1. A picture is worth more than a 1000 words. It can save a life. Arjun Watane

3. Gaussian Derivative I = imread('brain_tumor_mri_1.jpg'); I2 = rgb2gray(I); k = fspecial('gaussian', [7 7], 1); %Gaussian filter kernal kdx = conv2(k,[1 0 -1], 'valid'); %figure; surf(kdx); kdy = conv2(k, [1; 0; -1], 'valid'); %figure; surf(kdy); imx = conv2(I2, kdx, 'valid'); imy = conv2(I2, kdy, 'valid'); figure; imshow(I2); %figure; imshow(imx); figure; imshow(imy); imwrite(imy, 'brainTumorMRI1_GaussianDerivative.jpg');

4. Gaussian Derivative

5. Edge Detector 6 edge-finding methods – Sobel – Prewitt – Roberts – Laplacian – Zero-Cross – Canny Tested on Groceries and a Brain MRI

6. Edge Detection on Groceries I5 = imread('groceries.jpg'); IBW = rgb2gray(I5); BW = edge(IBW, 'prewitt'); figure; imshow(BW); Changed “groceries.jpg” with brain_mri_1. Changed “prewitt” with sobel, canny, roberts, Log, and zerocross.

7. Prewitt Edge Detection on Groceries

8. Canny Edge Detection on Groceries

9. Roberts Edge Detection on Groceries

10. Sobel Edge Detection on Groceries

11. Log Edge Detection on Groceries

12. Zerocross Edge Detection on Groceries

13. Edge Detection on Brain MRI (Tumor Detection) Prewitt ZeroCrossLoGSobel Roberts Canny

14. Adaboost Pgm files work better. Found online jpg to pgm converter.

15. Adaboost Face Detection

18. Harris Corner Detector im = imread('groceries.jpg'); im = rgb2gray(im); k = fspecial('gaussian', [15 15], 1); dx =[-1 0 1; -1 0 1; -1 0 1];%Derivative Masks dy = dx'; %transpose x to make y kdx = conv2(im, dx, 'valid'); %Image Derivatives kdy = conv2(im, dy, 'valid'); kdx2 = kdx.^2; %square every number in the matrix kdy2 = kdy.^2; kdxy = (kdx.*kdy); %multiply every number in the matrix with each other kdx2 = conv2(kdx2, k, 'same'); kdy2 = conv2(kdy2, k, 'same'); kdxy = conv2(kdxy, k, 'same'); H = [kdx2 kdxy; kdxy kdy2]; M = (kdx2.*kdy2 - kdxy.^2) -.04*(kdx2 + kdy2).^2; %Harris Corner Measure Equation imshow(M); imwrite(M, 'groceriesHarrisCorner.jpg');

19. Harris Corner Detector

20. SVM

22. Bag of Features

23. Optical Flow

25. SIFT – Plot Descriptors pfx = fullfile(vl_root, 'data', 'obama3.jpg'); I = imread(pfx); image(I); I = single(rgb2gray(I)); [f,d] = vl_sift(I); perm = randperm(size(f,2)); sel = perm(1:4);%4 represents the # of features h1 = vl_plotframe(f(:,sel)) ; h2 = vl_plotframe(f(:,sel)) ; set(h1,'color','k','linewidth',3) ; set(h2,'color','y','linewidth',2) ; h3 = vl_plotsiftdescriptor(d(:,sel),f(:,sel)) ; set(h3,'color','g') ;

26. SIFT – Plot Descriptors

28. SIFT – Match Descriptor Points pfx = fullfile(vl_root, 'data', 'obama1.jpg'); %receives, reads, grayscales, and resizes the image from the vl_root directory I = imread(pfx); figure; imshow(I); Ia = single(rgb2gray(I)); Ia = imresize(Ia, [300 300]); pfx = fullfile(vl_root, 'data', 'obama3.jpg'); I = imread(pfx); figure; imshow(I); Ib = single(rgb2gray(I)); Ib = imresize(Ib, [300 300]); [fa, da] = vl_sift(Ia); %calculate sift points [fb, db] = vl_sift(Ib); [matches, scores] = vl_ubcmatch(da, db); %matches the points on the images m1 = fa(1:2, matches(1,:)); m2 = fb(1:2, matches(2,:)); m2(1, :) = m2(1,:)+size(Ia,2)*ones(1,size(m2,2)); X = [m1(1,:); m2(1,:)]; Y = [m1(2,:); m2(2,:)]; c = [Ia Ib]; figure; imshow(c,[]); hold on; line(X(:,1:1:15), Y(:,1:1:15)) %draw lines

29. SIFT – Match Descriptor Points