CS 414 – Multimedia Systems Design Lecture 4 – Visual Perception and Digital Image Representation

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

2. 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. CS 414 - Spring 2011

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. 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 Rods vs Cones (Responsible for us seeing brightness and color) Cones Rods 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 Scale • 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 Original Saturation lightness CS 414 - Spring 2011 Source: Wikipedia

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

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

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. 4 gray levels 2gray levels

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

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

17. Image Representation Example 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 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) Spatial and Frequency Domains 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 X = CS 414 - Spring 2008

24. 20 12 14 23 45 15 19 33 55 34 81 22 8 64 49 95 Mean Filter Subset of image Convolution filter

25. Mean Filter 20 12 14 23 45 15 19 33 55 34 81 22 8 64 49 95 Subset of image Convolution filter

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 1st 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

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