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Computer Vision Source Code

Posted by Hemprasad Y. Badgujar on January 17, 2015

Computer Vision Source Code


Matlab code for Skin Detection:
Info: Readme.TXT
Full Matlab code and demo:

If you publish work which uses this code, please reference:
Ciarán Ó Conaire, Noel E. O’Connor and Alan F. Smeaton, “Detector adaptation by maximising agreement between independent data sources”, IEEE International Workshop on Object Tracking and Classification Beyond the Visible Spectrum 2007

(1) (2) (3)
(1) Original image, (2) Skin Likelihood image and (3) Detected Skin (using a threshold of zero)


Command line Face Detection

This uses the face-detector in the Blepo computer vision library, which in turn uses the OpenCV implementation of the Viola-Jones face detection method.

Info and usage: README


Simple MATLAB scripts

These small scripts are required for some of the other code on this page to work.
Code: makelinear.m – convert any size array/matrix into a Nx1 vector, where N = prod(size(inputMatrix))
Code: shownormimage.m – display any single-band or triple-band image, by normalising each band it so that the darkest pixel is black and the brightest is white
Code: filter3.m – uses filter2 to perform filtering on each image band separately
Code: removezeros.m – removes zero values from a vector.
Code: integrate.m – compute the cumulative sum of values in the rows of a matrix.

Layered Background Model

ReferencePerformance analysis and visualisation in tennis using a low-cost camera network, Philip Kelly, Ciarán Ó Conaire, David Monaghan, Jogile Kuklyte, Damien Connaghan, Juan Diego Pérez-Moneo Agapito, Petros Daras, Multimedia Grand Challenge Track at ACM Multimedia 2010, 25-29 October 2010, Firenze, Italy.
PDF Version




model creation:

N = 5; % number of layers

T = 20; % RGB Euclidian threshold (for determining if the pixel matches a layer)

U = 0.999; % update rate

A = 0.85; % fraction of observed colour to account for in the background


im = imread(‘image.jpg’);

bgmodel = initLayeredBackgroundModel(im, N, T, U, A);




model updating:

im = imread(‘image_new.jpg’);

[bgmodel, foreground, bridif, coldif, shadows] = updateLayeredBackgroundModel(bgmodel, im);



updated background model

foreground image

brightness difference

colour difference

shadow pixels image




Filtering/Image blurring

Code: gaussianFilter.m


% setup filter

filt = gaussianFilter(31,5);

% read in an image

im = double(imread(‘lena.jpg’));

% filter image

imf = filter3(filt, im);

% show images (get the code for ‘shownormimage.m’/’filter3.m’ above)

shownormimage(im); pause

shownormimage(imf); pause


Adaptive Image Thresholding

The following pieces of code implement the adaptive thresholding methods of Otsu, Kapur and Rosin.
References to the original papers given below.
Code: otsuThreshold.m
Code: kapurThreshold.m
Code: rosinThreshold.m
Code: dootsuthreshold.m
Code: dokapurthreshold.m
Code: dorosinthreshold.m

Example usage:

% read in image 0000 of an image sequence

im1 = double(imread(‘image0000.jpg’));

% read in image 0025 of an image sequence

im2 = double(imread(‘image0025.jpg’));


% compute the difference image (Euclidian distance in RGB space)

dif = sqrt(sum((im1-im2).^2,3));


% compute difference image histogram

[h, hc] = hist(makelinear(dif), 256);


% perform thresholding to detect motion

To = hc(otsuThreshold(h));

Tk = hc(kapurThreshold(h));

Tr = hc(rosinThreshold(h));


% display results

shownormimage(dif >= To); title(‘Otsu result’); pause

shownormimage(dif >= Tk); title(‘Kapur result’); pause

shownormimage(dif >= Tr); title(‘Rosin result’); pause


% Alternatively, you can use

% shownormimage(dokapurthreshold(dif)); % …. etc

Source images (above)

Thresholded difference images (from left to right): Rosin, Kapur and Otsu’s method.


  • N. Otsu, A threshold selection method from gray-level histogram, IEEE Trans on System Man Cybernetics 9 (1979), no. 1, 62-66.
  • J. Kapur, P. Sahoo, and A. Wong, A new method for graylevel picture thresholding using the entropy of the histogram, Computer Graphics and Image Processing 29 (1985), no. 3, 273-285
  • P. L. Rosin, Unimodal thresholding, Pattern Recognition 34 (2001), no. 11, 2083-2096
Image Descriptors and Image Similarity

Code to extract global descriptors for images and to compare these descriptors.
Can be used for image retrieval, tracking, etc.

