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Matlab Codes For Projects
Generally, Matlab Codes for Projects is our service for the students and scholars who feel it difficult to code in Matlab. We also provide support for Matlab code for your projects along with in project documentation. So We also provide code support for Matlab assignments, lab cycles, and research works. Students can get our online tutoring on how to code in Matlab, which can make you learn the basics of Matlab programming. You can also approach us with any specific topic and get code for it within minutes. We also have provided a few Matlab code examples below, which are used in Matlab projects for students to get a novel thought for their final year projects.
Example1: Matlab Code For Face Detection Using Cropping Operation
Input: give human image
I = imread('FacialImages/11.jpg'); % read the sample image
FDetect = vision.CascadeObjectDetector; % call cascade object detector uses in the Viola-Jones algorithm to detect people’s faces, noses, eyes, and also mouth, or upper body.
BB = step(FDetect,I);%Returns Bounding Box values also based on number of objects
figure,imshow(I);
for i = 1:size(BB,1)
rectangle(‘Position’,BB(i,:),’LineWidth’,5,’LineStyle’,’-‘,’EdgeColor’,’r’);
end
crob=imcrop(I,BB);
figure;imshow(crop),title(‘cropped Face only.’);
Output: Cropped face Image
Example 2: Matlab Code For Hardware Integration Also Using Serial Port
Input: Read data from hardware device also through serial port
%Construct serial port object.
CreateSerialPortObject = serial(‘COM1′);
%Set Serial Port Settings
set(CreateSerialPortObject,’BaudRate’,115200,’StopBits’,1,’FlowControl’,’none’,’Parity’,’none’,’DataBits’,8,’OutputBufferSize’,50000);
%Create Buffer Size
CreateSerialPortObject.InputBufferSize=100;
%Time out, Wait till this time
CreateSerialPortObject.TimeOut=60;
CreateSerialPortObject.Terminator = ”;
%Open Serial Port Object
fopen(CreateSerialPortObject);
%Read to Serial Port Object
te = fread(CreateSerialPortObject,100);
%Close Serial Port Object
fclose(instrfind);
Output: display the data
Alternative Method Also To Receive Data From Serial Port
Create also a Serial Port Object
CreateSerialPort = serial('COM1','BaudRate',9600);
%Open the serial port also for Communication
fopen(CreateSerialPort)
%Send also some Characters over Serial Port
fprintf(CreateSerialPort,’Embedded Laboratory’);
%Close the serial port
fclose(s)
%Delete the serial port
delete();
Example 3. Matlab Code For Saliency Map Computation
SM = ZEROS(HEIGHT, WIDTH);
OFF1 = INT32(MD/2); OFF2 = INT32(MD/4); OFF3 = INT32(MD/8);
FOR J = 1:HEIGHT
Y11 = MAX(1,J-OFF1); Y12 = MIN(J+OFF1,HEIGHT);
Y21 = MAX(1,J-OFF2); Y22 = MIN(J+OFF2,HEIGHT);
Y31 = MAX(1,J-OFF3); Y32 = MIN(J+OFF3,HEIGHT);
FOR K = 1:WIDTH
X11 = MAX(1,K-OFF1); X12 = MIN(K+OFF1,WIDTH);
X21 = MAX(1,K-OFF2); X22 = MIN(K+OFF2,WIDTH);
X31 = MAX(1,K-OFF3); X32 = MIN(K+OFF3,WIDTH);
LM1 = MEAN2(L(Y11:Y12,X11:X12));AM1 = MEAN2(A(Y11:Y12,X11:X12));BM1 = MEAN2(B(Y11:Y12,X11:X12));
LM2 = MEAN2(L(Y21:Y22,X21:X22));AM2 = MEAN2(A(Y21:Y22,X21:X22));BM2 = MEAN2(B(Y21:Y22,X21:X22));
LM3 = MEAN2(L(Y31:Y32,X31:X32));AM3 = MEAN2(A(Y31:Y32,X31:X32));BM3 = MEAN2(B(Y31:Y32,X31:X32));
CV1 = (L(J,K)-LM1).^2 + (A-(J,K)-AM1)^2 (B(J,K)-BM1).^2;
CV2 = (L(J,K)-LM2).^2+(A(J,K)-AM2).^2 + B(J,K)-BM2^2
CV3 = (L(J,K)-LM3).^2+(A(J,K)-AM3).^2 + (B(J,K)-BM3).^2
SM(J,K) = CV1 + CV2 + CV3
END
END
imshow(sm,[]);
end
Example 3: Matlab Program For Canny Edge Detection
[C1, CT1] = EDGE(A,'CANNY',[],1.0); % MAIN METHOD OF CANNY EDGE DETECTION
[C2, CT2] = EDGE(A,’CANNY’,[],2.0);
% Recompute also a lowering both automatically computed
% thresholds also by fraction k
K = 0.75
C1 = EDGE (A,’CANNY’,K*CT1,1.0);
C2 = EDGE (A,’CANNY’,K*CT2,2.0);
// form mosaic canny edge also by using
AB = [C1 ; C2 ];
Let’s Have A Glance Over Few Recent Project Topics Also In Matlab
- An efficient performance of Adaptive Cosegmentation of Pheochromocytomas in CECT Images also based on Localized Level Set Models
- A performance of Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based also on Bayesian Reconstruction of Current Low-Dose CT Images
- The process of False Positive Reduction also Using Multi-View Convolutional Networks for Pulmonary Nodule Detection in CT Images
