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Introduction to Computer Vision Lecture 07 Roger S. Gaborski GaborskiRoger S. Roger S. Roger S. Gaborski Gaborski 1 Linear Filtering in MATLAB g = imfilter(f, w, filtering mode, boundary, size) • filters the imput image f with the filter mask w. – f is input image. It can be of any class (logical/numeric) and dimension. – g is output image • filter mode: - 'corr' : correlation, and default mode - 'conv' : convolution Roger S. Gaborski 2 Parameters • g = imfilter(f, w, filtering mode, boundary, size) Boundary options -X pad boundary with value X. Default X = 0. - 'symmetric' symmetric padding - 'replicate' replicate padding - 'circular' circular padding Size options - 'same' g is the same size of f (default mode) - 'full' g is full filtered by w, so size of g is increased Roger S. Gaborski 3 MATLAB function for filtering: imfilter • g = imfilter(f, w, ‘replicate’) • Correlation is the default filtering mode. • If filters are pre-rotated 180 degrees, can simply use default(corr) for convolution • If filter is symmetric, doesn’t matter Roger S. Gaborski 4 Simple First Derivative Approximation Difference 1 1 1 1 0 x 1 1 1 = 0 0 0 -1 Smoothing -1 -1 -1 Roger S. GaborskiRoger S. Gaborski 5 Rotate Filter 1 1 0 1 0 -1 0 1 1 1 0 -1 1 0 -1 -1 0 1 0 -1 -1 1 0 -1 -1 -1 0 Sensitive to edges at different orientations Roger S. GaborskiRoger S. Gaborski 6 Sobel Filter • Consider unequal weights for smoothing operation 1 1 2 1 0 x 1 2 1 = 0 0 0 = Sy -1 -1 -2 -1 Roger S. GaborskiRoger S. Gaborski 7 Sobel Filter • Consider unequal weights for smoothing operation 1 -1 0 1 2 x -1 0 -1 = -2 0 2 = Sx 1 -1 0 1 Roger S. GaborskiRoger S. Gaborski 8 House Image >> I = imread('house1.jpg'); >> figure, imshow(I) >> Jg = rgb2gray(J); >> Jg = im2double(Jg); >> figure, imshow(Jg) Roger S. GaborskiRoger S. Gaborski 9 Horizontal Edge Detection >> f = fspecial('sobel') f= 1 2 1 0 0 0 -1 -2 -1 >> imEdge = imfilter(Jg, f ); >> figure, imshow(imEdge,[ ]); Roger S. GaborskiRoger S. Gaborski 10 >> figure, imshow(abs(imEdge),[]) Roger S. GaborskiRoger S. Gaborski 11 >> figure, hist(abs(imEdge(:)),100) Roger S. GaborskiRoger S. Gaborski 12 figure, imshow(imEdge>.5) Roger S. GaborskiRoger S. Gaborski 13 Vertical Edges >> fv = f' fv = 1 0 -1 2 0 -2 1 0 -1 >> imEdgeV = imfilter(Jg, fv ); >> figure, imshow(imEdgeV,[]); Roger S. GaborskiRoger S. Gaborski 14 Both Vertical and Horizontal Edges figure, subplot(1,2,1),imshow(abs(Eh)>.5) >> subplot(1,2,2),imshow(abs(Ev)>.5) Roger S. GaborskiRoger S. Gaborski 15 figure, imshow((abs(Eh)+abs(Ev))/2,[]) Roger S. GaborskiRoger S. Gaborski 16 build1.jpg Roger S. GaborskiRoger S. Gaborski 17 Edge image Roger S. GaborskiRoger S. Gaborski 18 ~ Operator Roger S. GaborskiRoger S. Gaborski 19 Is there a better way to remove noise than the simple Sobel approach? Roger S. GaborskiRoger S. Gaborski 20 Canny • Image is smoothed with 2D Gaussian Function • Canny uses two thresholds – One threshold detects strong edges – Second threshold detects weak edges connected to strong edges Roger S. GaborskiRoger S. Gaborski 21 RECALL: 2D Gaussian Distribution • The two-dimensional Gaussian distribution is defined by: • From this distribution, can generate smoothing masks whose width depends upon the standard deviation, : Roger S. GaborskiRoger S. Gaborski 22 Sigma Determines Spread of Filter Variance, 2 = .25 Variance, 2 = 4.0 Roger S. GaborskiRoger S. Gaborski 23 Creating Gaussian Kernels • The mask weights are evaluated from the Gaussian distribution: i2 + j2 W(i,j) = k * exp (- ) 2 2 • This can be rewritten as: W(i,j) i2 + j2 = exp (- ) k 2 2 Roger S. GaborskiRoger S. Gaborski 24 Example • Choose = 2. and n = 7, then: 2 j -3 -2 -1 0 1 2 3 -3 -2 -1 i 0 1 2 3 Roger S. GaborskiRoger S. Gaborski 25 7x7 Gaussian Filter Roger S. GaborskiRoger S. Gaborski 26 Building1 gray level image Roger S. GaborskiRoger S. Gaborski 27 31x31Gaussian, sigma = 3 Roger S. GaborskiRoger S. Gaborski 28 63x63 Gaussian, sigma = 10 Roger S. GaborskiRoger S. Gaborski 29 Canny Edge Detector Roger S. GaborskiRoger S. Gaborski 30 Implement Canny using MATLAB edge Function EDGE Find edges in intensity image. EDGE takes an intensity or a binary image I as its input, and returns a binary image BW of the same size as I, with 1's where the function finds edges in I and 0's elsewhere. EDGE supports six different edge-finding methods: The Sobel method finds edges using the Sobel approximation to the derivative. The Prewitt method finds edges using the Prewitt approximation to the derivative. The Roberts method finds edges using the Roberts approximation to the derivative. The Laplacian of Gaussian method finds edges by looking for zero crossings after filtering I with a Laplacian of Gaussian filter. The zero-cross method finds edges by looking for zero crossings after filtering I with a filter you specify. The Canny method finds edges by looking for local maxima of the gradient of I. The gradient is calculated using the derivative of a Gaussian filter. The method uses two thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges. This method is therefore less likely than the others to be "fooled" by Roger noise, and more likely to detectS. GaborskiRoger S. Gaborski true weak edges. 31 [g,t]=edge(im,'canny',[ ],.5) [ ] : Edge function determines the two thresholds CHANGING SIGMA OF GAUSSIAN, in this example = .5 Roger S. GaborskiRoger S. Gaborski 32 [g,t]=edge(im,'canny',[ ],1) CHANGING SIGMA OF GAUSSIAN Roger S. GaborskiRoger S. Gaborski 33 [g,t]=edge(im,'canny',[ ],2) CHANGING SIGMA OF GAUSSIAN Roger S. GaborskiRoger S. Gaborski 34 [g,t]=edge(im,'canny',[ ],3) CHANGING SIGMA OF GAUSSIAN Roger S. GaborskiRoger S. Gaborski 35 [g,t]=edge(im,'canny',[ ],5) CHANGING SIGMA OF GAUSSIAN Roger S. GaborskiRoger S. Gaborski 36 Second Derivative Edge Detection Methods • Second derivative is noisy • First smooth the image, then apply second derivative • Consider – Effect of smoothing filter. – Gaussian: Roger S. GaborskiRoger S. Gaborski 37 RECALL: Second Derivative Approximation • Discrete version of 2nd Partial Derivation of f(x,y) in x direction is found by taking the difference of Eq1 and Eq2: • 2 f(x,y) / x2 = Eq1-Eq2 = 2f(x,y)-f(x-1,y)-f(x+1,y) In y direction: • 2 f(x,y) / y2 = Eq3-Eq4 = 2f(x,y)-f(x,y-1)-f(x,y+1) Roger S. GaborskiRoger S. Gaborski 38 Derivative Approximation for Laplacian f(x-1, y-1) f(x, y-1) f(x+1, y-1) f(x-1, y) f(x, y) f(x+1, y) f(x-1, y+1) f(x, y+1) f(x+1, y+1) Roger S. GaborskiRoger S. Gaborski 39 Laplacian • Independent of edge orientation • Combine 2 f(x,y)/ x2 and 2 f(x,y)/ y2 =4 f(x,y) - f(x-1,y) – f(x+1,y) – f(x,y-1) – f(x,y+1) 0 -1 0 -1 4 -1 0 -1 0 Roger S. GaborskiRoger S. Gaborski 40 LoG • Laplacian of the Gaussian is a circularly symmetric operator. • Second derivative is linear operation Therefore: convolving an image with 2 G(x,y) is the same as first convolving first with smoothing filter (Gaussian) then computing Laplacian of result. • Edge are location of zero crossings Roger S. GaborskiRoger S. Gaborski 41 LoG Also called the Mexican hat operator. Roger S. GaborskiRoger S. Gaborski 42 2 Controls of the Size of the Filter 2 = 0.5 2 = 2.0 Roger S. GaborskiRoger S. Gaborski 43 Human Visual System Receptive Field Approximation 17 x 17 5x5 Roger S. GaborskiRoger S. Gaborski 44 LoG (7x7, sigma = 2) Roger S. GaborskiRoger S. Gaborski 45 LoG (15x15, sigma = 4) Roger S. GaborskiRoger