@Article{CristhianA.Aguilera-Carrasco2017,
author="Cristhian A. Aguilera-Carrasco
and Angel Sappa
and Cristhian Aguilera
and Ricardo Toledo",
title="Cross-Spectral Local Descriptors via Quadruplet Network",
journal="Sensors",
year="2017",
volume="17",
number="4",
pages="873",
abstract="This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.",
optnote="ADAS; 600.086; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2914), last updated on Mon, 24 Jan 2022 10:05:38 +0100",
doi="10.3390/s17040873",
file=":http://refbase.cvc.uab.es/files/ASA2017.pdf:PDF"
}