a picture is worth more than a 1000 words. it can save a life. arjun watane
Post on 18-Jan-2016
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A picture is worth more than a 1000 words. It can save a life.
Arjun Watane
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');
Gaussian Derivative
Edge Detector
• 6 edge-finding methods– Sobel– Prewitt– Roberts– Laplacian– Zero-Cross– Canny
• Tested on Groceries and a Brain MRI
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.
Prewitt Edge Detection on Groceries
Canny Edge Detection on Groceries
Roberts Edge Detection on Groceries
Sobel Edge Detection on Groceries
Log Edge Detection on Groceries
Zerocross Edge Detection on Groceries
Edge Detection on Brain MRI (Tumor Detection)
Prewitt
ZeroCrossLoGSobel
RobertsCanny
Adaboost
• Pgm files work better. • Found online jpg to pgm converter.
Adaboost Face Detection
Adaboost Face Detection
Adaboost Face Detection
Harris Corner Detectorim = imread('groceries.jpg');im = rgb2gray(im);k = fspecial('gaussian', [15 15], 1); dx =[-1 0 1; -1 0 1; -1 0 1];%Derivative Masksdy = dx'; %transpose x to make y kdx = conv2(im, dx, 'valid'); %Image Derivativeskdy = conv2(im, dy, 'valid'); kdx2 = kdx.^2; %square every number in the matrixkdy2 = 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');
Harris Corner Detector
SVM
SVM
Bag of Features
Optical Flow
Optical Flow
SIFT – Plot Descriptorspfx = 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 featuresh1 = 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') ;
SIFT – Plot Descriptors
SIFT – Plot Descriptors
SIFT – Match Descriptor Pointspfx = fullfile(vl_root, 'data', 'obama1.jpg'); %receives, reads, grayscales, and resizes the
image from the vl_root directoryI = 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 imagesm1 = 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
SIFT – Match Descriptor Points
SIFT – Match Descriptor Points
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