Robot\Machine Vision

1. Robot\Machine Vision

2. “Computer vision (or machine vision) is the science and technology of machines that see. Here see means the machine is able to extract information from an image, to solve some task, or perhaps “understand” the scene in either a broad or limited sense” Computer vision

3. Some applications: Objects Activities Scenes Locations Text / writing Faces Gestures Motions Emotions… amusement park sky The Wicked Twister Cedar Point Ferris wheel ride ride 12 E Lake Erie water ride tree tree people waiting in line people sitting on ride umbrellas tree maxair carousel deck bench tree pedestrians

4. 3D Reconstruction: Given many images of a certain scene we can use computer vision algorithms to reconstruct the 3D model.

5. Connection to other disciplines : Graphics Algorithms Robotics Artificial intelligence Image processing Mathematics Machine learning Computer vision

6. width 520 j=1 i=1 500 height I(176,201) = 164 I(194,203) has value 37 Image representation on Computer: Intensity : [0,255]

7. Color images, RGB color space : B R G

8. RGB to Grayscale

9. Image formation – Pinhole Camera: • Pinhole camera is a simple model to approximate imaging process, perspective projection. Image plane Virtual image pinhole If we treat pinhole as a point, only one ray from any given point can enter the camera.

10. Perspective Projection • A 3D orthogonal coordinate system with its origin at O. This is also where the camera aperture is located.. • An image plane where the 3D world is projected through the aperture of the camera. The image plane is parallel to axes X1 and X2. -f where f > 0. f is also referred to as the focal length of the pinhole camera. • A point R at the intersection of the optical axis and the image plane. This point is referred to as the principal point or image center. • A point P somewhere in the world at coordinate relative to the axes X1,X2,X3. • The projection line of point P into the camera. This is the green line which passes through point P and the point O. • There is also a 2D coordinate system in the image plane, with origin at R and with axes Y1 and Y2 which are parallel to X1 and X2, respectively. The coordinates of point Q relative to this coordinate system is .

11. Perspective Projection • We have 2 similar triangles • So:

12. Perspective Projection • The resulting image is rotated 180 degrees. • In order to produce an unrotated image there are two possibilities: • Rotate the coordinate system in the image plane 180° (in either direction). This is the way any practical implementation of a pinhole camera would solve the problem; for a photographic camera we rotate the image before looking at it, and for a digital camera we read out the pixels in such an order that it becomes rotated. • Place the image plane so that it intersects the X3 axis at f instead of at -f and rework the previous calculations. This would generate a virtual (or front) image plane which cannot be implemented in practice, but provides a theoretical camera which may be simpler to analyze than the real one.

13. Edge Detection

14. גילוי שפות - Edge Detection מפת שפות של התמונה עיבוד תמונות ואותות במחשב

15. גילוי שפות - Edge Detection • We look at the image as a continuous function f(x,y) . • The gradient of this function: • The gradient direction measures change in intensity, and the size of the gradient is the value of the highest slope. עיבוד תמונות ואותות במחשב

16. Gradient - Example

17. Gradient - Example עיבוד תמונות ואותות במחשב

18. Gradient Example >> i = double(imread('cameraman.tif')); >> gradFilt = [-1 0 1 ; -2 0 2 ; -1 0 1]/2; >> grad_x = imfilter(i , gradFilt , 'same' , 'replicate'); >> grad_y = imfilter(i , gradFilt' , 'same' , 'replicate'); >> [x,y] = meshgrid([1:size(i,2)] , [1:size(i,1)]); >> figure; imshow(i , []); hold on; >> quiver(x , y , grad_x , grad_y , 3 , 'm' , 'LineWidth' , 1); עיבוד תמונות ואותות במחשב

19. Yet another example rice.png עיבוד תמונות ואותות במחשב

20. -1 0 1 1 1 1 2 1 1 -1 0 1 -1 0 1 0 0 0 0 0 0 -2 0 2 -1 0 1 -1 -1 -1 -2 -1 -1 -1 0 1 Approximation of the gradient מסנן לחשוב נגזרת בכיוון y מסנן לחשוב נגזרת בכיוון x מסנן לחשוב נגזרת בכיוון x מסנן לחשוב נגזרת בכיוון y sobel prewitt עיבוד תמונות ואותות במחשב

21. גילוי שפות - Edge Detection עיבוד תמונות ואותות במחשב

22. Filtering by gradient size T = 100 T = 70 T =40 T = 20 T = 10 T = 2 עיבוד תמונות ואותות במחשב