The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une nouvelle méthode est proposée pour récupérer des séquences d'images dans le but de prévoir des modèles naturels complexes et variables dans le temps. À cette fin, nous introduisons un cadre appelé Memory-Based Forecasting ; il fournit des informations de prévision basées sur l'évolution temporelle des séquences récupérées passées. Cet article cible les modèles d'écho radar dans les images radar météorologiques et vise à réaliser une méthode de récupération d'images qui aide les prévisionnistes météorologiques à prédire les précipitations locales. Pour caractériser les modèles d'écho radar, une représentation basée sur l'apparence du modèle d'écho et son champ de vitesse sont utilisés. Des caractéristiques de texture temporelles sont introduites pour représenter des caractéristiques de motif locales, notamment un mouvement complexe non rigide. De plus, le développement temporel d'une séquence est représenté sous forme de chemins dans les espaces propres des caractéristiques de l'image, et une distance normalisée entre deux séquences dans l'espace propre est proposée comme mesure de dissimilarité utilisée pour récupérer des séquences similaires. Plusieurs expériences confirment les bonnes performances du schéma de récupération proposé et indiquent la prévisibilité de la séquence d'images.
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Kazuhiro OTSUKA, Tsutomu HORIKOSHI, Haruhiko KOJIMA, Satoshi SUZUKI, "Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 7, pp. 1458-1465, July 2000, doi: .
Abstract: A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_7_1458/_p
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@ARTICLE{e83-d_7_1458,
author={Kazuhiro OTSUKA, Tsutomu HORIKOSHI, Haruhiko KOJIMA, Satoshi SUZUKI, },
journal={IEICE TRANSACTIONS on Information},
title={Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern},
year={2000},
volume={E83-D},
number={7},
pages={1458-1465},
abstract={A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.},
keywords={},
doi={},
ISSN={},
month={July},}
Copier
TY - JOUR
TI - Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern
T2 - IEICE TRANSACTIONS on Information
SP - 1458
EP - 1465
AU - Kazuhiro OTSUKA
AU - Tsutomu HORIKOSHI
AU - Haruhiko KOJIMA
AU - Satoshi SUZUKI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2000
AB - A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.
ER -