1. Chapter 2 : Imaging and Image Representation Computer Vision Lab. Chonbuk National University

2. Contents 2.1 Sensing Light 2.2 Image Device 2.3 Problems in Digital Images* 2.4 Picture Function and Digital Image 2.5 Digital Image Formats * 2.6 Richness and Problems of Real Imagery 2.7 3D Structure from 2D Images 2.8 Five Frames of Reference 2.9 Other Types of Sensors *

3. 2.1 Sensing Light Simple model of common photography (Sun or Flash bulb) Reflects radiation Toward camera Sense via chemical on film

4. 2.2 Imaging devices CCD Camera

5. 2.2 Imaging devices Frame Buffer –High speed image store available : Actually Store several Images or their derivatives Digital Image refer to pixel values as I[r,c] –I : array name –r : row –c : column

6. 2.2 Imaging devices The Human Eye

7. 2.4 Picture functions and digital images Concepts of analog image and digital images Digital image : 2D rectangular array of discrete values –Image space and intensity range are quantized into a discrete set of values –Permitting the image to be stored in 2D computer memory structure –Common intensity range: 8bit (0~255) –In C program, unsigned char I[512][512] analog image: F(x,y) which has infinite precision in spatial parameters x and y and infinite precision in intensity at each spatial point (x, y) digital image: I[r,c] represented by a discrete 2D array of intensity samples, each of which is represented using a limited precision

8. 2.4 Picture functions and digital images Coordinate systems

9. 2.4.2 Image Quantization and Spatial Measurement picture function: f(x,y) of a picture as a function of two spatial variables x and y that are real values defining points of picture and f(x, y) is usually also real value gray scale image: Monochrome digital image I[r,c] with one intensity value per pixel multi-spectral image: 2D image M[x,y] has a vector of values at each spatial point or pixel (If image is color, vector has 3 elements) binary image: digital image with all pixel values 0 or 1 labeled image: digital image L[r,c] whose pixel values are symbols from finite alphabet (Related concepts thematic image and pseudo-colored image)

10. 2.4.2 Image Quantization and Spatial Measurement nominal resolution: size of scene element that images to a single pixel on the image plane resolution: number of pixels (e.g., 640*480)

11. 2.4.2 Image Quantization and Spatial Measurement Use appropriate resolution –Too little produce poor recognition –Too much slow down algorithm and waste memory

12. 2.4.2 Image Quantization and Spatial Measurement Spatial quantization effects impose limits on measurement accuracy and detectability

13. 2.5 Digital Image Formats Dozen of different formats still in use Raw data: encode image pixels in row-by-row (raster order) Most recently developed standard formats contain a header with non-image information necessary to label the data to decode it

14. 2.5.1/2 Image File header & Data Image File header –Need to make an image file self-describing so that image-processing tools can work with them –Should contain : image dimension, type, data, title color table, coding table history Image Data –Nowadays, multimedia format including image data along with text, graphics, music, etc.

15. 2.5.3 Data Compression Reduce the size of an image (30 percent or even 3 percent of raw size) Copression can be lossless or lossy –Lossless compression : original image recovered exactly –Lossy compression : loss of quality is perceived (but, not always) To implement compression –Include overhead (compression method and parameter) –Loss or change of a few bit having little or no affect on consumers (exciting area from signal processing to object recognition)

16. 2.5.4 Commonly Used Formats For colleague, Image data base, scanned documents –GIF, JPG, PS, TIFF etc. Image/Graphics file formats are still evolving

17. 2.5.5 Run-Coded Binary Images Efficient for binary or labeled images Reduce memory space Speed up image operations

18. 2.5.6 PGM : Portable Gray Map Simplest file format Family format: PBM/PGM,PPM Image header encoded in ASCII

19. 2.5.9 JPEG Image File Format JPEG (Joint Photographic Experts Group) Provide practical compression of high-quality color Stream oriented and allow realtime hardware for encoding and decoding Up to 64K X 64K pixels of 24 bits Header contain thumbnail image (up to 64k) To achieve high compression, flexible but lossy coding scheme: Unnoticeable degration(1/20) Compression work well when has large constant regions Compression scheme : DCT(Discrete Cosine Transform) followed by Huffman coding

20. 2.5.11 MPEG format for video Stream-oriented encoding scheme for video, text, and graphics MPEG stands for Motion Picture Experts Group MPEG-1 –Primary design for multimedia systems –Data rate Compression audio : 0.25 Mbits/s Compression video : 1.25 Mbits/s MPEG-2 –Data rate up to 15Mbits/s –Handle high definition TV rates Compression scheme takes advantage of both spatial redundancy (used in JPEG) and temporal redundancy, general 1/25, 1/200 possible Motion JPEG compression is not good

21. 2.5.12 Comparison of Formats

22. 2.6 Richness and Problems of real imagery

23. 2.8 Five frames of reference Pixel Coordinate Frame –Each point has integer pixel coordinates –Using only image I, cannot determine which object is actually larger in 3D

24. 2.8 Five frames of reference Object Coordinate Frame O –Used to model ideal objects in both computer graphics and computer vision –Remains the same regardless of how block is posed related to world Camera Coordinate Frame C –Often needed for egocentric (camera centric) view – Represent just in front of the sensor

25. 2.8 Five frames of reference Real Image Coordinate Frame F –Coordinate [x f, y f, f] –F : focal length –x f, y f : not description of pixels in the image array but related to the pixel size and pixel position of optical axis in the image –Frame F contians the picture function digital image in the pixel array I World Coordinate Frame W –Needed to relate objects in 3D