Illuminant spectrum estimation to study animal coloration from multispectral camera images

Oregon Convention Center 

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.

multispectral imaging

function estimation

constrained estimation

P-splines

Bayesian inference 

Section on Nonparametric Statistics