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Color Image Processing Color Fundamentals (II) Color Fundamentals (I) Color Image Processing The visible light spectrum is continuous Image Processing with Biomedical Six Broad regions: Applications Violet, blue, green, yellow, orange, and red ELEG-475/675 Object color depends on what wavelengths it reflects Achromatic light is void of color (flat spectrum) Prof. Barner Characterization: intensity (gray level) Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 3 Color Image Processing Color Fundamentals (II) Full-color and pseudo-color processing Color vision Color space representations Color processing Chromatic light spectrum: 400-700 nm Correction Descriptive quantities: Enhancement Radiance – total energy that flows from a light source (Watts) Luminance – amount of energy and observer perceives from a Smoothing/sharpening light source (lumens) Segmentation Brightness – subjected descriptor of intensity Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 2 Color Image Processing University of Delaware 4 1 Vision Brightness and Chromaticity Response Cone response: Brightness – notion of intensity 6-7 million receptors Hue – an attribute associated with the dominant Red sensitive: 65% wavelength (color) Green sensitive: 33% The color of an object determines its hue Blue sensitive: 2% Saturation – relative purity, or the amount of white Most sensitive receptors light mixed with a hue Pure spectrum colors are fully saturated, e.g., red Primary colors: red (R), green (G), blue (B) Saturation is inversely proportional to the amount of white International Commission on Illumination (CIE) light in a color standard definitions: Chromaticity is hue and saturation together Blue (435.8 nm), Green (546.1 nm), Red (700 nm) A color may be characterized by its brightness and Defined in 1931 – doesn’t exactly match human perception chromaticity Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 5 Color Image Processing University of Delaware 7 Primary and Secondary Colors Tristimulus Representation Add primary colors to obtain Tristimulus values: X – red; Y – green; Z –blue secondary colors of light: Trichromatic coefficients: Magenta, cyan, and yellow X x = XYZ+ + Primarily colors of: Y y = Light – sources X +YZ+ Z Red, green, blue z = X + YZ+ Pigments – absorbs (subtracts) a x + yz+=1 primary color of light and reflects (transmits) the other two alternate approach: chromaticity diagram Magenta (absorbs green), cyan Gives color composition as a function of red (x) and green (absorbs red), and yellow (absorbs (y) blue) Solve for blue (z) according to the above Secondary pigments: Projects 3-D color space on to two dimensions Red, green, and blue Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 6 Color Image Processing University of Delaware 8 2 Chromaticity The RGB Color Model (Space) Diagram Pure colors are on the RGB is the most widely boundary used hardware-oriented Fully saturated Interior points are color space mixtures Graphics boards, monitors, A line between two cameras, etc. testing colors indicates all possible mixtures of the Normalized RGB values two colors Grayscale is a diagonal line Color gamut – triangle defined by three colors through the cube Three color mixtures are Quantization determines restricted to the gamut color depth No three-color gamut completely encloses the Full-color: 24-bit chromaticity diagram representations (16,777,216 colors) Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 9 Color Image Processing University of Delaware 11 Color Gamut Color Image Generation Examples RGB monitor color gamut Monochrome images Regular (triangular) shaped represent each color Based on three highly component controllable light Hyperplane examples: primaries Fix one dimension Printing device color gamut Example shows three hidden sides of the color Combination of additive cube and subtracted color mixing Acquisition process – Difficult control process reverse operations Neither gamut includes all colors Filter light to obtain RGB components Monitor is better Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 10 Color Image Processing University of Delaware 12 3 Safe RGB Colors (I) The CMY and CMYK Color Spaces Consistent color reproduction is problematic