Multispectral imaging based on natural neighbor interpolation with rendering under various illuminations

Tzren-ru Chou, Chi Heng Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this study, we constructed the multispectral imaging by the reconstruction of spectral reflectance. This reconstruction method was based on natural neighbor interpolation, abbreviated as NNI. The experiments focused on the feasibility of the reconstruction of reflectance spectra with multispectral images, and the experimental targets were divided into two types; paper packages, and portraits. The performance was evaluated in terms of root mean square error (RMSE), color difference (?E), and peak signal-tonoise ratio (PSNR). The experimental results showed that the proposed NNI reconstruction method was able to directly generate target spectra from sRGB channel values accurately. Furthermore, even after rendering under various illuminations, the average of color difference was still within an acceptable range.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Advances in Image Processing, ICAIP 2017
PublisherAssociation for Computing Machinery
Pages45-49
Number of pages5
ISBN (Electronic)9781450352956
DOIs
Publication statusPublished - 2017 Aug 25
Event2017 International Conference on Advances in Image Processing, ICAIP 2017 - Bangkok, Thailand
Duration: 2017 Aug 252017 Aug 27

Publication series

NameACM International Conference Proceeding Series
VolumePart F131200

Other

Other2017 International Conference on Advances in Image Processing, ICAIP 2017
CountryThailand
CityBangkok
Period17/8/2517/8/27

Fingerprint

Interpolation
Lighting
Color
Imaging techniques
Mean square error
Experiments

Keywords

  • Natural neighbor interpolation
  • Reconstruction of spectral reflectance
  • Spectral reflectance

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Chou, T., & Hsieh, C. H. (2017). Multispectral imaging based on natural neighbor interpolation with rendering under various illuminations. In Proceedings of 2017 International Conference on Advances in Image Processing, ICAIP 2017 (pp. 45-49). (ACM International Conference Proceeding Series; Vol. Part F131200). Association for Computing Machinery. https://doi.org/10.1145/3133264.3133269

Multispectral imaging based on natural neighbor interpolation with rendering under various illuminations. / Chou, Tzren-ru; Hsieh, Chi Heng.

Proceedings of 2017 International Conference on Advances in Image Processing, ICAIP 2017. Association for Computing Machinery, 2017. p. 45-49 (ACM International Conference Proceeding Series; Vol. Part F131200).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chou, T & Hsieh, CH 2017, Multispectral imaging based on natural neighbor interpolation with rendering under various illuminations. in Proceedings of 2017 International Conference on Advances in Image Processing, ICAIP 2017. ACM International Conference Proceeding Series, vol. Part F131200, Association for Computing Machinery, pp. 45-49, 2017 International Conference on Advances in Image Processing, ICAIP 2017, Bangkok, Thailand, 17/8/25. https://doi.org/10.1145/3133264.3133269
Chou T, Hsieh CH. Multispectral imaging based on natural neighbor interpolation with rendering under various illuminations. In Proceedings of 2017 International Conference on Advances in Image Processing, ICAIP 2017. Association for Computing Machinery. 2017. p. 45-49. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3133264.3133269
Chou, Tzren-ru ; Hsieh, Chi Heng. / Multispectral imaging based on natural neighbor interpolation with rendering under various illuminations. Proceedings of 2017 International Conference on Advances in Image Processing, ICAIP 2017. Association for Computing Machinery, 2017. pp. 45-49 (ACM International Conference Proceeding Series).
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