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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 연구에서는 눈 입자를 눈송이와 Graupel로 분류하기 위한 세 가지 영상 처리 시스템을 제시합니다. 이들 모두는 기능 분류를 기반으로 하지만 모든 경우에 참신함으로 여러 기능이 활용됩니다. 또한 각각은 서로 다른 데이터 흐름을 특징으로 합니다. 성능을 비교하기 위해 다양한 특징을 고려할 뿐만 아니라 다양한 분류기를 제안합니다. 가장 좋은 결과는 통계 분류기 이전에 적용된 눈송이 식별 방법에 대한 것이며 이 경우 정확한 분류 비율은 94%에 달합니다. 다른 경우에는 최상의 결과가 약 88%입니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Karolina NURZYNSKA, Mamoru KUBO, Ken-ichiro MURAMOTO, "2D Feature Space for Snow Particle Classification into Snowflake and Graupel" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 12, pp. 3344-3351, December 2010, doi: 10.1587/transinf.E93.D.3344.
Abstract: This study presents three image processing systems for snow particle classification into snowflake and graupel. All of them are based on feature classification, yet as a novelty in all cases multiple features are exploited. Additionally, each of them is characterized by a different data flow. In order to compare the performances, we not only consider various features, but also suggest different classifiers. The best achieved results are for the snowflake discrimination method applied before statistical classifier, as the correct classification ratio in this case reaches 94%. In other cases the best results are around 88%.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3344/_p
부
@ARTICLE{e93-d_12_3344,
author={Karolina NURZYNSKA, Mamoru KUBO, Ken-ichiro MURAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={2D Feature Space for Snow Particle Classification into Snowflake and Graupel},
year={2010},
volume={E93-D},
number={12},
pages={3344-3351},
abstract={This study presents three image processing systems for snow particle classification into snowflake and graupel. All of them are based on feature classification, yet as a novelty in all cases multiple features are exploited. Additionally, each of them is characterized by a different data flow. In order to compare the performances, we not only consider various features, but also suggest different classifiers. The best achieved results are for the snowflake discrimination method applied before statistical classifier, as the correct classification ratio in this case reaches 94%. In other cases the best results are around 88%.},
keywords={},
doi={10.1587/transinf.E93.D.3344},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - 2D Feature Space for Snow Particle Classification into Snowflake and Graupel
T2 - IEICE TRANSACTIONS on Information
SP - 3344
EP - 3351
AU - Karolina NURZYNSKA
AU - Mamoru KUBO
AU - Ken-ichiro MURAMOTO
PY - 2010
DO - 10.1587/transinf.E93.D.3344
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2010
AB - This study presents three image processing systems for snow particle classification into snowflake and graupel. All of them are based on feature classification, yet as a novelty in all cases multiple features are exploited. Additionally, each of them is characterized by a different data flow. In order to compare the performances, we not only consider various features, but also suggest different classifiers. The best achieved results are for the snowflake discrimination method applied before statistical classifier, as the correct classification ratio in this case reaches 94%. In other cases the best results are around 88%.
ER -