1. 1 Image Basics Hao Jiang Computer Science Department Sept. 4, 2014

2. Image Formulation  The most common way to obtain an image is from a camera 2

3. A “Simple” Camera 3 Let’s hold a sensor (a film) in front of the object. Hopefully we will have an image…

4. A “Simple” Camera 4 Unfortunately, at the same image point, light may come from different source points on an object.

5. The Pinhole Camera 5

6. Camera with Lens 6

7. The Imaging Model 7 lighting Surface property: material, geometry. Camera pose, Optical properties

8. Images as Surfaces Image can be treated as a 2D function z = f(x, y).

9. Image Profile 9

10. Sampling  To “digitize” the continuous image, we need to sample the image first. Sampling on a grid Sampling problem

11. The image of Barbara

12. Aliasing due to sampling

13. fs = 2.5f fs = 1.67f Original signal A new component is added This is denoted as aliasing.

14. Image Resolution  Sensor: size of the real world scene into a single image pixel.  Image: number of Pixels. 14

15. Digitization  The samples are continuous and have infinite number of possible values.  The digitization process approximates these values with a fixed number of numbers.  To represent N numbers, we need log 2 N bits.  So, what determines the number of bits we need for an image?

16. Image as Matrices 16 174 167 184 207 213 227

17. Types of Digital Images  Grayscale image  Usually we use 256 levels for each pixel. Thus we need 8bits to represent a pixel (2^8 == 256)  Some images use more bits per pixel, for example MRI images could use 16bits / pixel. A 8bit grayscale Image.

18.  Binary Image A binary image has only two values (0 or 1). Binary image is quite important in image analysis and object detection applications.

19. Gay Scale Image as a Stack of Binary Images [ b7 b6 b5 b4 b3 b2 b1 b0] MSBLSB Each bit plane is a binary image.

20. Dithering  A technique to represent a grayscale image with a binary one. 0  1  2  3  Convert image to 4 levels: I’ = floor(I/64)

21. Color Image r g b 24 bit image

22. Color Table Image with 256 colors r g b Clusters of colors It is possible to use much less colors to represent a color image without much degradation.

23. Gamma Correction  Display device’s brightness is not linearly related to the input. I’ = I    To compensate for the nonlinear distortion we need to raise it to a power again (I’) 1/  = I   for CRT is about 2.2.

24. Gamma Correction Linearly increasing intensity without gamma correction Linearly increasing intensity with gamma correction

25. Image File Formats  An image in “ppm” format: P6: (this is a ppm image) Resolution: 512x512 Depth: 0-255 (8bits per pixel in each channel)

26. An image contains a header and a bunch of (integer) numbers.

27. Image Compression and Encoding  Raw image takes a lot of space. Compute the file sizes of a raw image that has resolution 512x512 in true color.  BMP, PPM, TXT  Images can be “compressed” losslessly or lossly  Lossy image format: JPEG  Losslessly compressed image format: PNG  Compression ratio and bit rate 27

28. Digital Video Frame N-1 Frame 0 time Digital video is digitized version of a 3D function f(x,y,t)