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Colour Vision I

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Colour Vision I

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    1. Colour Vision 1 Colour Vision I Moritz Strring

    2. Colour Vision 2 Overview Colour Image Formation Colour Spaces Reflectance Model Colour Constancy, Correction Segmentation Recognition

    3. Colour Vision 3 Colour Image Formation KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?

    4. Colour Vision 4 Electromagnetic Spectrum

    5. Colour Vision 5 Visible Light

    6. Colour Vision 6 Light Spectrum of Fluorescent Light Other representation of light spectrum Osram Biolux 6500K DaylightOther representation of light spectrum Osram Biolux 6500K Daylight

    7. Colour Vision 7 Light Source Spectra A light souce is characterised by its spectral composition. Here to the left are several examples for spectra of everyday light sources. The upper are the spectra of a blackbody radiator. A blackbody radiator is for example an oven. If we look inside an oven at room temperature (300K) it is black. If the oven is then heated up it becomes first red then via yellow it gets white and finaly bluish. That is what we see here. The red spectrum has a maxium at long wavelength and gives the objects a reddish appearance, for example sunset of electric light bulbs. The higher temperature spectrum has a max at shorter wavelength which gives the material a bluish appearance. The lower spectra are from fluorescent lamps. They can be characterised by their correlated colour temperature. The CCT of a light source is relating to a blackbody of a similar spectral composition. which relates the spectral distribution to that of a blackbody radiator. This is used in the following Blackbody tungsten, electric light bulb Fluorescent The correlated colour temperature is a measure used to characterise the spectrum of a light source reddish, low CCT, maximum at long wavelengths, i.e. low frequency (e.g. sunset, artificial indoor light) bluish, high CCT, maximum at short wavelengths (high frequency) (early morning skylight) A light souce is characterised by its spectral composition. Here to the left are several examples for spectra of everyday light sources. The upper are the spectra of a blackbody radiator. A blackbody radiator is for example an oven. If we look inside an oven at room temperature (300K) it is black. If the oven is then heated up it becomes first red then via yellow it gets white and finaly bluish. That is what we see here. The red spectrum has a maxium at long wavelength and gives the objects a reddish appearance, for example sunset of electric light bulbs. The higher temperature spectrum has a max at shorter wavelength which gives the material a bluish appearance. The lower spectra are from fluorescent lamps. They can be characterised by their correlated colour temperature. The CCT of a light source is relating to a blackbody of a similar spectral composition. which relates the spectral distribution to that of a blackbody radiator. This is used in the following Blackbody tungsten, electric light bulb Fluorescent The correlated colour temperature is a measure used to characterise the spectrum of a light source reddish, low CCT, maximum at long wavelengths, i.e. low frequency (e.g. sunset, artificial indoor light) bluish, high CCT, maximum at short wavelengths (high frequency) (early morning skylight)

    8. Colour Vision 8

    9. Colour Vision 9

    10. Colour Vision 10 Colour Image Formation KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?

    11. Colour Vision 11 Reflectance of Gray

    12. Colour Vision 12 Reflectance of Vegetation & Soil

    13. Colour Vision 13 Reflectance of Human Skin

    14. Colour Vision 14 Colour Image Formation KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?

    15. Colour Vision 15 Sun, Human, Silicon

    16. Colour Vision 16 Observer/Sensor

    17. Colour Vision 17 Single CCD Sensitivities JAI CV-M7

    18. Colour Vision 18 Single Sensor Camera

    19. Colour Vision 19

    20. Colour Vision 20 3CCD Camera

    21. Colour Vision 21 Spectral Integration Having the light spectrum and the spectral reflectance curve of the object the appearance of the object depends on the spectral sensitivity of the Observer. He has something like a Tristimulus RGB values of camera = Colour * Tristimulus From the RGB values one can calculate the chromaticities (pure colour) which contain no more brightness information Transformation from 3 to 2 dimensions Whit these information it is possible to model the skin colour area/cluster in the chromaticity plane Having the light spectrum and the spectral reflectance curve of the object the appearance of the object depends on the spectral sensitivity of the Observer. He has something like a Tristimulus RGB values of camera = Colour * Tristimulus From the RGB values one can calculate the chromaticities (pure colour) which contain no more brightness information Transformation from 3 to 2 dimensions Whit these information it is possible to model the skin colour area/cluster in the chromaticity plane

