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

    • Matlab:Feature Extraction Using Segmentation and Edge Detection
    • Detecting Edges Using the edge Function
      In an image, an edge is a curve that follows a path of rapid change in image intensity.
    • Detecting Edges Using the edge Function
      • I = imread(‘dmt.jpg');
      • imshow(I);
      • BW1 = edge(I,'sobel');
      • BW2 = edge(I,'canny');
      • figure, imshow(BW1) ;
      • figure, imshow(BW2)
    • Detecting Edges Using the edge Function
      • I = imread(‘bird.png');
      • imshow(I);
      • BW1 = edge(I,'sobel');
      • BW2 = edge(I,'canny');
      • figure, imshow(BW1) ;
      • figure, imshow(BW2)
    • Detecting Edges Using the edge Function
    • Radon Transform
      The radon function computes projections of an image matrix along specified directions.
    • Radon Transform
      >> I=zeros(100,100);
      >> I(40:60, 40:60)=1;
      >> imshow(I);
      >> [R,xp] = radon(I,0);
      >> figure,plot(xp,R);
    • Radon Transform
      >> I=zeros(100,100);
      >> I(40:60, 40:60)=1;
      >> imshow(I);
      >> [R,xp] = radon(I,45);
      >> figure,plot(xp,R);
    • 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.
    • 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.
    • 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);
    • Inverse Radon Transform
    • 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.
    • Marker-Controlled Watershed Segmentation
      • Step 1: Read in the Color Image and Convert it to Grayscale
      • Step 2: Use the Gradient Magnitude as the Segmentation Function
      • Step 3: Mark the Foreground Objects
      • Step 4: Compute Background Markers
      • Step 5: Compute the Watershed Transform of the Segmentation Function.
      • Step 6: Visualize the Result
    • Marker-Controlled Watershed Segmentation