Matlab Based Digital Image Processing Projects

Matlab based digital image processing projects are used in human computer interaction for gesture recognition and face recognition. Digital image processing is an improvement of pictorial information for human interpretation Sub field of signals and systems but particularly focuses on images

Implementing Matlab Based Digital Image Processing Projects:

 

1.Matlab has many plot options to represent the process of digital image. Plots ate plot, quiver and pcolor.

2.Matlab should make a large variety of graphs.

3.On concepts of digital image processing need to import data, Matlab will help us to import data from a variety of sources.

 

Features of Matlab:

  • Custom GUIs.
  • Mathematical function libraries.
  • High-level language of technical computing.

Fields of DIP:

  • Computer Vision.
  • Artificial Intelligence.
  • Computer Graphics.
  • Signal Processing.

 

ADVANTAGES OF MATLAB BASED DIGITAL IMAGE PROCESSING PROJECTS:

 

1.Images are easily stored in computer memory and it can be retrieved on same set of environment

2.To correct image density and contrast manipulate the pixel shades

3.Provide electronic transmission of images to third-party providers

 

Methods of Image Restoration:

1.Inverse Filtering.

2.Geometric Transform.

3.Weiner Filter.

4.Interactive Restoration.

 

Example Code for Matlab Based Digital Image Processing Projects:

fs = 10000;
t = 0:1/fs:1.5;
x1 = sawtooth(2*pi*50*t);
x2 = square(2*pi*50*t);
subplot(211),plot(t,x1), axis([0 0.2 -1.2 1.2])
xlabel(‘Time (sec)’);
ylabel(‘Amplitude’);
title(‘Sawtooth Periodic Wave’);
subplot(212)
plot(t,x2)
axis([0 0.2 -1.2 1.2]);
xlabel(‘Time (sec)’);
ylabel(‘Amplitude’);
title(‘Square Periodic Wave’);

Example Code for Matlab Based Digital Image Processing Projects:
Read an Image
I = im2double(imread(‘cameraman.tif’));
imshow(I);
title(‘Original Image (courtesy of MIT)’);
Simulate a motion blur
LEN = 21;
THETA = 11;
PSF = fspecial(‘motion’, LEN, THETA);
blurred = imfilter(I, PSF, ‘conv’, ‘circular’);
imshow(blurred);
title(‘Blurred Image’);
Restore Blurred Image
wnr1 = deconvwnr(blurred, PSF, 0);
imshow(wnr1);
title(‘Restored Image’);

Example Code for Matlab Based Digital Image Processing Projects:
Read an image and conversion process
rgb = imread(‘pears.png’);
I = rgb2gray(rgb);
imshow(I)

text(732,501,’Image courtesy of Corel(R)’,…
‘FontSize’,7,’HorizontalAlignment’,’right’)
Use Gradient magnitude as segmentation function
hy = fspecial(‘sobel’);
hx = hy’;
Iy = imfilter(double(I), hy, ‘replicate’);
Ix = imfilter(double(I), hx, ‘replicate’);
gradmag = sqrt(Ix.^2 + Iy.^2);
figure
imshow(gradmag,[]), title(‘Gradient magnitude (gradmag)’)
Mark Foreground Object
se = strel(‘disk’, 20);
Io = imopen(I, se);
figure
imshow(Io), title(‘Opening (Io)’)
Compute Watershed Transformation of Segmented Function
gradmag2 = imimposemin(gradmag, bgm | fgm4);
Finally we are ready to compute the watershed-based segmentation.
L = watershed(gradmag2);