Matlab PHD projects are created and implemented for research scholars according to their objective on several sub domains concepts are developed integrated into other languages using matlab simulation tool. Matlab PHD projects are implemented comes under the concepts of security, object detection, segmentation and classifciation,

 

Areas of Matlab PHD Projects:

  • Digital Image Processing
  • Remote Sensing
  • Medical Imaging
  • Pattern Analysis & Machine Intelligence
  • Biometrics

 

Algorithms used in PhD Matlab Projects

Gray Level Co-occurrence Matrix (GLCM)

Hidden Markov Model Algorithm (HMMA)

Advanced Encryption Standard (AES) Algorithm

Fuzzy Clustering Algorithm

Object Detection Algorithm

Shadow Detection Algorithm

Histogram Equalization

Semantic Retrieval Algorithm

Video Annotation Algorithm

Applications of PhD Matlab Projects

Voice Dialing

Scene Object Identification

Machine Learning

Industrial Applications

Signal Processing

Computer Graphics

Pattern Recognition

Registration Techniques

Computational Biology and Parallel Computing

Support for Matlab PhD Projects

Matlab supports to accept several input image formats such as GIF, Tiff, PGM, FITS, JPEG and PNG. Communication related projects are developed using matlab, communication fields are seismic, speech recognition, optics and astronomy. Matlab features are used to develop efficient project, features are OOP, file I/O functions, string processing and creation of graphics .Different matlab functions such as nested functions, local functions, base and function workspace help us to create algorithms more effective.

Read Image

I = imread(‘board.tif’);

figure, imshow(I);

Create labels with floating point number

label_str = cell(3,1);conf_val = [85.212 98.76 78.342];   for ii=1:3    label_str{ii} = [‘Confidence: ‘ num2str(conf_val(ii),’%0.2f’) ‘%’];end

Set the Position

position = [23 373 60 66;35 185 77 81;77 107 59 26];

Insert Annotation

RGB = insertObjectAnnotation(I, ‘rectangle’, position, label_str,’TextBoxOpacity’, 0.9, ‘FontSize’, 18);

Display Annotated Image

figure, imshow(RGB), title(‘Annotated chips’);

Read Image

I = imread(‘cell.tif’);figure, imshow(I), title(‘original image’);text(size(I,2),size(I,1)+15, …    ‘Image courtesy of Alan Partin’, …    ‘FontSize’,7,’HorizontalAlignment’,’right’);text(size(I,2),size(I,1)+25, ….    ‘Johns Hopkins University’, …    ‘FontSize’,7,’HorizontalAlignment’,’right’);

Detect Entire cell

[~, threshold] = edge(I, ‘sobel’);fudgeFactor = .5;BWs = edge(I,’sobel’, threshold * fudgeFactor);figure, imshow(BWs), title(‘binary gradient mask’);

Dilate Image

se90 = strel(‘line’, 3, 90);se0 = strel(‘line’, 3, 0);

Fill Interior Gaps

BWdfill = imfill(BWsdil, ‘holes’);figure, imshow(BWdfill);title(‘binary image with filled holes’);

Remove Connected Objects on Border

BWnobord = imclearborder(BWdfill, 4);figure, imshow(BWnobord), title(‘cleared border image’);

Smoothen the Object

seD = strel(‘diamond’,1);BWfinal = imerode(BWnobord,seD);BWfinal = imerode(BWfinal,seD);figure, imshow(BWfinal), title(‘segmented image’);