Matlab Training

Matlab training videos has built-in functions for solving problems requiring signal processing, optimization and data analysis. Matlab originally designed for solving linear algebra type problems using matrices Matlab contains functions for 2D and 3D graphics and animation

Integrate matlab code with other languages and applications and distribute matlab algorithms and applications.

 

Advantages of Matlab Training:

1.Quick plotting and analysis.

2.Handles vector and matrices very nice.

3.Lots of pleasant functions (fuzzy logic, neural nets, FFT, numerical integration) are used.

Matlab Training helps you to quickly understand the concept whats behind it and help you to achieve complete project quickly.

 

Features of Matlab Training:

1.Development environment for managing code, files and data

2.Tools for building custom graphical user interface

3.Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization and numerical interfaces

Uses of Matlab:

1.Matlab is more helpful for creating complex equations

2.Create function handlers should supply the function handlers with the values to substitute into the variables

3.Using matlab syntax to customize plots

4.Matlab commands are used to find minimum and maximum values

 

 

clear;
% Threshold level parameter alfa:
alfa=0.1;% less than 1/3

[x,map]=gifread(‘lena.gif’);
ix=ind2gray(x,map);
I_max=max(max(ix));
I_min=min(min(ix));
level1=alfa*(I_max-I_min)+I_min;
level2=2*level1;
level3=3*level1;
thix1=max(ix,level1.*ones(size(ix)));
thix2=max(ix,level2.*ones(size(ix)));
thix3=max(ix,level3.*ones(size(ix)));
figure(1);colormap(gray);
subplot(2,2,1);imagesc(ix);title(‘lena’);
subplot(2,2,2);imagesc(thix1);title(‘threshold one alfa’);
subplot(2,2,3);imagesc(thix2);title(‘threshold two alfa’);
subplot(2,2,4);imagesc(thix3);title(‘threshold three alfa’);

Import Video and initialize foreground detector:
foregroundDetector = vision.ForegroundDetector (‘NumGaussians’, 3, ..’NumTrainingFrames’, 50);
videoReader=vision.videoFileReader (‘videtraffic.avi’);
for i=1:50
frame = step (videoReader); %read the next video frame
foreground = step (foregroundDetector, frame);
end
Detect cars in an initial video Frame
se = stre1 (‘square’, 3);
filteredForeground = imopen (foreground, se);
figure;
imshow (filteredForeground);
title (‘Clean foreground’);
result = insertShape (frame, Rectangle’, bbox, ‘Color’, ‘green’);