Sequential adaptive estimation for spectral reflectance based on camera responses

Rank-based camera spectral sensitivity estimation.

In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab-a difficult and lengthy procedure-or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral st...

Performances of classic and weighted versions of some selected methods in estimation of spectral data from camera responses

Supervised training sample selection for the estimation of spectral reflectance using a RGB camera

Spectral imaging can provide spectral information from which spectral radiance or reflectance can be recovered at each image pixel. Recovery algorithms lead to good spectral and colorimetric performance by directly transforming RGB digital counts to spectral reflectances, but his approach is sensitive to the size and composition of the training set. What we propose here is a supervised method t...

Preliminary Experiment for Spectral Reflectance Estimation of Human Iris using a Digital Camera

1) Goal – Perform preliminary experiment to check the feasibility and evaluate the accuracy of spectral estimation using a digital camera. 2) Background Multi-spectral acquisition and linear methods have been widely used to estimate spectral properties of imaged materials. 1 2 0 0 O , (1) and the object spectral reflectance as r=(r 1 , r 2 , ... r n) T , where the index indicates the set of n w...

Noise adaptive speech recognition based on sequential noise parameter estimation

In this paper, a noise adaptive speech recognition approach is proposed for recognizing speech which is corrupted by additive non-stationary background noise. The approach sequentially estimates noise parameters, through which a nonlinear parametric function adapts mean vectors of acoustic models. In the estimation process, posterior probability of state sequence given observation sequence and ...