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Exploring Color Perception and Imaging Techniques in Digital Processing

This document delves into the intricate field of color perception and processing, covering essential concepts such as the physics of light, human perception of colors, and technological advancements in hardware like color cameras and frame grabbers. It discusses color representation models including CIE standards, hue, saturation, and intensity (HSI), along with challenges in pixel classification and color histograms for image retrieval. Additionally, it addresses applications like face detection and the use of multi-spectral imaging in various systems, showcasing the complexity of color in both perception and technological implementation.

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Exploring Color Perception and Imaging Techniques in Digital Processing

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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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