Visual perception basics. Image aquisition system. P. Strumiłło

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1 Visual perception basics Image aquisition system P. Strumiłło

2 Light perception by humans Humans perceive approx. 90% of information about the environment by means of visual system. Efficiency of the human visual system is characterised by a number of features: the ability to resolve image details (θ=1 =1 /60=pi/10800); the ability to discriminate between brightness levels (contrast sensitivity); colour perception; brightness adaptation;

3 pupil Human visual system lens Visual cortex cornea iris retina receptors Lateral geniculate nucleus Optic chaism Optic nerve

4 Visual axis Structure of the human eye lens iris retina receptors Blind spot PWN Fovea Nerve

5 Distribution of rods and cones in the retina No of cones/rods per mm 2 Blind spot 50x50 um Rods Rods Webvision Cones Cones Angle PWN

6 Binocular vision Perception of depth (distance) ~10 m 17 mm Eye convergence angle Disparity in binocular vision

7 A A B B Role of colours in depth perception Denis Meredith

8 Electromagnetic spectrum frequency [Hz] gamma rays X rays microwaves radio waves ultraviolet Visible spectrum Infrared waves [nm]

9 Spectral sensitivity characteristic of the human eye rods cones µm ulraviolet infrared

10 Visual perception Subjective brightness sensation assumes a logarithmic characteristic. Human eye can percieve brightness in the range of Eye adaptation range Glare limit Local adaptation range Log [ml]

11 Contrast sensitivity (Weber fraction) I+ I I I I 2% I [cd/m 2 ] I I The ratio is termed the Weber fraction. It reflects contrast sensitivity characteristc of the human eye.

12 Contrast sensitivity (Weber fraction) I I I o =3 I o =30 I o =300 I o =3000 I I+ I I 0 I [cd/m 2 ] The eye achieves maximum sensitivity for: I+ I I 0

13 Image coded using 16 gray levels 4 bits/pixel MIT

14 Mach bands Brightness intensity Subjective brightness

15 Visual illusions

16 Visual perception basics Image aquisition system

17 Visual path of the image processing system Visual path a set optical and electronic elements converting radiant energy into an electrical signal and imaging it using display devices. 3D 2D Imaging sensor Image formation Opto-electrical conversion Visualisation

18 Pinhole camera (camera obscura) Pin hole Pros and cons: small hole little light goes in large hole image blurring

19 Cameras today Pointgrey CCD sensor Pros and cons: sharp, high-contrast image geometric distortions

20 Image formation model y 2D 3D (α,β) Image formation model (x,y) f(x,y) x Image plane of the imaging sensor

21 Image formation model y f ( x, y) = i( x, y) r( x, y) (x,y) f(x,y) 0 < i( x, y) < - illumination (x,y) 0 < r( x, y) < 1 reflectance coefficient at (x,y) Image a 2-D light intensity function f(x,y)>=0 reflecting light energy distribution x illumination: sunny day ~ 5000 cd/m 2, cloudy day ~ 1000 cd/m 2, full moon ~ cd/m 2, Reflectance coeff.: black velvet , white wall - 0.8, snow

22 Image formation model For a linear process of energy acummulation in the image sensor plane: f ( x, y) = f ( α, β ) h( x, y, α, β ) dα dβ h(.) is the impulse response of the system; in optical systems it is termed the point spread function of the system

23 Image formation model If the point spread function is shift invariant, then the image formation model is given by a convolution integral: f = ( x, y) f ( α, β ) h( x α, y β ) dα dβ h( x, y ) 2 x + y = exp 2 2σ 2

24 Sampling of 1-D signals f(x) F(ω) x -Ω Ω ω s(x) S(ω) x x -1/ x 1/ x ω s(x)f(x) S(ω)*F(ω)... -Ω Ω... ω

25 Sampling of 2-D signals Assume the source image (analog image) features a limited Fourier bandwidth ω y ω maxy ω maxx F ( ω ω ) x, y ω maxx ω x ω maxy

