Version 1
: Received: 9 August 2024 / Approved: 12 August 2024 / Online: 12 August 2024 (10:19:13 CEST)
How to cite:
Tominaga, S.; Sakai, H. Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks. Preprints2024, 2024080785. https://doi.org/10.20944/preprints202408.0785.v1
Tominaga, S.; Sakai, H. Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks. Preprints 2024, 2024080785. https://doi.org/10.20944/preprints202408.0785.v1
Tominaga, S.; Sakai, H. Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks. Preprints2024, 2024080785. https://doi.org/10.20944/preprints202408.0785.v1
APA Style
Tominaga, S., & Sakai, H. (2024). Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks. Preprints. https://doi.org/10.20944/preprints202408.0785.v1
Chicago/Turabian Style
Tominaga, S. and Hideaki Sakai. 2024 "Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks" Preprints. https://doi.org/10.20944/preprints202408.0785.v1
Abstract
In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combines model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
