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Colour Image P rocessing

Colour Image P rocessing. Web reference www.cse.msu.edu/~stockman/Book/book.html. Colour Perception. Physics of light Human Perception Land Colour Mondrains. Human Vs Hardware. Hardware . Colour Cameras Mosaic 3 chip Frame grabbers 3 frame buffers Red, Green and Blue. Image Physics.

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Colour Image P rocessing

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  1. Colour Image Processing Web reference www.cse.msu.edu/~stockman/Book/book.html

  2. Colour Perception • Physics of light • Human Perception • Land Colour Mondrains

  3. Human Vs Hardware

  4. Hardware • Colour Cameras • Mosaic • 3 chip • Frame grabbers • 3 frame buffers • Red, Green and Blue

  5. Image Physics • Colour Depends on • Spectral reflectance of surface • Spectrum of illumination • Spectral response of sensors • Hue, Saturation, Intensity (HSI) • Intensity • Hue (light of a particular wavelength) • Saturation (degree of dominance of a colour)

  6. CIE standards for colour reproduction • CIE XYZ, CIE xyY, CIE L*u*v*, CIE L*a*b*,… • Colour Constancy • Illumination independent recognition • Match colours under varying illumination • Land Mondrian

  7. Blue Magenta Cyan S H Red Green Yellow Hue, Saturation, Intensity

  8. Other colour spaces • Opponent • YIQ (NTSC)

  9. Colour Vision • Why? • Feature tollerant to • Scale • Optical distortion • View point • A natural cue • Useful in addition to geometric features • But? • May not be intrinsic (can lepard change its spots) • Objects contain many colours

  10. Colour Image Processing • Pixel by pixel classification is error prone • Noise • Specular reflections • Hue unreliable when saturation is low • Saturation unreliable when intensity is low

  11. Colour Edges • Better quality edges than intensity alone • Extra computation • Fusion ? • Not many new edges

  12. Colour Histograms • Histograms tolerant to • Translation, rotation, scale and partial occlusion • Image Database retrieval • Swain and Ballard 1991 • Create colour histogram of images • Match histograms to retrieve images • Find similar images

  13. Colour Histograms • Reduce the complexity • 26, 24,… • Concatenate separate RGB histograms into one • Intersection of h(i) and h(m) min over all K bins • Match can normalise over those bins defined in the model • This removes the contribution of background pixels in h(i)

  14. Other metrics possible • Examples:

  15. Back Projection • Locate a region within an image containing a learned object • Remove intensity component • Smooth Histograms • Colour Profile • Characterize flaws • Recognize flaw signature

  16. Colour Profiles • Histogram • Good • Flaws • Profile • Colour unique to flaws • Classify based on unique colours

  17. Face Detection With Colour • Human Skin tones lie within narrow range • Face recognition • Image filters for porn on the web • Other objects also have similar colour • Colour Segmentation followed by • Connected component analysis • Morphology • Blob analysis

  18. Multi-spectral imaging • IR, X-ray, Radar, MRI….. • GIS systems • Medical systems • Pseudo Colour (Thematic) Images • Colour placed on images to communicate information • Doppler information on ultrasound images • Depth information on GIS images

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