CS 556: Computer Vision. Lecture 2

Transcription

1 CS 556: Computer Vision Lecture 2 Prof. Sinisa Todorovic sinisa@eecs.oregonstate.edu 1

2 Basic MATLAB Commands imread size whos imshow imwrite im2double rgb2gray, im2uint8, im2bw img1 = img(1:end-4,:), img1 = img(1:3:end,1:4:end) zeros(m,n), ones(m,n) rand(m,n), randn(m,n) min(min(i1)), max(max(i2)) 2

3 Basic MATLAB Commands figure; subplot(2,2,1); imshow(i1); title('fig. 1 caption'); subplot(2,2,2); imshow(i2); title('fig. 2 caption'); subplot(2,2,3); imshow(i3); title('fig. 3 caption'); Scaled = uint8( * ( I1 - min(min(i1)) )... /(max(max(i1))-min(min(i1)))); subplot(2,2,4); imshow(scaled); title('fig. 4 caption'); 3

4 Basic MATLAB Commands print -dpsc hw1.ps print -djpeg hw1.jpg 4

5 Basic MATLAB Commands ismember, isempty intersect, union for, while meshgrid imadjust imhist function [outputs] = name_func(inputs) return 5

6 Basic MATLAB Commands 6

7 Basic MATLAB Commands img = im2double(imread( image_name )) 6

8 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) 6

9 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) 6

10 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) print -djpeg Fig1.jpg 6

11 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) print -djpeg Fig1.jpg [x,y] = size(img) 6

12 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) print -djpeg Fig1.jpg [x,y] = size(img) rgb2gray(img) 6

13 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) print -djpeg Fig1.jpg [x,y] = size(img) rgb2gray(img) list = find(img == 0) 6

14 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) print -djpeg Fig1.jpg [x,y] = size(img) rgb2gray(img) list = find(img == 0) w = fspecial( Gauss, 3, 0.01) 6

15 Basic MATLAB Commands img = im2double(imread( image_name )) imwrite(uint8(img), image_name ) imshow(img) print -djpeg Fig1.jpg [x,y] = size(img) rgb2gray(img) list = find(img == 0) w = fspecial( Gauss, 3, 0.01) img_out = imfilter(img,w) 6

16 Typical Computational Steps for, e.g., Object Recognition 7

17 An Example Approach to Object Detection Car/non-car Classifier 8 from Kristen Grauman, B. Leibe

18 An Example Approach to Object Detection Car/non-car Classifier Problem 1: What are good features, window size? 8 from Kristen Grauman, B. Leibe

19 An Example Approach to Object Detection Car/non-car Classifier 9 from Kristen Grauman, B. Leibe

20 An Example Approach to Object Detection Car/non-car Classifier Problem 2: What are good training examples? 9 from Kristen Grauman, B. Leibe

21 An Example Approach to Object Detection Car/non-car Classifier 10 from Kristen Grauman, B. Leibe

22 An Example Approach to Object Detection Car/non-car Classifier Problem 3: What is a good classifier? 10 from Kristen Grauman, B. Leibe

23 An Example Approach to Object Detection Optimal features/windows? What is a non-car? Optimal decision? Training examples Car/non-car Classifier Feature extraction 11 from Kristen Grauman, B. Leibe

24 Image Features vs. Feature Descriptors Features Descriptors Interest points (e.g., corners, patches, junctions) Edges, contours, curves, shapes, boundaries Regions SIFT, HOG Shape context, Beam-angle Color-histogram, Shape descriptors Texture Responses of a bank of filters 12

25 Feature Extraction by Filtering g(x, y) = a i= a b j= b w(i, j)f(x + i, y + j) filter image 13

26 Linear Spatial Filtering 14

27 Gaussian Filter N(x, y) = exp( x2 + y ) 3x3 filter: w 1 = N( 1, 1), w 2 = N( 1, 0),... w 9 = N(1, 1) 15

28 Smoothing Filter -- Low Pass Filter spatial averaging weighted averaging 16

29 Example: Smoothing Filter 17

30 Image Gradient I x (x, y) =I(x +1,y) I(x, y) = {z 0 } D x (x,y) 5 I(x, y) 18

31 Image Gradient I y (x, y) =I(x, y + 1) I(x, y) = {z 0 } D y (x,y) I(x, y) 19

32 Example: Image Gradient input image [D x (x, y)+d y (x, y)] I(x, y) 20

33 Example: Image Laplacian input image Laplacian r 2 I(x, y) D x (x, y) [D x (x, y) I(x, y)] +D y (x, y) [D y (x, y) I(x, y)] 21

34 Weighted Image Gradient w(x, y; ) I x (x, y) =w(x, y; ) D x (x, y) I(x, y) Example: smoothing filter =[w(x, y; ) D x (x, y)] I(x, y) 22

35 Weighted Image Gradient w(x, y; ) I x (x, y) =w(x, y; ) D x (x, y) I(x, y) Example: smoothing filter =[w(x, y; ) D x (x, y)] I(x, y) convolution is associative 23

36 Weighted Image Gradient w(x, y; ) I x (x, y) =w(x, y; ) D x (x, y) I(x, y) =[w(x, y; ) D x (x, y)] I(x, y) =[D x (x, y) w(x, y; )] I(x, y) convolution is commutative 24

37 Weighted Image Gradient w(x, y; ) I x (x, y) =w(x, y; ) D x (x, y) I(x, y) =[w(x, y; ) D x (x, y)] I(x, y) =[D x (x, y) w(x, y; )] I(x, y) = w x (x, y; ) I(x, y) derivative of the filter 25

38 Weighted Image Gradient w(x, y; ) I x (x, y) =w x (x, y; ) I(x, y) w(x, y; ) I y (x, y) =w y (x, y; ) I(x, y) Image is discrete Gradient is approximate We always find the gradient of the kernel! 26

39 Interest Points Harris corners Hessian points Harris-Laplacian points Difference-of-Gaussians Harris-Difference-of-Gaussians 27

40 Properties of Interest Points Locality -- robust to occlusion, noise Saliency -- rich visual cue Stable under affine transforms Distinctiveness -- differ across distinct objects Efficiency -- easy to compute 28

41 Example of Detecting Harris Corners 29