    22. Colour Vision 22 Rewriting Integration as Summation

    23. Colour Vision 23 Camera Response Integrals in Matrix Form

    24. Colour Vision 24 Colour Spaces A colour space maps qualities of colours onto three coordinate axes RGB (output of most cameras) HSI family Perceptually Uniform, e.g., L* u* v*, L* a* b* CIE standardized (Commission Internationale de LEclairage) A color space is a three-dimensional definition of a color system. The identifying attributes of the color system are mapped onto the coordinate axes. Many different color spaces exist; they each have advantages and disadvantages for color selection and specification. All color spaces are subsets of the CIE color space.A color space is a three-dimensional definition of a color system. The identifying attributes of the color system are mapped onto the coordinate axes. Many different color spaces exist; they each have advantages and disadvantages for color selection and specification. All color spaces are subsets of the CIE color space.

    25. Colour Vision 25 Camera output Screen input Linear/non-linear RGB

    26. Colour Vision 26 L*a*b*

    27. Colour Vision 27 http://www.education.siggraph.org/slides/slides95/s46.htmhttp://www.education.siggraph.org/slides/slides95/s46.htm

    28. Colour Vision 28 HSI Family HSI, HLS, HSV Good for human interaction

    29. Colour Vision 29

    30. Colour Vision 30 Colour Spaces

    31. Colour Vision 31 Colour Image Formation KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?KREIS UM HNDE!!!!!!!!!!!1 Light spectrum, relative radiant power distribution visible wavelengths human camera The appearance of an object is determined by its reflectance and the light it is exposed with (and angle) If the spectrum of the light source changes then the colour of the reflected light changes as well. Reflection of different materials?

    32. Colour Vision 32 Dichromatic Reflection Model for Dielectrical Materials Mention Shafer, Klincker!! For non-homogeous dielectric materials with high oil or water content the reflected light is an additive composition of ... Body, Lambertian or Matte Reflectance Light enters the surface is reflected and scattered, in a wavelength dependent way by colorant particles. The reflected light is equally intense in all directions Surface, Highlight, Specular or Interface Reflectance No light enters the surface. Light is reflected in a mirror-like way. The reflected light is concentrated in a small cone where the cone angle of reflection(s) is similar to the angle of incidence. Dichromatic Reflectance Dichromatic reflectances have interface and body reflectances. The reflected colour signals depend on viewing position and are a combination of body and interface reflected light so if we can detect and separate the surface reflections from the body reflections we get an estimate for the illuminant colour Mention Shafer, Klincker!! For non-homogeous dielectric materials with high oil or water content the reflected light is an additive composition of ... Body, Lambertian or Matte Reflectance Light enters the surface is reflected and scattered, in a wavelength dependent way by colorant particles. The reflected light is equally intense in all directions Surface, Highlight, Specular or Interface Reflectance No light enters the surface. Light is reflected in a mirror-like way. The reflected light is concentrated in a small cone where the cone angle of reflection(s) is similar to the angle of incidence. Dichromatic Reflectance Dichromatic reflectances have interface and body reflectances. The reflected colour signals depend on viewing position and are a combination of body and interface reflected light so if we can detect and separate the surface reflections from the body reflections we get an estimate for the illuminant colour

