical line. When solving a Sudoku puzzle, this is exactly where I look for a solution, and exactly where my thought process starts for my Sudoku solver algorithm.