Image colour histogram extraction: getPatchHist.m
Histogram comparison using the Bhattacharyya coefficient: compareHists.m
Image colour spatiogram extraction: getPatchSpatiogram_fast.m
Spatiogram comparison: compareSpatiograms_new_fast.m
MPEG-7 Edge Orientation Histogram extraction: edgeOrientationHistogram.m
(Note: code for histogram comparison can be used with both colour histograms and edge orientation histograms)

Sample code:

% —- Code to compare image histograms —-


% read in database image

im1 = double(imread(‘flower.jpg’));

% read in query image

im2 = double(imread(‘garden.jpg’));


% Both images are RGB Colour images

% Extract an 8x8x8 colour histogram from each image

bins = 8;

h1 = getPatchHist(im1, bins);

h2 = getPatchHist(im2, bins);


% compare their histograms using the Bhattacharyya coefficient

sim = compareHists(h1,h2);


% 0 = very low similarity

% 0.9 = good similarity

% 1 = perfect similarity

disp(sprintf(‘Image histogram similarity = %f’, sim));



% —- Code to compare image SPATIOGRAMS —-


% Both images are RGB Colour images

% Extract an 8x8x8 colour SPATIOGRAM from each image

bins = 8;

[h1,mu1,sigma1] = getPatchSpatiogram_fast(im1, bins);

[h2,mu2,sigma2] = getPatchSpatiogram_fast(im2, bins);


% compare their histograms using the Bhattacharyya coefficient

sim = compareSpatiograms_new_fast(h1,mu1,sigma1,h2,mu2,sigma2);


% 0 = very low similarity

% 0.9 = good similarity

% 1 = perfect similarity

disp(sprintf(‘Image spatiogram similarity = %f’, sim));


Nearest-Neighbour search using KD-Trees

(coming soon…)

Hierarchical Clustering using K-means
Finding the nearest neighbour of a data point in high-dimensional space is known to be a hard problem [1].
This code clusters the data into a hierarchy of clusters and does a depth-first search to find the approximate nearest-neighbour to the query point. This technique was used in [2] to match 128-dimensional SIFT descriptors to their visual words.

K-means clustering: kmeans.m
Building a hierarchical tree of D-dimensional points: kmeanshierarchy.m
Approximate Nearest-Neighbour using a hierarchical tree: kmeanshierarchy_findpoint.m

Sample code:


D=2; % dimensionality

K=2; % branching factor

N=50; % size of each dataset

iters = 3; % number of clustering iterations

% setup datasets, first column is the ID

dataset1 = [ones(N,1) randn(N,D)];

dataset2 = [2*ones(N,1) 2*randn(N,2)+repmat([-5 3],[N 1])];

dataset3 = [3*ones(N,1) 1.5*randn(N,2)+repmat([5 3],[N 1])];

data = [dataset1; dataset2; dataset3];



% build the tree structure

% select columns 2 to D+1, column 1 stores the dataset-ID that the point came from.

[hierarchy] = kmeanshierarchy(data, 2, D+1, iters, K);


for test = 1:16


% Generate a random point

point = [rand*16-8 randn(1,D-1)];


% plot the data

cols = [‘bo’;’rx’;’gv’];

hold off

for i = 1:size(data,1)


hold on


hold off


if (test==1)

title(‘Data Clusters (1=Blue, 2=Red, 3=Green)’)




hold on

plot(point(1), point(2), ‘ks’);

hold off

title(‘New Point (shown as a black square)’);



% Find its approximate nearest-neighbour in the tree

nn = kmeanshierarchy_findpoint(point, hierarchy, 2, D+1, K);


nearest_neighbour = nn(2:(D+1));


% Which set did it come from?

set_id = nn(1);


line([point(1) nearest_neighbour(1)],[point(2) nearest_neighbour(2)])

title(sprintf(‘Approx Nearest Neighbour: Set %d’, set_id))






[1] Piotr Indyk. Nearest neighbors in high-dimensional spaces.
Handbook of Discrete and Computational Geometry, chapter 39.
Editors: Jacob E. Goodman and Joseph O’Rourke, CRC Press, 2nd edition, 2004. CITESEER LINK

[2] David Nistér and Henrik Stewenius, Scalable Recognition with a Vocabulary Tree, CVPR’06 CITESEER LINK

Mutual Information Thresholding

The idea of selecting thresholds for data “adaptively” has been around for a long time. The standard paradigm is to observe some property of the data (such as histogram shape, entropy, spatial layout, etc) and to choose a threshold that maximises some proposed performance measure. Mutual Information (MI) Thresholding takes a different approach. Instead of examining a single property of the data, it looks instead at how choices of threshold for two sources of data will affect how well they “agree” with each other.

More formally: Given two sources of data that have uncorrelated noise, choose a threshold for each of the sources, such that the mutual information between the resulting binary signals is maximised. This search through threshold-space can be done very efficiently using integral-images.

Matlab Code Download: ZIP FILE

Sample code:


signal = 30;

noise = 5;

im = zeros(N,N);

im(round(N/3:N/2),round(N/3:N/2)) = signal;

im1 = abs(im + noise * randn(N,N));

im2 = abs(im + 1.5*noise * randn(N,N));

[T1, T2, mi, imT1, imT2, imF, quality, miscore, mii1, mii2] = mutualinfoThreshold(im1, im2);

subplot(1,2,1); shownormimage2(im1);

subplot(1,2,2); shownormimage2(im2);


subplot(1,2,1); shownormimage2(imT1); title(sprintf(‘Threshold = %f’, T1));

subplot(1,2,2); shownormimage2(imT2); title(sprintf(‘Threshold = %f’, T2));



shownormimage2(mi); title(‘Mutual Information Surface’);



  • C. Ó Conaire, N. O’Connor, E. Cooke and A. Smeaton, Detection Thresholding using Mutual Information, VISAPP 2006
    PDF Available
  • C. Ó Conaire and N. O’Connor, Unsupervised feature selection for detection using mutual information thresholding, WIAMIS 2008



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