    33. Colour Vision 33 Example Reflectance of Skin Beschriftungen! Ohtsuki model to synthesise May be show a colour bar along the wavelength axis from blue to red? Tell that we have a maximum at long wavelength, thats why we look more red than blue or green. Sunburn curve (erythematous) of Caucasians use for red limit Reflectance curves of other human races lie in-between the two outer curves (evtl linkes Bild durch Colour Bibel Bild austauschen?) Beside the reflectance the colour appearance of an object is determined by the light it is exposed with Beschriftungen! Ohtsuki model to synthesise May be show a colour bar along the wavelength axis from blue to red? Tell that we have a maximum at long wavelength, thats why we look more red than blue or green. Sunburn curve (erythematous) of Caucasians use for red limit Reflectance curves of other human races lie in-between the two outer curves (evtl linkes Bild durch Colour Bibel Bild austauschen?) Beside the reflectance the colour appearance of an object is determined by the light it is exposed with

    34. Colour Vision 34 RGBs of a single material lie on a plane

    35. Colour Vision 35 Dichromatic Plane

    36. Colour Vision 36 Measurement in RGB Space

    37. Colour Vision 37 How do image colours depend on lighting? Colour Constancy

    38. Colour Vision 38

    39. Colour Vision 39 Image Dependencies Lighting geometry (shading) The colour of the light Lecture 4 page 10Lecture 4 page 10

    40. Colour Vision 40

    41. Colour Vision 41

    42. Colour Vision 42 Independence to Intensity RGB to Chromaticities

    43. Colour Vision 43

    44. Colour Vision 44 Image colours depend on Illuminant Colour

    45. Colour Vision 45

    46. Colour Vision 46

    47. Colour Vision 47

    48. Colour Vision 48 Representative Colour Constancy Algorithms Grey-world colour constancy [e.g. Hunt] Linear model algorithms [Maloney-Wandell] Neural Net approach [Funt-Cardei] Gamut Mapping Colour Constancy [Forsyth 1991] Example: Illuminant Estimation from Skin

    49. Colour Vision 49 Illuminant Estimation from Highlights on the Nose

    50. Colour Vision 50 Dichromatic Reflection Model for Dielectrical Materials Mention Shafer, Klincker!! For non-homogeous dielectric materials with high oil or water content the reflected light is an additive composition of ... Body, Lambertian or Matte Reflectance Light enters the surface is reflected and scattered, in a wavelength dependent way by colorant particles. The reflected light is equally intense in all directions Surface, Highlight, Specular or Interface Reflectance No light enters the surface. Light is reflected in a mirror-like way. The reflected light is concentrated in a small cone where the cone angle of reflection(s) is similar to the angle of incidence. Dichromatic Reflectance Dichromatic reflectances have interface and body reflectances. The reflected colour signals depend on viewing position and are a combination of body and interface reflected light so if we can detect and separate the surface reflections from the body reflections we get an estimate for the illuminant colour Mention Shafer, Klincker!! For non-homogeous dielectric materials with high oil or water content the reflected light is an additive composition of ... Body, Lambertian or Matte Reflectance Light enters the surface is reflected and scattered, in a wavelength dependent way by colorant particles. The reflected light is equally intense in all directions Surface, Highlight, Specular or Interface Reflectance No light enters the surface. Light is reflected in a mirror-like way. The reflected light is concentrated in a small cone where the cone angle of reflection(s) is similar to the angle of incidence. Dichromatic Reflectance Dichromatic reflectances have interface and body reflectances. The reflected colour signals depend on viewing position and are a combination of body and interface reflected light so if we can detect and separate the surface reflections from the body reflections we get an estimate for the illuminant colour

    51. Colour Vision 51 Stepwise Principal Component Analysis in Ascending Intensity Figure was generated with ~mst/projects/Colour/ColourDetection/CGIP_presentation/cgip_eig.m And then in Animation shop from photoshop Figure was generated with ~mst/projects/Colour/ColourDetection/CGIP_presentation/cgip_eig.m And then in Animation shop from photoshop

