1. CS 414 - Spring 2011 CS 414 – Multimedia Systems Design Lecture 4 – Visual Perception and Digital Image Representation Klara Nahrstedt Spring 2011

2. CS 414 - Spring 2011 Administrative If you did not send group configuration to TA, please, do so today 1/26/2011 MP1 will be posted on 1/28 (Friday) Start by reading the MP1 and organizing yourself as a group this week, start to read documentation, search for multimedia files, try the tutorial.

3. Administrative Leasing Process from TSG Helpdesk  Lease one Logitech camera for each student or at least two cameras within one group to start MP1, and then for MP2/MP3.  Leasing process starts on January 26  Pick up the camera from the TSG Helpdesk  Bring your student ID to sign for the camera  Each cs414 student is responsible for his/her own camera if you loose it (or badly damage) and you don’t have police report, you pay for it (charged to your student account at the end of the semester)  If nobody is at the TSG Helpdesk, then go to Barb Leisner office, room 2312 SC  Helpdesk hours are Monday –Friday 8am-8pm  No camera pickup on Saturday and Sunday CS 414 - Spring 2011

4. Human Visual System Eyes, optic nerve, parts of the brain Transforms electromagnetic energy

5. Human Visual System Image Formation  cornea, sclera, pupil, iris, lens, retina, fovea Transduction  retina, rods, and cones  Retina has photosensitive receptors at back of eye Processing  optic nerve, brain

6. Rods vs Cones (Responsible for us seeing brightness and color) Contain photo-pigment Respond to low energy Enhance sensitivity Concentrated in retina, but outside of fovea One type, sensitive to grayscale changes Contain photo-pigment Respond to high energy Enhance perception Concentrated in fovea, exist sparsely in retina Three types, sensitive to different wavelengths ConesRods CS 414 - Spring 2011

7. Tri-stimulus Theory 3 types of cones (6/7 Mil. of them)  Red = L cones, Green = M cones, Blue = S cones  Ratio differentiates for each person  E.g., Red (64%), Green (32%), rest S cones  E.g., L(50.6%), M(44.2%), rest S cones Each type most responsive to a narrow band electro-magnetic waves  red and green absorb most energy, blue the least Light stimulates each set of cones differently, and the ratios produce sensation of color

8. Color and Visual System Color refers to how we perceive a narrow band of electromagnetic energy  source, object, observer Visual system transforms light energy into sensory experience of sight

9. Color Perception (Color Theory) Hue  Refers to pure colors  dominant wavelength of the light Saturation  Perceived intensity of a specific color  how far color is from a gray of equal intensity Brightness (lightness)  perceived intensity CS 414 - Spring 2011 Hue Scale Saturation Original lightness Source: Wikipedia

10. Digitalization of Images – Capturing and Processing CS 414 - Spring 2011

11. Capturing Real-World Images Picture – two dimensional image captured from a real-world scene that represents a momentary event from the 3D spatial world CS 414 - Spring 2011 W3 W1 W2 r s Fr= function of (W1/W3); s=function of (W2/W3)

12. Image Concepts An image is a function of intensity values over a 2D plane I(r,s) Sample function at discrete intervals to represent an image in digital form  matrix of intensity values for each color plane  intensity typically represented with 8 bits Sample points are called pixels CS 414 - Spring 2011

13. Digital Images Samples = pixels Quantization = number of bits per pixel Example: if we would sample and quantize standard TV picture (525 lines) by using VGA (Video Graphics Array), video controller creates matrix 640x480pixels, and each pixel is represented by 8 bit integer (256 discrete gray levels) CS 414 - Spring 2011

14. Image Representations Black and white image  single color plane with 2 bits Grey scale image  single color plane with 8 bits Color image  three color planes each with 8 bits  RGB, CMY, YIQ, etc. Indexed color image  single plane that indexes a color table Compressed images  TIFF, JPEG, BMP, etc. 2gray levels4 gray levels

15. Digital Image Representation (3 Bit Quantization) CS 414 - Spring 2011

16. Color Quantization Example of 24 bit RGB Image CS 414 - Spring 2011 24-bit Color Monitor

17. Image Representation Example 128135166138190132 129255105189167190 229213134111138187 135190 255167 213138 128138 129189 229111 166132 105190 134187 24 bit RGB Representation (uncompressed) Color Planes

18. Graphical Representation CS 414 - Spring 2011

19. Image Properties (Color) CS 414 - Spring 2011

20. Color Histogram CS 414 - Spring 2011

21. Spatial and Frequency Domains Spatial domain  refers to planar region of intensity values at time t Frequency domain  think of each color plane as a sinusoidal function of changing intensity values  refers to organizing pixels according to their changing intensity (frequency) CS 414 - Spring 2011

22. Image Processing Function: 1. Filtering Filter an image by replacing each pixel in the source with a weighted sum of its neighbors Define the filter using a convolution mask  non-zero values in small neighborhood, typically centered around a central pixel  generally have odd number of rows/columns CS 414 - Spring 2011

23. Example: Convolution Filter CS 414 - Spring 2008 100 50 100 010 000 000 50 100 50100 X =

24. Mean Filter Convolution filterSubset of image 9549648 22813455 33191545 23141220

25. Mean Filter Convolution filterSubset of image 9549648 22813455 33191545 23141220

26. Common 3x3 Filters Low/High pass filter Blur operator H/V Edge detector

27. Example CS 414 - Spring 2011

28. Image Function: 2. Edge Detection Identify areas of strong intensity contrast  filter useless data; preserve important properties Fundamental technique  e.g., use gestures as input  identify shapes, match to templates, invoke commands

29. Edge Detection CS 414 - Spring 2011

30. Simple Edge Detection Example: Let assume single line of pixels Calculate 1 st derivative (gradient) of the intensity of the original data  Using gradient, we can find peak pixels in image  I(x) represents intensity of pixel x and  I’(x) represents gradient (in 1D),  Then the gradient can be calculated by convolving the original data with a mask (-1/2 0 +1/2)  I’(x) = -1/2 *I(x-1) + 0*I(x) + ½*I(x+1) CS 414 - Spring 2011 5764152148149

31. Basic Method of Edge Detection Step 1: filter noise using mean filter Step 2: compute spatial gradient Step 3: mark points > threshold as edges CS 414 - Spring 2011

32. Summary Other Important Image Processing Functions  Image segmentation  Image recognition Formatting Conditioning Marking Grouping Extraction Matching  Image synthesis CS 414 - Spring 2011