Matlab PhD Thesis

Matlab PhD Thesis work depends upon implementation of concepts and also paper preparation. Image processing research work needed matlab simulation to provide clear and perfect results of images. Matlab simulation should help us to simulate and produce efficient and effective results.

 

Functionalities of Matlab:

  • Easy to do very rapid prototyping
  • Majorly used for teaching and research purposes
  • Have excellent display capabilities

 

MATLAB PHD THESIS Areas & Topics:

Image Processing

Image Representation

Image Segmentation

Image Transformation

Remote Sensing

Shadow Detection

Object Classification on ASTER Images

Process of Highly Remote Sensing Images

Underwater Sea Image Based Object Detection

Medical Imaging

Modalities Variation

Disease identification

Image Registration

Signal Processing

Speech Recognition

Wireless Communication

Pattern Analysis and Machine Intelligence

Generation Methods with Images

Pattern Identification

Biomedical Engineering

Technique Supported Disease Identification

Optical Tomography Image Process

Microwave Tomography Image Process

Biometrics

Iris Recognition

Finger Print Identification

Finger vein Recognition

Example Code for Iris Recognition:

function pfm = pfm(template1, mask1, template2, mask2)
scales =1;
template1 = logical(template1);
mask1 = logical(mask1);
template2 = logical(template2);
mask2 = logical(mask2);
pfm = NaN;
% shift template left and right, use the lowest distance
for shifts=-8:8
template1s = shiftbits(template1, shifts,scales);
mask1s = shiftbits(mask1, shifts,scales);
mask = mask1s | mask2;
nummaskbits = sum(sum(mask == 1));
totalbits = (size(template1s,1)*size(template1s,2)) – nummaskbits;
C = xor(template1s,template2);
C = C & ~mask;
bitsdiff = sum(sum(C==1));
if totalbits == 0
pfm = NaN;
else
pfm1 = bitsdiff / totalbits;
if pfm1 < pfm || isnan(pfm)
pfm = pfm1;
end
end
end

Example Code for Low Pass Filter based Image Retrieval:

function f = lowpassfilter(sze, cutoff, n)
if cutoff < 0 | cutoff > 0.5
error(‘cutoff frequency must be between 0 and 0.5’);
end
if rem(n,1) ~= 0 | n < 1 error(‘n must be an integer >= 1’);
end
if length(sze) == 1
rows = sze; cols = sze;
else
rows = sze(1); cols = sze(2);
end
% Set up X and Y matrices with ranges normalised to +/- 0.5
% The following code adjusts things appropriately for odd and even values
% of rows and columns.
if mod(cols, 2)
xrange = [-(cols-1)/2:(cols-1)/2]/(cols-1);
else
xrange = [-cols/2:(cols/2-1)]/cols;
end
if mod(rows, 2)
yrange = [-(rows-1)/2:(rows-1)/2]/(rows-1);
else
yrange = [-rows/2:(rows/2-1)]/rows;
end[x, y] = meshgrid(xrange, yrange);
radius = sqrt(x.^2 + y.^2); % A matrix with every pixel = radius relative to centre.
f = ifftshift( 1 ./ (1.0 + (radius ./ cutoff).^(2*n)) ); % The filter