1. COMP 9517 Computer Vision
Digital Images
1/28/2018
COMP 9517 S2, 2009

2. Overview of Digital Images
Humans derive a great deal of information about the world through their visual sense – eyes.
Three components for construction of images:
A scene of objects
Illumination of the objects
Sensing the illumination
1/28/2018
COMP 9517 S2, 2009

3. Overview of Digital Image
2D digital images is an array of intensity samples reflected from or transmitted through objects
Digital images contain fixed number of rows and columns of Pixels
Pixels (picture elements) are little tiles holding quantised values (0-255) represent the brightness at the points of the image
Colour images have three values for each pixel (for example, RGB)
1/28/2018
COMP 9517 S2, 2009

4. Overview of Digital Image1 2 3 4 5 6 7
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3 band for colour image
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1/28/2018
COMP 9517 S2, 2009

5. Digital Images - 2D Projection of 3D
3D world has color, texture, surfaces, volumes, light sources, objects, motion, connections, etc.
2D image is a projection of a scene from a specific viewpoint; many 3D features are captured, but some missed.
1/28/2018
COMP 9517 S2, 2009

6. Image Receives Reflections
Light reaches surfaces of objects
Surfaces reflect
Camera receives light energy
1/28/2018
COMP 9517 S2, 2009

7. Radiation
Different types of electromagnetic radiation, such as X-ray, infra-red
Different wavelengths of radiation have different properties
Different devices to detect different radiation
1/28/2018
COMP 9517 S2, 2009

8. Image Devices CCD (charge-coupled device) cameras
Lens collects light rays
Cells (array of small fixed elements) convert light energy into electrical charge
Through frame grabber or IEEE 1394 to PC
1/28/2018
COMP 9517 S2, 2009

9. Computer Vision System
Camera inputs to frame buffer
Program can interpret data
Program can add graphics
Program can add imagery
1/28/2018
COMP 9517 S2, 2009

10. Image Formation
The geometry of image formation: the projection of each point of the 3D scene through the centre of projection (or lens centre) onto the image plane
Pinhole Camera
Perspective projection
Affine projection
1/28/2018
COMP 9517 S2, 2009

11. Perspective Projection
The apparent size of object depends on their distance: far object appear smaller
By similar triangles
Ignore the third coordinate, and get
1/28/2018
COMP 9517 S2, 2009

12. Affine Project
Scene depth is small relative to the average distance from the camera
Let magnification to be positive constant, since is negative, i.e. treat all points in scene being at constant distance from camera
Leads to weak perspective projection
1/28/2018
COMP 9517 S2, 2009

13. Affine Project
The camera always remains at a roughly constant distance from the scene
Orthographic projection when normalise m to be -1
1/28/2018
COMP 9517 S2, 2009

14. Picture function
A picture function is a mathematical representation f(x,y) of a picture as a function of two spatial variables x and y.
x and y: real values defining points of the picture
f(x,y): real value defining the intensity of point (x,y)
1/28/2018
COMP 9517 S2, 2009

15. Picture Function and Digital Images
Analog image: a 2D image F(x,y) has infinite precision in both spatial parameters x, y and intensity at each spatial point (x,y)
Digital image: a 2D image I[r,c] by a discrete 2D array of intensity samples with limited precision
Can be stored in a 2D computer memory structure
2D array of discrete values. In C, char I[512][512]
Intensity as an 8-bit number allows values of 0-255
3 such values for colour image.
1/28/2018
COMP 9517 S2, 2009

16. Sampling and Quantisation
Digitisation: convert analog image to digital image
Sampling: digitising the coordinate
spatial discretisation of a picture function f (x,y)
use a grid of sampling points, normally rectangular: image sampled at points x = j Δx, y = k Δy, j = 1...M, k = 1...N.
Δx, Δy called the sampling interval.
1/28/2018
COMP 9517 S2, 2009

17. Spatial Resolution Spatial Resolution: pixels per unit of length
Resolution decreases by one half
Human faces can be recognized at 64 x 64 pixels per face
Appropriate resolution is essential:
too little resolution, poor recognition
too much resolution,slow and wastes memory
1/28/2018
COMP 9517 S2, 2009

18. Sampling and Quantisation
Quantisation: digitising the amplitude values
called intensity or gray level quantisation
Gray-level resolution:
usually has 16, 32, 64, ...., 128, 256 levels
number of levels should be high enough for human perception of shading details - human visual system requires about 100 levels for a realistic image.
1/28/2018
COMP 9517 S2, 2009

19. Image Coordinate System
Raster oriented: down-leftward (a)
Cartesian coordinate: up-leftward (b, c)
Relationship btn pixel centre point to I[i,j]
1/28/2018
COMP 9517 S2, 2009

20. Type of images
Gray-scale image: a monochrome digital image I[r,c] with one intensity value per pixel
Multispectral image: a 2D image M[x,y] has a vector of values at each pixel, colour image (r,g,b)
Binary image: a digital image with all pixel values 0 or 1
Labelled image: a digital image L[r,c] with pixel values as symbols denoting the decisions made for that pixel
1/28/2018
COMP 9517 S2, 2009

21. Digital Image Format
Image file header: non image information for labelling and decoding data
Image data
Data Compression
Lossless: can be recovered exactly
Lossy: may lose quality
1/28/2018
COMP 9517 S2, 2009

22. Common Image Format Run-Coded Binary Image:
an efficient coding scheme for binary or labelled images
1/28/2018
COMP 9517 S2, 2009

23. Common Image Format PGM(PBM/PGM, PPM): Portable gray map
One of the simplest file formats
1/28/2018
COMP 9517 S2, 2009

24. Common Image Format
Gif(GIF): Graphics Interchange Format, WWW, 8 bits – 256 colour levels, may be lossless
Tiff(TIFF/TIF): Tag Image File Format, 1-24 bits, lossy or lossless
Jpeg(JFIF/JFI/JPG): Joint Photographic Experts Group, up to 24 bits, recent standard, independent of colour system, lossy or lossless
PostScript(PDF/PDL/EPS): encoded by ASCII
Mpeg(MPG/MPEG/MPEG-2): Motion Picture Expert Group, stream-oriented encoding of video
1/28/2018
COMP 9517 S2, 2009

25. References
Driscoll, W. and Vaughan, W., eds (1978), Handbook of Optics, McGrtaw-Hill.
Boyle, W. and Smith, G. (1970), Charge coupled semiconductor devices, Bell Syst. Tech. J. 49,
Huang, T.S.(1965), PCM Picture Transmission. IEEE Spectrum, vol. 2, no.12, pp
1/28/2018
COMP 9517 S2, 2009

26. Acknowledgement
Some material, including images and tables, were drawn from the textbook and Stockman’s online resources.
1/28/2018
COMP 9517 S2, 2008