    52. Colour Vision 52 Body Vector Estimation CHANGE HEADDER OF FIG RIGHT IS EIGENVALUES AND Y AXIS ONLY VALUE CHANGE COLOUR OF CHI 2 TO RED Show 2. Eigenvalue Plot chi square Mark body pixels in color cube Plot estimated body vector in color cubeCHANGE HEADDER OF FIG RIGHT IS EIGENVALUES AND Y AXIS ONLY VALUE CHANGE COLOUR OF CHI 2 TO RED Show 2. Eigenvalue Plot chi square Mark body pixels in color cube Plot estimated body vector in color cube

    53. Colour Vision 53 Surface Vector Estimation CHANGE SURF VECTOR COLOR TO GREEN Annimation done with Chau in 30 deg geometry using ~mst/projects/Colour/ColourDetection/CGIP_presentation/cgip_eig.m CHANGE SURF VECTOR COLOR TO GREEN Annimation done with Chau in 30 deg geometry using ~mst/projects/Colour/ColourDetection/CGIP_presentation/cgip_eig.m

    54. Colour Vision 54 Example of Application Colour Correction Also result?? Give the error 1.6 deg. Incident angle is 30 deg. Normalize to max intens in tip of nose? Animated with von Kreis equation under the middle image? Also result?? Give the error 1.6 deg. Incident angle is 30 deg. Normalize to max intens in tip of nose? Animated with von Kreis equation under the middle image?

    55. Colour Vision 55 Colour Image Segmentation Pixel-based techniques Region-based techniques Edge-based techniques Stochastical Model-based techniques Physics-based techniques Hybrid techniques Example modelling skin and segmenting

    56. Colour Vision 56 Spectral Integration Having the light spectrum and the spectral reflectance curve of the object the appearance of the object depends on the spectral sensitivity of the Observer. He has something like a Tristimulus RGB values of camera = Colour * Tristimulus From the RGB values one can calculate the chromaticities (pure colour) which contain no more brightness information Transformation from 3 to 2 dimensions Whit these information it is possible to model the skin colour area/cluster in the chromaticity plane Having the light spectrum and the spectral reflectance curve of the object the appearance of the object depends on the spectral sensitivity of the Observer. He has something like a Tristimulus RGB values of camera = Colour * Tristimulus From the RGB values one can calculate the chromaticities (pure colour) which contain no more brightness information Transformation from 3 to 2 dimensions Whit these information it is possible to model the skin colour area/cluster in the chromaticity plane

    57. Colour Vision 57 Skin Colour in the Chromaticity Plane

    58. Colour Vision 58 Modelled and Measured Skin Colour in the Chromaticity Plane Show only a part of the chromaticity plane Model and mean of measurements (hand segmented) Structure of the distribution of the mean values looks similar for the different CCTs Model for the lower CCTs too low and for the higher CCTs too higt We use only device specifications and no measurements for the camera characteristic and the light sources. Show only a part of the chromaticity plane Model and mean of measurements (hand segmented) Structure of the distribution of the mean values looks similar for the different CCTs Model for the lower CCTs too low and for the higher CCTs too higt We use only device specifications and no measurements for the camera characteristic and the light sources.

    59. Colour Vision 59 Test Images The camera is white balanced to 3870K (top right)The camera is white balanced to 3870K (top right)

    60. Colour Vision 60 Segment Result

    61. Colour Vision 61 Recognition Colour distribution as a cue for object recognition Histogram [Swain & Ballard. U of Rochester]

    62. Colour Vision 62

    63. Colour Vision 63

    64. Colour Vision 64

    65. Colour Vision 65

    66. Colour Vision 66

    67. Colour Vision 67

    68. Colour Vision 68

    69. Colour Vision 69

    70. Colour Vision 70 Summary Colour Image Formation Colour Spaces Dichromatic Reflectance Model Colour Constancy, Correction Segmentation Recognition, colour as a cue

    71. Colour Vision 71 Exercise Physics-based modelling Segmentation MATLAB

    72. Colour Vision 72

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