@misc{10481/42789,
year = {2016},
url = {http://hdl.handle.net/10481/42789},
abstract = {Spectral imaging systems have been used for spectral measurements for several decades,
mainly for scienti c purposes, and were usually linked to costly applications. Thanks to
research in imaging science and machine learning and the technological advancement in
recent years, spectral imaging became feasible for various industrial and even consumer
applications.
This dissertation deals with line-scan multi-spectral imaging systems for spectral re-

ectance and color measurements. The most important aspects under consideration are
optimization of spectral properties of the optical components, image registration and estimation
of surface spectral re
ectances. We focus on a particular system design, in which
multiple color  ltered RGB images with distinct spectral content are acquired at the same
time. These images correspond to di erent viewpoints of the scanning scene due to the
mechanical arrangement of camera sensor and optics.
By optimizing the system's optical component spectral properties, the amount of spectral
information acquired can be increased and the spectral re
ectance estimation can be
improved. We propose a  lter selection framework and demonstrate that optimization for
various line-scan system con gurations results in an improvement of spectral and color
measurement performance. Multi-channel image registration is required to account for
viewpoint di erences and other sources of image channel misalignment. We develop a
calibration scheme for planar scanning objects and propose scene-adaptive registration
for non-planar scanning objects. For our 12-channel laboratory imaging system, subpixel
accuracy is achieved. Based on the registered multi-channel image data, spectral
re
ectance estimation can be performed. Physical and empirical estimation methods are
considered, and we propose a logarithmic kernel function for kernel ridge regression. We
experimentally compare performance of various estimation methods for simulated and
measured camera response data and consider di erent noise levels and number of spectral
channels. Empirical estimation performance is in
uenced by model training. We compare
various training sample selection approaches and propose an application dependent
selection scheme. Further, adaptive training methods from related literature are uni ed
conceptually and evaluated systematically.
We show that the aforementioned aspects of line-scan multi-spectral imaging system
design are critical for spectral and color measurement, and that application speci c design
is often bene cial to improve system performance.},
organization = {Tesis Univ. Granada. Programa oficial de doctorado en Física y Ciencias del Espacio},
publisher = {Universidad de Granada},
keywords = {Sistemas de imágenes},
keywords = {Astrofísica},
keywords = {Espectroscopia},
keywords = {Interferometría},
keywords = {Procesado de imágenes},
keywords = {Reflectancia},
keywords = {Sistemas de barrido},
title = {Design considerations for line-scan multi-spectral imaging systems},
author = {Eckhard, Timo Micha},
}