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
Pengesanan ufuk berguna dalam pemprosesan imej maritim untuk pelbagai tujuan, seperti anggaran orientasi kamera, pendaftaran bingkai berturut-turut dan sekatan kawasan carian objek. Kaedah pengesanan ufuk sedia ada adalah berdasarkan pengekstrakan tepi. Untuk ketepatan, mereka menggunakan berbilang imej, yang ditapis dengan saiz penapis yang berbeza. Walau bagaimanapun, ini meningkatkan masa pemprosesan. Di samping itu, kaedah ini tidak kukuh untuk mengaburkan. Oleh itu, kami membangunkan kaedah pengesanan ufuk tanpa mengekstrak calon daripada maklumat tepi dengan merumuskan masalah pengesanan ufuk sebagai masalah pengoptimuman global. Garis ufuk dalam satah imej diwakili oleh dua parameter, yang dioptimumkan oleh algoritma evolusi (algoritma genetik). Oleh itu, ciri tempatan dan global suatu ufuk digunakan serentak dalam proses pengoptimuman, yang dipercepatkan dengan menggunakan strategi kasar hingga halus. Akibatnya, kami dapat mengesan garis ufuk pada imej maritim resolusi tinggi dalam kira-kira 50ms. Prestasi kaedah yang dicadangkan telah diuji pada 49 video set data marin Singapura dan set data Buoy, yang mengandungi lebih 16000 bingkai di bawah senario berbeza. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan boleh mencapai ketepatan yang lebih tinggi daripada kaedah terkini.
Uuganbayar GANBOLD
Iwate University
Junya SATO
Gifu University
Takuya AKASHI
Iwate University
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Salinan
Uuganbayar GANBOLD, Junya SATO, Takuya AKASHI, "Coarse-to-Fine Evolutionary Method for Fast Horizon Detection in Maritime Images" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 12, pp. 2226-2236, December 2021, doi: 10.1587/transinf.2021EDP7064.
Abstract: Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7064/_p
Salinan
@ARTICLE{e104-d_12_2226,
author={Uuganbayar GANBOLD, Junya SATO, Takuya AKASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Coarse-to-Fine Evolutionary Method for Fast Horizon Detection in Maritime Images},
year={2021},
volume={E104-D},
number={12},
pages={2226-2236},
abstract={Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2021EDP7064},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Coarse-to-Fine Evolutionary Method for Fast Horizon Detection in Maritime Images
T2 - IEICE TRANSACTIONS on Information
SP - 2226
EP - 2236
AU - Uuganbayar GANBOLD
AU - Junya SATO
AU - Takuya AKASHI
PY - 2021
DO - 10.1587/transinf.2021EDP7064
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2021
AB - Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.
ER -