Matlab Feature Extraction Using Segmentation And Edge Detection
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This document discusses various image processing techniques in Matlab including edge detection, the radon transform, inverse radon transform, and marker-controlled watershed segmentation. It provides code examples for detecting edges using the edge function, computing projections of an image using the radon transform, reconstructing images from projections using the inverse radon transform, and segmenting touching objects using marker-controlled watershed segmentation.
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![Radon Transform>> I=zeros(100,100);>> I(40:60, 40:60)=1;>> imshow(I);>> [R,xp] = radon(I,0);>> figure,plot(xp,R);](https://image.slidesharecdn.com/matlabfeatureextractionusingsegmentationandedgedetection-100110034652-phpapp02/85/Matlab-Feature-Extraction-Using-Segmentation-And-Edge-Detection-15-320.jpg)
![Radon Transform>> I=zeros(100,100);>> I(40:60, 40:60)=1;>> imshow(I); >> [R,xp] = radon(I,45);>> figure,plot(xp,R);](https://image.slidesharecdn.com/matlabfeatureextractionusingsegmentationandedgedetection-100110034652-phpapp02/85/Matlab-Feature-Extraction-Using-Segmentation-And-Edge-Detection-16-320.jpg)
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Matlab: Gui
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Matlab: Linear Methods, Quantilesmatlab Content This document discusses quantiles and quantile functions in Matlab. It defines quantiles as points taken at regular intervals from a cumulative distribution function that divide ordered data into essentially equal subsets. It lists some common quantile names like median, tertiles, quartiles, quintiles, deciles, and percentiles. It provides an example quantile function in Matlab that returns quantiles of input data values at specified cumulative probability values.
Matlab Data And Statistics
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Matlab Importing Data
Matlab Importing Datamatlab Content This document provides steps for importing text files into MATLAB: save the data in a text file, open the import data window in MATLAB and select the file, select values in the import wizard, finalize the import, and then view the imported data in the MATLAB workspace. It also lists several methods for importing data into MATLAB such as entering it at the command prompt, loading it from an ASCII flat file, or using MATLAB I/O functions. The document concludes by recommending visiting additional self-help tutorials on the website www.dataminingtools.net.
Matlab Organizing Data
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Cell arrays allow different data types to be stored in containers called cells. Function handles pass functions as inputs to other functions. Structures store selected data together in fields, with each field containing an array of a Matlab data type.
Matlab Text Files
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Matlab Visualizing Data
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Matlab Working With Images
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Matlab: Non Linear Methods
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Matlab: Procedures And Functions
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Matlab: Programming Environment
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Matlab: Saving And Publishing
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- 1. Matlab:Feature Extraction Using Segmentation and Edge Detection
- 2. Detecting Edges Using the edge FunctionIn an image, an edge is a curve that follows a path of rapid change in image intensity.
- 3. Detecting Edges Using the edge FunctionI = imread(‘dmt.jpg');
- 4. imshow(I);
- 8. figure, imshow(BW2)Detecting Edges Using the edge FunctionI = imread(‘bird.png');
- 9. imshow(I);
- 13. figure, imshow(BW2)Detecting Edges Using the edge Function
- 14. Radon TransformThe 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 TransformThe 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 TransformP = 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); colorbarxlabel('\theta'); ylabel('x\prime');>> I1 = iradon(R1,10);I2 = iradon(R2,5);I3 = iradon(R3,2);imshow(I1);figure, imshow(I2);figure, imshow(I3);
- 21. Marker-Controlled Watershed SegmentationSeparating 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 SegmentationStep 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 ResultMarker-Controlled Watershed Segmentation