07. Discrete wavelength models sensors - 2011

# 07. Discrete wavelength models sensors - 2011 - ECE 638...

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ECE 638: Principles of Digital Color Imaging Systems Lecture 7: Discrete Wavelength Models Sensors

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Synopsis Discretization of model Sensor subspace Decomposition of stimulus into fundamental and nullspace components Graphical interpretation
Why discretize the model? So far we have assumed that is continuous Upside of this assumption matches real world Downside of this assumption for computation, we must discretize the model we can gain further intuition if we descretize the model 400 nm ≤ λ ≤ 700 nm

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Discretization of wavelength It is common practice to make the following choices: This implies that we need samples to characterize the visible spectrum. M = 31 λ k = ( k 1) Δλ + min , k = 1, , M min = 1 = 400 nm max = M = 700 nm Δλ = 10 nm
Notation This material is taken from the paper by Mark Wolski: “A review of linear color descriptor spaces and their applications,” which is available for download at the course website. Wolski’s notation differs somewhat from that used thus far to describe the continuous-wavelength trichromatic model. For ease of cross-reference between the lecture notes and Wolski’s paper, we will follow his notation here.

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Discrete-wavelength trichomatic model Stimulus
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## This note was uploaded on 02/19/2012 for the course ECE 638 taught by Professor Staff during the Fall '08 term at Purdue.

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07. Discrete wavelength models sensors - 2011 - ECE 638...

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