Matlab Feature Extraction Using Segmentation And Edge Detection

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.

4. imshow(I);

5. BW1 = edge(I,'sobel');

6. BW2 = edge(I,'canny');

7. figure, imshow(BW1) ;

9. imshow(I);

10. BW1 = edge(I,'sobel');

11. BW2 = edge(I,'canny');

12. figure, imshow(BW1) ;

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('xrime'); >> 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.

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.