Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model

This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When obser...

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Autores principales:	Carlo Alberto Avizzano, Gabriele Scivoletto, Paolo Tripicchio
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Kalman filter Gaussian mixtures image stitching rolling stock digital twin Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0a3558d80d294bc99b87caf5b776fd22
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spelling	oai:doaj.org-article:0a3558d80d294bc99b87caf5b776fd222021-11-24T00:02:37ZRobust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model2169-353610.1109/ACCESS.2021.3128564https://doaj.org/article/0a3558d80d294bc99b87caf5b776fd222021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615245/https://doaj.org/toc/2169-3536This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When observing large surfaces with nearly constant textures, metallic reflections, and repetitive patterns, motion estimation algorithms based on whole image error minimization and feature pairing with Random Sampling and Consensus (RANSAC) or Least Median of Squares (LMedS) fail to provide appropriate associations. To overcome such an issue, we propose a custom Kalman Filter (KF) modified by adding multiple input-noise sources represented as a Gaussian mixture distribution (GM), and specific algorithms to select appropriate data and variance to use for state prediction and correction. The proposed algorithm has been tested on images of train vessels, having a high number of windows, and large metallic paintings with constant or repetitive patterns. The approach here presented showed to be robust in the presence of high environmental disturbances and a reduced number of features. A large set of rolling stocks has been collected during a six months campaign. The set was employed to demonstrate the validity of the proposed algorithm by comparing the reconstructed twin versus known data. The system showed an overall accuracy in length estimation above 99%.Carlo Alberto AvizzanoGabriele ScivolettoPaolo TripicchioIEEEarticleKalman filterGaussian mixturesimage stitchingrolling stockdigital twinElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154011-154021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Kalman filter Gaussian mixtures image stitching rolling stock digital twin Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Kalman filter Gaussian mixtures image stitching rolling stock digital twin Electrical engineering. Electronics. Nuclear engineering TK1-9971 Carlo Alberto Avizzano Gabriele Scivoletto Paolo Tripicchio Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model
description	This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When observing large surfaces with nearly constant textures, metallic reflections, and repetitive patterns, motion estimation algorithms based on whole image error minimization and feature pairing with Random Sampling and Consensus (RANSAC) or Least Median of Squares (LMedS) fail to provide appropriate associations. To overcome such an issue, we propose a custom Kalman Filter (KF) modified by adding multiple input-noise sources represented as a Gaussian mixture distribution (GM), and specific algorithms to select appropriate data and variance to use for state prediction and correction. The proposed algorithm has been tested on images of train vessels, having a high number of windows, and large metallic paintings with constant or repetitive patterns. The approach here presented showed to be robust in the presence of high environmental disturbances and a reduced number of features. A large set of rolling stocks has been collected during a six months campaign. The set was employed to demonstrate the validity of the proposed algorithm by comparing the reconstructed twin versus known data. The system showed an overall accuracy in length estimation above 99%.
format	article
author	Carlo Alberto Avizzano Gabriele Scivoletto Paolo Tripicchio
author_facet	Carlo Alberto Avizzano Gabriele Scivoletto Paolo Tripicchio
author_sort	Carlo Alberto Avizzano
title	Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model
title_short	Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model
title_full	Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model
title_fullStr	Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model
title_full_unstemmed	Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model
title_sort	robust image stitching and reconstruction of rolling stocks using a novel kalman filter with a multiple-hypothesis measurement model
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0a3558d80d294bc99b87caf5b776fd22
work_keys_str_mv	AT carloalbertoavizzano robustimagestitchingandreconstructionofrollingstocksusinganovelkalmanfilterwithamultiplehypothesismeasurementmodel AT gabrielescivoletto robustimagestitchingandreconstructionofrollingstocksusinganovelkalmanfilterwithamultiplehypothesismeasurementmodel AT paolotripicchio robustimagestitchingandreconstructionofrollingstocksusinganovelkalmanfilterwithamultiplehypothesismeasurementmodel
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Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter With a Multiple-Hypothesis Measurement Model

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