Filter Selection for Hyperspectral Estimation

Boaz Arad; Ohad Ben-Shahar

doi:10.1109/ICCV.2017.342

Filter Selection for Hyperspectral Estimation

Boaz Arad, Ohad Ben-Shahar

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

47 Scopus citations

Abstract

While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this paper we demonstrate that optimal selection of camera response filters may improve hyperspectral estimation accuracy by over 33%, emphasizing the importance of considering and selecting these response profiles wisely. Additionally, we present an evolutionary optimization methodology for optimal filter set selection from very large filter spaces, an approach that facilitates practical selection from families of customizable filters or filter optimization for multispectral cameras with more than 3 channels.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
Publisher	Institute of Electrical and Electronics Engineers
Pages	3172-3180
Number of pages	9
ISBN (Electronic)	9781538610329
DOIs	https://doi.org/10.1109/ICCV.2017.342
State	Published - 22 Dec 2017
Event	16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy Duration: 22 Oct 2017 → 29 Oct 2017

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2017-October
ISSN (Print)	1550-5499

Conference

Conference	16th IEEE International Conference on Computer Vision, ICCV 2017
Country/Territory	Italy
City	Venice
Period	22/10/17 → 29/10/17

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCV.2017.342

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Arad, B., & Ben-Shahar, O. (2017). Filter Selection for Hyperspectral Estimation. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 3172-3180). Article 8237604 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCV.2017.342

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title = "Filter Selection for Hyperspectral Estimation",

abstract = "While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this paper we demonstrate that optimal selection of camera response filters may improve hyperspectral estimation accuracy by over 33%, emphasizing the importance of considering and selecting these response profiles wisely. Additionally, we present an evolutionary optimization methodology for optimal filter set selection from very large filter spaces, an approach that facilitates practical selection from families of customizable filters or filter optimization for multispectral cameras with more than 3 channels.",

author = "Boaz Arad and Ohad Ben-Shahar",

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year = "2017",

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doi = "10.1109/ICCV.2017.342",

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Arad, B & Ben-Shahar, O 2017, Filter Selection for Hyperspectral Estimation. in Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017., 8237604, Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-October, Institute of Electrical and Electronics Engineers, pp. 3172-3180, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22/10/17. https://doi.org/10.1109/ICCV.2017.342

Filter Selection for Hyperspectral Estimation. / Arad, Boaz; Ben-Shahar, Ohad.
Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers, 2017. p. 3172-3180 8237604 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this paper we demonstrate that optimal selection of camera response filters may improve hyperspectral estimation accuracy by over 33%, emphasizing the importance of considering and selecting these response profiles wisely. Additionally, we present an evolutionary optimization methodology for optimal filter set selection from very large filter spaces, an approach that facilitates practical selection from families of customizable filters or filter optimization for multispectral cameras with more than 3 channels.

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Filter Selection for Hyperspectral Estimation

Abstract

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