26 ( ) ( ) = = = ,, M i N k y k y x i x y x S δ Image sampling function: and a sampled image: ( ) ( ) ( ) ( ) ( ) = = = = = ,,,,, M i N k s y k y x i x y k x i f y x S y x f y x f δ x y Sampling of 2-D signals

27 ( ) ( ) = = = , 1, M i N k y y x x y x s k i F y x F ω ω ω ω ω ω Fourier spectrum of the sampled image: where: y x y x = = 1, 1 ω ω ω x ω y Sampling of 2-D signals

28 Sampling of 2-D signals ω y ω maxy Ω max < ω x ω 2 x = 1 2 x ω maxx image bandwidth

29 Aliasing distortion - example 500 dpi Scanned images: 100 dpi (dots per inch)

30 Image acquisition Image acquisition is the process of converting light energy radiating from image scene points into an electrical signal (suitable for storing or transmission). Image acquisition devices: CCD camera Video camera Scanner Digitizer

31 Image acquisition There are two basic schems of converting optical images into electrical signals: without accumulation of photo-charges (eg. optical scanner), with accumulation of photo-charges (np. vidicon, CCD array)

32 Imaging sensor (no photo-charges) scanning direction R s(x,y) + photodiode focusing screen

33 CCD array (accumulation of photo-charges) Image formation is based on the internal photo-electric phenomenon Φ U F <0 insulator Capacitor cell electrical potential well n ~10µm

34 The Bayer matrix Raw CCD Format M M N N Calculate RGB image by interpolating colour components from the Bayer matrix

35 Pixim Digital Pixel System (DPS) A/D converter for each pixel (no charge couplings) Single A/D converter

36 CMOS image sensors Pros: cheap technology (used for fabricating memory and CPU modules), low power consumption (100 times!) random access to pixel regions (block image processing) no charge leaking typical for CCD technology on-chip analog-to-digital conversion and signal processing Cons: more susceptibel to noise than CCD lower light sensitivity due to many transistors used for single pixel OmniVision

37 Monochrome TV standards European CCIR standard: (625 (575) lines, line display time 64us, 50 half-images per sec., 1Vpp, 75Ω, signal American RS170 standard: (525 (484) lines, line display time 63,5 us, 60 half-images per sec.,1.4 Vpp, 75Ω signal American RS-343 standard : (875 lines, 60 half-images, dedicated to CCTV, scientific applications, )

38 TV CCIR standard 625/575 lines 3 odd lines even lines 0.7 V 4 52 µs peak white level 0 V (DC) -0.3 V 64 µs Composite video signal video signal blanking level sync level horizontal sync pulse

39 COHU CCD camera Specification Highlights Imager: 1/2" interline transfer CCD Picture Elements: RS-170A: 768 (H) x 494 (V); CCIR: 752 (H) x 582 (V) Pixel Cell Size: RS-170A: 8.4 µm (H) x 9.8 µm (V); CCIR: 8.6 µm (H) x 8.3 µm (V) Resolution: RS-170A: 580 horizontal TVL, 350 vertical TVL; CCIR: 560 horizontal TVL, 450 vertical TVL Synchronization: Crystal/H&V/Asynchronous, standard Shutter: 1/60 to 1/10,000 AGC: 20 db Integration: 2-16 Fields Sensitivity: Full video, No AGC: 0.65 lux; 80% video, AGC on: 0.04 lux; 30% video, AGC on: lux S/N Ratio (Gamma 1, gain 0 db): 55 db

40 CCD image sensors characteristics small size, robust to mechanical vibrations (70 G), no geometrical distortions, low supply voltage (12 V, 1.4W), SNR ~70 db, linear (gamma coefficient), no intra-frame photo-charge accumulation, high resolution, reliable cheap

41 Image frame grabber Matrox CronosPlus Video capture board for PCI captures from NTSC, PAL, RS-170 and CCIR video sources, connect up to 4 CVBS or 1 Y/C trigger input, 7 TTL auxiliary I/Os, 32-bit/33MHz PCI-bus master Matrox Software is sold separately, includes e.g., Matrox Imaging Library for Microsoft Windows

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