Abstract Number:
2825
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yang Long (1), David Kepplinger (1), Daniel Hanley (1)
Institutions:
(1) George Mason University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Multispectral camera images have been playing a crucial role in animal vision research. Reconstructing animal vision from static images typically involves camera calibration, spectral reflectance estimation, and linear transformation of camera responses to animal quantum catches - a procedure that lacks transferability across scenes as camera recalibration is required for changes in illumination settings. In this study, we propose a novel yet simple framework for studying animal vision using multispectral consumer cameras. This framework requires additional estimation of the spectral illumination using a basis representation, with the advantage of needing only a one-time camera calibration. Unlike typical regression problems involving basis functions, only camera readings are observed, which are the inner products of spectral illumination, sensitivity, and reflectance. To address this, the coefficients are estimated using transformed basis functions, and Bayesian interval estimation is applied. This framework enables the reconstruction of animal vision with moving objects and variable lighting, paving the ways for generating animal vision videos in natural habitats.
Keywords:
multispectral imaging|function estimation |constrained estimation |P-splines |Bayesian inference |
Sponsors:
Section on Nonparametric Statistics
Tracks:
Applications of nonparametric methods
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