S. Gaborski 46 Summarizing: Laplacian of Gaussian (LoG) • Gaussian function: h(r) = -exp(-r2/22) • Applying the Gaussian has a smoothing or blurring effect • Blurring depends on sigma • THEN Laplacian of Gaussian (Second Derivative) Roger S. GaborskiRoger S. Gaborski 47 MATLAB edge Function EDGE Find edges in intensity image. EDGE takes an intensity or a binary image I as its input, and returns a binary image BW of the same size as I, with 1's where the function finds edges in I and 0's elsewhere. EDGE supports six different edge-finding methods: The Sobel method finds edges using the Sobel approximation to the derivative. The Prewitt method finds edges using the Prewitt approximation to the derivative. The Roberts method finds edges using the Roberts approximation to the derivative. The Laplacian of Gaussian method finds edges by looking for zero crossings after filtering I with a Laplacian of Gaussian filter. The zero-cross method finds edges by looking for zero crossings after filtering I with a filter you specify. The Canny method finds edges by looking for local maxima of the gradient of I. The gradient is calculated using the derivative of a Gaussian filter. The method uses two thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges. This method is therefore less likely than the others to be "fooled" by Roger noise, and more likely to detectS. GaborskiRoger S. Gaborski true weak edges. 48 Implementation: Laplacian of Gaussian (LoG) • Syntax: [g] = edge(f, ‘log’, T, sigma); Ignores edges that are not stronger than T If T not provided, MATLAB automatically chooses T Roger S. GaborskiRoger S. Gaborski 49 Building What’s important?? Roger S. GaborskiRoger S. Gaborski 50 [g,t]=edge(im,'log',[ ],1); sigma = 1 Roger S. GaborskiRoger S. Gaborski 51 [g,t]=edge(im,'log',[ ],2); Roger S. GaborskiRoger S. Gaborski 52 [g,t]=edge(im,'log',[ ],3) Roger S. GaborskiRoger S. Gaborski 53 Exploring the LoG Parameter Space [g, t] = edge(f, 'log',[],2); Default threshold, t= .918, sigma = 2 WHAT IF WE CHANGE SIGMA?? Roger S. GaborskiRoger S. Gaborski 54 t= .918 Sigma = 1 Sigma = 3 (more smoothing) Roger S. GaborskiRoger S. Gaborski 55 t = .500 Sigma = 1 Sigma = 3 Roger S. GaborskiRoger S. Gaborski 56 t = 1.500 Sigma = 1.0 Sigma = 3.0 Roger S. GaborskiRoger S. Gaborski 57 DoG • Difference of Gaussians is an approximation to Laplacian of Gaussian Roger S. GaborskiRoger S. Gaborski 58 Comparison of Canny and LoG LoG St dev = 2 Canny, Std dev = 1 Roger S. GaborskiRoger S. Gaborski 60 The fern image [g,t]=edge(fernGray,'canny',[ ],2); The structure of the image affects the performance of edge detection Roger S. GaborskiRoger S. Gaborski 61 [g,t]=edge(fernGray,'canny',[ ],3); Roger S. GaborskiRoger S. Gaborski 62 Resize image first >> sm=imresize(fernGray, .5, 'bicubic'); >> [g,t]=edge(sm,'canny',[ ],2); Roger S. GaborskiRoger S. Gaborski 63 >> [g,t]=edge(sm,'canny',[ ],3); >> figure, imshow(g) Roger S. GaborskiRoger S. Gaborski 64 Other Features • Could color be used as a second feature to locate the fern? • Texture? Roger S. GaborskiRoger S. Gaborski 65 Canny, sigma = 3 Roger S. GaborskiRoger S. Gaborski 66 Canny, sigma = 2 Roger S. GaborskiRoger S. Gaborski 67 Canny, sigma = 1 Roger S. GaborskiRoger S. Gaborski 68 Canny, sigma = .5 Roger S. GaborskiRoger S. Gaborski 69 2 Sigma 3 Sigma Roger S. GaborskiRoger S. Gaborski 70 Box Image Roger S. GaborskiRoger S. Gaborski 71 Box Sobel edge image Horizontal Edges Roger S. GaborskiRoger S. Gaborski 72 Note missing Horizontal Edges Roger S. GaborskiRoger S. Gaborski 73 Canny Example Roger S. GaborskiRoger S. Gaborski 74 Canny with larger Sigma Roger S. GaborskiRoger S. Gaborski 75

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