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Open Access Illumination and Reflectance Spectra Separation of Hyperspectral Image Data under Multiple Illumination Conditions

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Recently, a remarkably simple method was developed to solve the illumination and reflectance spectra separation problem (IRSS) based on the standard low-dimensionality assumption of reflectance. However, because this method assumes the scene is under one uniform illumination, it can not handle scene contains multiple illuminations or dominant shadows. In this paper, we address this problem by formulating the multiple illuminations and reflectance separation problem as a Conditional Random Field (CRF) optimization task over local separations. We then improve local illumination and reflectance separation by incorporating spatial information in each local patch.

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Keywords: COLOR CONSTANCY; HYPERSPECTRAL IMAGE; ILLUMINATION AND REFLECTANCE SPECTRA SEPARATION (IRSS); ILLUMINATION ESTIMATION; SHADOW IMAGE

Document Type: Research Article

Publication date: January 29, 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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