CMY – cyan, magenta, and yellow Plethora of hardware from different manufacturers CMYK – adds black Define a subset of colors to be faithfully reproduced on all hardware Black is difficult (and costly) to produce with CMY 256 colors Four-color printing Sufficient number to produce good images Subtracted primaries – widely used in printing Small enough set to be accurately reproduced 40 of these yield hardware specific results De facto safe RGB/Web/browser colors: 216 colors ⎡ CR⎤⎡⎤⎡⎤1 Formed as RGB triplets of values below ⎢ ⎥⎢⎥⎢⎥ ⎢M ⎥⎢⎥⎢⎥=−1 G ⎣⎢ YB⎦⎣⎦⎣⎦⎥⎢⎥⎢⎥1 Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 13 Color Image Processing University of Delaware 15 Safe RGB Colors (II) The HSI Color Space (I) 216 safe RGB colors Hue, saturation, intensity human perceptual descriptions 256 color RGB system of color includes 16 gray levels Decouples intensity (gray level) Six are in the 216 safe from hue and saturation colors (underlined) Rotate RGB cube so intensity is the vertical axis RGB said-color cube The intensity component of any color is its vertical component Saturation – distance from vertical axis Zero saturation: colors (gray values) on the vertical axis Fully saturated: pure colors on the cube boundaries Hue – primary color indicated as an angle of rotation Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 14 Color Image Processing University of Delaware 16 4 The HSI Color Space (II) HSI to RGB Conversion – Three Cases View the HSI space Case 1: RG sector (0°≤H≤120°) from top down B = IS(1− ) Slicing plane ⎡ SHcos ⎤ perpendicular to RI=+⎢1 ⎥ intensity ⎣ cos(60°−H ) ⎦ Intensity – height of GRB=1(−+ ) slicing plane Case 2: GB sector (120°≤H240°) Saturation – distance from HH= −°120 center (intensity R = IS(1− ) axis) ⎡ SHcos ⎤ GI=+⎢1 ⎥ Hue – rotation ⎣ cos(60°−H )⎦ angle from Red B = 1(−+RG ) Natural shape: Case 3: BR sector (240°≤H≤360°) hexagon HH= −°240 Normalized to circle or triangle GI= (1− S ) ⎡ SHcos ⎤ BI=+⎢1 ⎥ ⎣ cos(60°−H )⎦ R = 1(−+GB ) Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 17 Color Image Processing University of Delaware 19 RGB to HSI HSI Component Example (I) Conversion Common HSI representations RGB to HSI conversion θ if BG≤ H = { 360−>θ if BG ⎧⎫1 []()()RG−+− RB −1 ⎪⎪2 θ = cos ⎨⎬1 ⎪⎪⎡⎤()()()RG−+−2 RBGB − 2 ⎩⎭⎣⎦ 3 SRGB=−1min(,,)[] HSI representations of the color cube ()RGB++ 1 Normalized values represented as gray values I =++()RGB 3 Only values on surface of cube shown Result for normalized (circular) HSI representation Explain: Take care to note which HSI Sharp transition in hue representation is being used! Dark and light corners in saturation Uniform intensity Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 18 Color Image Processing University of Delaware 20 5 HSI Component Example (II) Density Slicing Example (I) Primary and secondary colors Eight color density slicing of thyroid Phantom HSI Density slicing enables visualization of variations representation and details Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 21 Color Image Processing University of Delaware 23 Pseudocolor Image Processing Density Slicing Example (II) Assigning colors to gray X-ray image of a values yields Pseudocolor weld (false color) images Density slicing to help assignment criteria is application-specific visualize cracks Intensity (density) slicing Assign colors based on gray value relation to slicing plane f (,xy )=∈ ckk if f(, xy ) V Special case: Thresholding Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 22 Color Image Processing University of Delaware 24 6 Density Slicing Example (III) Example: Airport x-ray Scanning System Sinusoidal color mappings Phase changes between components yield different results Greatest color changes at sinusoidal troughs Largest derivative First mapping: Highlights explosives Second mapping: Explosives and bag have similar mappings Explosive is “transparent” Image Processing Prof. Barner, ECE Department, Image Processing Prof. Barner, ECE Department, Color Image Processing University of Delaware 25 Color Image Processing University of Delaware 27 Gray Level to Color Transformations Multispectral Extensions Each color can be a dependent/independent Pseudo coloring is often used in the visualization of
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