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Topic 5 - Imaging Mapping - II

Topic 5 - Imaging Mapping - II. Department of Physics and Astronomy. DIGITAL IMAGE PROCESSING Course 3624. Professor Bob Warwick. 5.1 The Perception of Colour.

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Topic 5 - Imaging Mapping - II

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  1. Topic 5 - Imaging Mapping - II Department of Physics and Astronomy DIGITAL IMAGE PROCESSING Course 3624 Professor Bob Warwick

  2. 5.1 The Perception of Colour • Although the human eye has only a limited ability to discern shades of grey (~ 2 5), it does much better with colour gradations (> 2 8) • Inclusion of colour invariably adds to the visual content of an image (eg black and white versus colour TV pictures) • The human perception of colour is a complex subject. It depends on both physiological and psychological factors. Also the eye/brain system is highly adaptive

  3. The Nature of White Light

  4. Colour Mixing Experiments Combining colour pigments Magenta, cyan, yellow, and black are the colours often used in printing

  5. The Human Eye The Retina Fovea Optic nerve Macula

  6. Spectral Sensitivity Curves of Rods and Cones The range of sensitivity is roughly 400 - 650 nm. There are three types of cone (blue, green, red). The rods (dashed) primarily give sensitivity in low illumination

  7. Tri-Stimulus Colour Theory Any colour can be reproduced by mixing an appropriate set of three “primary colours” – Thomas Young 1802

  8. A More Complex Model of Human Colour Perception

  9. Colour Matching Experiments Choose three “primary” light sources with spectra P1(λ), P2(λ) & P3(λ) C(λ) = Colour matching involves adjusting the input light levels β1β2β3 to match the tristimulus values of the test colour

  10. Colour Matching with Monochromatic Primaries 435.8 nm 546.1 nm 700nm

  11. The CIE 1931 (All Positive) Colour Matching Functions The corresponding tristimulus values for an arbitrary spectral colour I are: The CIE XYZ system was designed so that the Y parameter was a measure of brightness ie luminance. Then x=X/(X+Y+Z) & y=Y/(X+Y+Z) represent "chromaticity" parameters, (since z = 1 - x – y gives no further colour information) Thus three parameters define an input in the CIE xyY colour space.

  12. CIE 1931 Chromaticity Diagram The gamut of colours that can be generated using monochromatic primaries at 435.8, 546.1 and 700 nm, lie within the triangle. Similarly the xy coordinates of the three phosphors in a display will define the "realizable" colours of the device The outer curve boundary represents the track of monochromatic colours in the x,y plane. (Although in practice the colours shown are restricted by the performance of the display devices.)

  13. Alternative Colour Specifications Colour shade (red, green,blue etc..) Depends on peak λ Measures the purity of the colour Degree of dilution by white light Luminance Hue Saturation (LHS) Colour Space

  14. CYAN - RED CYAN -RED GREEN - MAGENTA GREEN-MAGENTA BLUE - YELLOW BLUE-YELLOW Complementary Colours Colours opposite each other on the colour disk are called “complementary”. Author: Richard Alan Peters II

  15. 5.2 Pseudo-Colour Techniques Colour displays utilize three different phosphors (RGB) to generate a set of "realizable" colours. These colours occupy the "colour cube" of the device, which can be visualized as a 3-d space. B G R The Colour Cube has R,G & B axes normalised so that at full intensity R=G=B=1. Then R=G=B=0 is black R=G=B=1 is full intensity white R=G=B= 0  1 represents a grayscale (along the leading diagonal)

  16. Pseudo-Colour Processing Pseudo-colour processing involves the use of colour to enhanced the display of a single band (monochrome) image. The input grayscale is subdivided into intervals, against which a set of colours are assigned. If the number of intervals is small  Density Slicing Colour Assignment Yellow Red Blue Black Grey Level ( f )

  17. The Colour Table More sophisticated applications of pseudo-colour involve the use of a colour table. The Colour Table represents a specific (and more systematic) mapping from grey level to RGB colour. For example: R,G,B 1 0 0 63 127 191 255 f

  18. Example: Colour Table with 64 levels A single band image in pseudo-colour. XMM Image of SNR G21.5-0.9

  19. 5.3 True Colour and False Colour Techniques In many applications (e.g., remote sensing, astronomy etc..) more than one image is recorded of the same scene using different spectral filters. Up to three such images can be displayed simultaneously with a "three channel" display system. In this case the possibilities are: TRUE COLOUR PROCESSING - where the images are recorded through R,G & B filters and are (respectively). displayed on the R, G and B channels of the display system. In many applications (e.g. photography, TV etc..) special care is needed to achieve an acceptable level of colour fidelity. FALSE COLOUR PROCESSING - where images recorded in three different wavebands are displayed arbitrarily on the R, G and B channels of the display system.

  20. Implementation of Colour Enhancementvia the device Colour (Look-up) Table Single Channel System – Pseudo-Colour D/A D/A D/A D/A D/A D/A Three Channel System – True or False Colour R Colour Look-up Table Image Store Video Out G R B Colour Look-up Table Colour Look-up Table Colour Look-up Table Image Store 1 Image Store 2 Image Store 3 Video Out Video Out Video Out G B

  21. True Colour DISPLAY I/P CHANNELS

  22. False Colour DISPLAY I/P CHANNELS 10 micron image 2.2 micron image 1.2 micron image

  23. Pseudo Colour DISPLAY I/P CHANNELS Colour Table

  24. Pseudo Colour Examples

  25. True, False or Pseudo Colour ?

  26. Studying Temporal Changes in Remote SensingExample: Panama Canal

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