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
철도교통의 지능형 운영 및 유지관리 시스템으로부터 획득된 차량 상태 영상에 대한 새로운 적응형 이진화 방법을 제안한다. 이 방법은 철도 교통의 지능형 운영 및 유지 관리 시스템에서 해당 차량 상태 정보를 보다 빠르고 효과적으로 확인할 수 있으며, 차량 운영 상태를 실시간으로 추적 및 모니터링하고 시스템의 비상 대응 능력을 향상시킬 수 있습니다. 제안한 방법의 장점은 크게 두 가지이다. 탈색을 위해 대비 보존 탈색 방법[1]을 사용하여 차량 상태 영상 배경의 색상 정보를 최대한 유지할 수 있는 RGB 영상의 계조에 대한 R, G, B의 적절한 비율을 구하고 이를 유지한다. 전경과 배경의 대비. 임계값 선택 측면에서는 주요 클러스터 추정[2]을 이용하여 차량 상태 영상의 다색 배경에 해당하는 회색값의 평균값과 표준편차를 구하고 이진화를 위한 2 시그마 원리에 의해 적응형 임계값을 결정하며, 텍스트, 식별자 및 기타 대상 정보를 효과적으로 추출할 수 있습니다. 실험 결과는 배경 색상 정보가 풍부한 차량 상태 이미지에 대해 이 방법이 전역 임계값 Otsu 알고리즘[3] 및 로컬 임계값 Sauvola 알고리즘[4],[5]과 같은 기존 이진화 방법보다 우수함을 보여줍니다. 임계값 기반, Mean-Shift 알고리즘[6], K-Means 알고리즘[7] 및 통계 학습 기반 Fuzzy C Means[8] 알고리즘. 지능형 철도교통 데이터 검증을 위한 영상 전처리 기법으로, 다양한 차량 상태의 영상이 포함된 데이터 세트를 통해 광학 문자 인식을 검증함으로써 텍스트 및 식별자 인식의 정확도를 효과적으로 향상시킬 수 있다.
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부
Ye TIAN, Mei HAN, "Adaptive Binarization for Vehicle State Images Based on Contrast Preserving Decolorization and Major Cluster Estimation" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 679-688, March 2022, doi: 10.1587/transinf.2021EDP7218.
Abstract: A new adaptive binarization method is proposed for the vehicle state images obtained from the intelligent operation and maintenance system of rail transit. The method can check the corresponding vehicle status information in the intelligent operation and maintenance system of rail transit more quickly and effectively, track and monitor the vehicle operation status in real time, and improve the emergency response ability of the system. The advantages of the proposed method mainly include two points. For decolorization, we use the method of contrast preserving decolorization[1] obtain the appropriate ratio of R, G, and B for the grayscale of the RGB image which can retain the color information of the vehicle state images background to the maximum, and maintain the contrast between the foreground and the background. In terms of threshold selection, the mean value and standard deviation of gray value corresponding to multi-color background of vehicle state images are obtained by using major cluster estimation[2], and the adaptive threshold is determined by the 2 sigma principle for binarization, which can extract text, identifier and other target information effectively. The experimental results show that, regarding the vehicle state images with rich background color information, this method is better than the traditional binarization methods, such as the global threshold Otsu algorithm[3] and the local threshold Sauvola algorithm[4],[5] based on threshold, Mean-Shift algorithm[6], K-Means algorithm[7] and Fuzzy C Means[8] algorithm based on statistical learning. As an image preprocessing scheme for intelligent rail transit data verification, the method can improve the accuracy of text and identifier recognition effectively by verifying the optical character recognition through a data set containing images of different vehicle statuses.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7218/_p
부
@ARTICLE{e105-d_3_679,
author={Ye TIAN, Mei HAN, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Binarization for Vehicle State Images Based on Contrast Preserving Decolorization and Major Cluster Estimation},
year={2022},
volume={E105-D},
number={3},
pages={679-688},
abstract={A new adaptive binarization method is proposed for the vehicle state images obtained from the intelligent operation and maintenance system of rail transit. The method can check the corresponding vehicle status information in the intelligent operation and maintenance system of rail transit more quickly and effectively, track and monitor the vehicle operation status in real time, and improve the emergency response ability of the system. The advantages of the proposed method mainly include two points. For decolorization, we use the method of contrast preserving decolorization[1] obtain the appropriate ratio of R, G, and B for the grayscale of the RGB image which can retain the color information of the vehicle state images background to the maximum, and maintain the contrast between the foreground and the background. In terms of threshold selection, the mean value and standard deviation of gray value corresponding to multi-color background of vehicle state images are obtained by using major cluster estimation[2], and the adaptive threshold is determined by the 2 sigma principle for binarization, which can extract text, identifier and other target information effectively. The experimental results show that, regarding the vehicle state images with rich background color information, this method is better than the traditional binarization methods, such as the global threshold Otsu algorithm[3] and the local threshold Sauvola algorithm[4],[5] based on threshold, Mean-Shift algorithm[6], K-Means algorithm[7] and Fuzzy C Means[8] algorithm based on statistical learning. As an image preprocessing scheme for intelligent rail transit data verification, the method can improve the accuracy of text and identifier recognition effectively by verifying the optical character recognition through a data set containing images of different vehicle statuses.},
keywords={},
doi={10.1587/transinf.2021EDP7218},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - Adaptive Binarization for Vehicle State Images Based on Contrast Preserving Decolorization and Major Cluster Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 679
EP - 688
AU - Ye TIAN
AU - Mei HAN
PY - 2022
DO - 10.1587/transinf.2021EDP7218
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - A new adaptive binarization method is proposed for the vehicle state images obtained from the intelligent operation and maintenance system of rail transit. The method can check the corresponding vehicle status information in the intelligent operation and maintenance system of rail transit more quickly and effectively, track and monitor the vehicle operation status in real time, and improve the emergency response ability of the system. The advantages of the proposed method mainly include two points. For decolorization, we use the method of contrast preserving decolorization[1] obtain the appropriate ratio of R, G, and B for the grayscale of the RGB image which can retain the color information of the vehicle state images background to the maximum, and maintain the contrast between the foreground and the background. In terms of threshold selection, the mean value and standard deviation of gray value corresponding to multi-color background of vehicle state images are obtained by using major cluster estimation[2], and the adaptive threshold is determined by the 2 sigma principle for binarization, which can extract text, identifier and other target information effectively. The experimental results show that, regarding the vehicle state images with rich background color information, this method is better than the traditional binarization methods, such as the global threshold Otsu algorithm[3] and the local threshold Sauvola algorithm[4],[5] based on threshold, Mean-Shift algorithm[6], K-Means algorithm[7] and Fuzzy C Means[8] algorithm based on statistical learning. As an image preprocessing scheme for intelligent rail transit data verification, the method can improve the accuracy of text and identifier recognition effectively by verifying the optical character recognition through a data set containing images of different vehicle statuses.
ER -