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Matlab Feature Extraction Using Segmentation And Edge Detection
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Matlab Feature Extraction Using Segmentation And Edge Detection

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Matlab Feature Extraction Using Segmentation And Edge Detection

Matlab Feature Extraction Using Segmentation And Edge Detection

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  • 1. Matlab:Feature Extraction Using Segmentation and Edge Detection
  • 2. Detecting Edges Using the edge Function
    In an image, an edge is a curve that follows a path of rapid change in image intensity.
  • 3. Detecting Edges Using the edge Function
    • I = imread(‘dmt.jpg');
    • 4. imshow(I);
    • 5. BW1 = edge(I,'sobel');
    • 6. BW2 = edge(I,'canny');
    • 7. figure, imshow(BW1) ;
    • 8. figure, imshow(BW2)
  • Detecting Edges Using the edge Function
    • I = imread(‘bird.png');
    • 9. imshow(I);
    • 10. BW1 = edge(I,'sobel');
    • 11. BW2 = edge(I,'canny');
    • 12. figure, imshow(BW1) ;
    • 13. figure, imshow(BW2)
  • Detecting Edges Using the edge Function
  • 14. Radon Transform
    The radon function computes projections of an image matrix along specified directions.
  • 15. Radon Transform
    >> I=zeros(100,100);
    >> I(40:60, 40:60)=1;
    >> imshow(I);
    >> [R,xp] = radon(I,0);
    >> figure,plot(xp,R);
  • 16. Radon Transform
    >> I=zeros(100,100);
    >> I(40:60, 40:60)=1;
    >> imshow(I);
    >> [R,xp] = radon(I,45);
    >> figure,plot(xp,R);
  • 17. Inverse Radon Transform
    The iradon function inverts the Radon transform and can therefore be used to reconstruct images. iradon reconstructs an image from parallel-beam projections. In parallel-beam geometry, each projection is formed by combining a set of line integrals through an image at a specific angle.
  • 18. Inverse Radon Transform
    P = phantom(def, n) generates an image of a head phantom that can be used to test the numerical accuracy of radon and iradon or other two-dimensional reconstruction algorithms.
  • 19. Inverse Radon Transform
    >> P=phantom(256);
    >> imshow(P)
    >> theta1 = 0:10:170; [R1,xp] = radon(P,theta1);
    theta2 = 0:5:175; [R2,xp] = radon(P,theta2);
    theta3 = 0:2:178; [R3,xp] = radon(P,theta3);
    >> figure, imagesc(theta3,xp,R3); colormap(hot); colorbar
    xlabel(' heta'); ylabel('xprime');
    >> I1 = iradon(R1,10);
    I2 = iradon(R2,5);
    I3 = iradon(R3,2);
    imshow(I1);
    figure, imshow(I2);
    figure, imshow(I3);
  • 20. Inverse Radon Transform
  • 21. Marker-Controlled Watershed Segmentation
    Separating touching objects in an image is one of the more difficult image processing operations. The watershed transform is often applied to this problem.
  • 22. Marker-Controlled Watershed Segmentation
    • Step 1: Read in the Color Image and Convert it to Grayscale
    • 23. Step 2: Use the Gradient Magnitude as the Segmentation Function
    • 24. Step 3: Mark the Foreground Objects
    • 25. Step 4: Compute Background Markers
    • 26. Step 5: Compute the Watershed Transform of the Segmentation Function.
    • 27. Step 6: Visualize the Result
  • Marker-Controlled Watershed Segmentation