DIP Projects Using Matlab

DIP projects using matlab based on matlab built-in functions used for easiest way of computation. Matlab should provide large number of standard elementary mathematical functions and also some other application domain functions.

Applications of DIP Projects Using Matlab:

1.Symbolic Computation.

2.Engineering Graphics and Scientific Visualization.

3.Data Manipulation.

4.Simulation and Prototyping.

DIP used in Law Enforcement:

1.Fingerprint Recognition

2.Number plate recognition for automated toll systems

3.Enhancement of CCTV images

Tasks of DIP:

1.Multi-Scale Signal Analysis.

2.Feature Extraction.

3.Pattern Recognition.

Image Enhancement Techniques:

1.Spatial Filtering.

2.Color Image Processing.

3.Histogram Processing.

4.Basic Intensity Functions.

Implementing DIP Projects Using Matlab:

Digital image can be optimized for application by enhancing for altering the appearances of structures within it. Usually digital images are invisible it will need more output devices for viewing an image

Example Code for Measuring Regions in Grayscale Images:

Create Synthetic image
I = propsSynthesizeImage;
imshow(I)
title(‘Synthetic Image’)
Create a Binary Image
BW = I > 0;
imshow(BW)
title(‘Binary Image’)
Calculate Object Properties
s = regionprops(BW, I, {‘Centroid’,’WeightedCentroid’});
imshow(I)
title(‘Weighted (red) and Unweighted (blue) Centroids’);
hold on
numObj = numel(s);
for k = 1 : numObj
plot(s(k).WeightedCentroid(1), s(k).WeightedCentroid(2), ‘r*’);
plot(s(k).Centroid(1), s(k).Centroid(2), ‘bo’);
end
hold off

Example Code for Block Processing:

file_name = ‘cameraman.tif’;
I = imread(file_name);
normal_edges = edge(I,’canny’);
imshow(I);
title(‘Original Image’);
figure
imshow(normal_edges);
title(‘Conventional Edge Detection’);
edgeFun = @(block_struct) edge(block_struct.data,’canny’);
block_size = [50 50];
block_edges = blockproc(file_name,block_size,edgeFun);
figure
imshow(block_edges);
title(‘Block Processing – Simplest Syntax’);

Example Code for Spatial Filtering:

%% Read Image
X = imread(‘(1).jpg’);
X = rgb2gray(X);
%% Spatial Filtering Smoothing &High Boost Filtering
favg = [1/16 2/16 1/16; 2/16 4/16 2/16; 1/16 2/16 1/16] filtim = imfilter(X,favg,’symmetric’, ‘conv’);
subplot(2,2,1)
imshow(X);
title(‘Original’);
subplot(2,2,2)
imshow(filtim);
title(‘Weighted Filtering’);
subplot(2,2,3)
imshow(X-filtim);
title(‘Difference Image’);
subplot(2,2,4)
imshow(X+0.2.*(X-filtim));
title(‘High Boost Sharpened’);