Noise prism : a novel multispectral visualization technique
Canham, Trevor (York University)
Vázquez i Corral, Javier (Universitat Autònoma de Barcelona)
Long, David L. (Rochester Institute of Technology)
Murray, Richard F. (York University)
Brown, Michael S. (York University)

Imprint:	Springfield, VA : Society for Imaging Science and Technology, 2023
Description:	6 pàg.
Abstract:	A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method's information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique.
Grants:	Agencia Estatal de Investigación PID2021-128178OB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-01499
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Capítol de llibre ; recerca ; Versió publicada
Subject:	Multispectral imaging ; Visualization ; Noise
Published in:	31st Color and Imaging Conference Final Program and Proceedings, (Nov. 2023) , p. 193 - 198

DOI: 10.2352/CIC.2023.31.1.36

6 p, 3.2 MB

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