MATLAB THESIS

Matlab thesis work to be taken for research issues and solved those problems based on several methods, algorithms and techniques. Matlab simulation tool should support to implement research work (concepts) in an effective manner. Matlab Thesis research work would be assisted by our concern with 100% confidential and success. Matlab thesis based paper is plagiarism free. We would guarantee for Matlab thesis research work would be published in SCI and SCOPUS Journals.

Steps involved in Matlab thesis writing:

  • Paper analysis.
  • Problem formulation.
  • Propose an new idea.
  • Briefly discuss about new algorithms, methodologies and techniques.
  • Performance evaluation.
  • Conclude proposed concept and put some future idea.

Areas focused on Matlab Thesis:

  • Digital image Processing
  • Remote Sensing
  • Medical imaging
  • Digital image processing covers the topics of security, image manipulation, image analysis and image segmentation
  • Remote sensing covers the topics of identifying and classifying geo spatial and geological structures
  • Medical imaging covers the topics of disease identification and classification, generate modalities

 

Classification:

  • Process of pattern recognition in computer vision
  • Retrieve conceptual information in images
  • Data is used to assign corresponding levels with respect to groups with same characteristics
  • Classification done with the help of defined features such as density, texture and color in the feature space

Steps of Classification:

  • Define classification classes
  • Selection of features
  • Sampling training data
  • Estimating universal statistics
  • Classification
  • Verification

Sample code Classification:

K-NN Classification:

function test_data = knn(test_data, tr_data,k)

numoftestdata = size(test_data,1);
numoftrainingdata = size(tr_data,1);

for sample=1:numoftestdata

%Step 1: Computing euclidean distance for each testdata
R = repmat(test_data(sample,:),numoftrainingdata,1) ;
euclideandistance = (R(:,1) – tr_data(:,1)).^2;

%Step 2: compute k nearest neighbors and store them in an array[dist position] = sort(euclideandistance,’ascend’);
knearestneighbors=position(1:k);
knearestdistances=dist(1:k);

% Step 3 : Voting
for i=1:k
A(i) = tr_data(knearestneighbors(i),2);
end

M = mode(A);

if (M~=1)
test_data(sample,2) = mode(A);
else
test_data(sample,2) = tr_data(knearestneighbors(1),2);
end
end

SVM Classifier:

imagefiles = dir(‘*.jpg’);
nfiles = 20;

for i = 1:nfiles
currentfilename = imagefiles(i).name;
currentimage = imread(currentfilename);
images{i} = currentimage;
images{i} = im2double(images{i});
images{i} = rgb2gray(images{i});
images{i} = imresize(images{i},[200 200]);
images{i} = reshape(images{i}’, 1, size(images{i},1)*size(images{i},2));
end

trainData = zeros(nfiles, 40000);

for ii=1:nfiles
trainData(ii,:) = images{ii};
end

class = [1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1];
SVMStruct = svmtrain (trainData, class);

inputImg = imread(‘testImg.jpg’);
inputImg = im2double(inputImg);
inputImg = rgb2gray(inputImg);
inputImg = imresize(inputImg, [200 200]);
inputImg = reshape (inputImg’, 1, size(inputImg,1)*size(inputImg,2));
result = svmclassify(SVMStruct, inputImg);