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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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### Transcript

• 1. Matlab:Feature Extraction Using Segmentation and Edge Detection<br />
• 2. Detecting Edges Using the edge Function<br />In an image, an edge is a curve that follows a path of rapid change in image intensity.<br />
• 3. Detecting Edges Using the edge Function<br /><ul><li>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)</li></li></ul><li>Detecting Edges Using the edge Function<br /><ul><li>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)</li></li></ul><li>Detecting Edges Using the edge Function<br />
• 14. Radon Transform<br />The radon function computes projections of an image matrix along specified directions.<br />
• 15. Radon Transform<br />&amp;gt;&amp;gt; I=zeros(100,100);<br />&amp;gt;&amp;gt; I(40:60, 40:60)=1;<br />&amp;gt;&amp;gt; imshow(I);<br />&amp;gt;&amp;gt; [R,xp] = radon(I,0);<br />&amp;gt;&amp;gt; figure,plot(xp,R);<br />
• 16. Radon Transform<br />&amp;gt;&amp;gt; I=zeros(100,100);<br />&amp;gt;&amp;gt; I(40:60, 40:60)=1;<br />&amp;gt;&amp;gt; imshow(I);<br /> &amp;gt;&amp;gt; [R,xp] = radon(I,45);<br />&amp;gt;&amp;gt; figure,plot(xp,R);<br />
• 17. Inverse Radon Transform<br />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.<br />
• 18. Inverse Radon Transform<br />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.<br />
• 19. Inverse Radon Transform<br />&amp;gt;&amp;gt; P=phantom(256);<br />&amp;gt;&amp;gt; imshow(P)<br />&amp;gt;&amp;gt; theta1 = 0:10:170; [R1,xp] = radon(P,theta1);<br />theta2 = 0:5:175; [R2,xp] = radon(P,theta2);<br />theta3 = 0:2:178; [R3,xp] = radon(P,theta3);<br />&amp;gt;&amp;gt; figure, imagesc(theta3,xp,R3); colormap(hot); colorbar<br />xlabel(&amp;apos; heta&amp;apos;); ylabel(&amp;apos;xprime&amp;apos;);<br />&amp;gt;&amp;gt; I1 = iradon(R1,10);<br />I2 = iradon(R2,5);<br />I3 = iradon(R3,2);<br />imshow(I1);<br />figure, imshow(I2);<br />figure, imshow(I3);<br />
• 20. Inverse Radon Transform<br />
• 21. Marker-Controlled Watershed Segmentation<br />Separating touching objects in an image is one of the more difficult image processing operations. The watershed transform is often applied to this problem.<br />
• 22. Marker-Controlled Watershed Segmentation<br /><ul><li>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</li></li></ul><li>Marker-Controlled Watershed Segmentation<br />