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Image/Video Storage, Retrieval

LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION


Noisy image segmentation is one of the most important and challenging problem in computer vision. In this paper, we propose a level set segmentation technique inspired by the classic Otsu thresholding method. The front propagation of the proposed level set based method embeds a cost function that takes into account first-order statistical moments. In order to deal with highly noisy images, we also added a morphological step to our algorithm which led the final segmentation more robust and efficient.

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Authors:
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques
Submitted On:
6 September 2017 - 10:35am
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[1] Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques, "LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1844. Accessed: Sep. 20, 2020.
@article{1844-17,
url = {http://sigport.org/1844},
author = { Alan M. Braga; Fátima N. S. de Medeiros; Regis C. P. Marques },
publisher = {IEEE SigPort},
title = {LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION},
year = {2017} }
TY - EJOUR
T1 - LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION
AU - Alan M. Braga; Fátima N. S. de Medeiros; Regis C. P. Marques
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1844
ER -
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques. (2017). LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION. IEEE SigPort. http://sigport.org/1844
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques, 2017. LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION. Available at: http://sigport.org/1844.
Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques. (2017). "LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION." Web.
1. Alan M. Braga, Fátima N. S. de Medeiros, Regis C. P. Marques. LEVEL-SET FORMULATION BASED ON OTSU METHOD WITH MORPHOLOGICAL REGULARIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1844

Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation

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Authors:
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao
Submitted On:
3 September 2017 - 3:43am
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[1] Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao, "Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1821. Accessed: Sep. 20, 2020.
@article{1821-17,
url = {http://sigport.org/1821},
author = {Cheng Pang; Hongdong Li; Anoop Cherian; Hongxun Yao },
publisher = {IEEE SigPort},
title = {Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation},
year = {2017} }
TY - EJOUR
T1 - Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation
AU - Cheng Pang; Hongdong Li; Anoop Cherian; Hongxun Yao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1821
ER -
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao. (2017). Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation. IEEE SigPort. http://sigport.org/1821
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao, 2017. Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation. Available at: http://sigport.org/1821.
Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao. (2017). "Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation." Web.
1. Cheng Pang, Hongdong Li, Anoop Cherian, Hongxun Yao. Part Based Fine-grained Bird Image Retrieval Respecting Species Correlation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1821

EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS


Selecting authentic scenes about activities of daily living (ADL) is useful to support our memory of everyday life. Key-frame extraction for first-person vision (FPV) videos is a core technology to realize such memory assistant. However, most existing key-frame extraction methods have mainly focused on stable scenes not related to ADL and only used visual signals of the image sequence even though the activities usually associate with our visual experience. To deal with dynamically changing scenes of FPV about daily activities, integrating motion and visual signals are essential.

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Authors:
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe
Submitted On:
1 September 2017 - 2:09am
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[1] Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe, "EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1816. Accessed: Sep. 20, 2020.
@article{1816-17,
url = {http://sigport.org/1816},
author = {Yujie Li; Atsunori Kanemura; Hideki Asoh; Taiki Miyanishi; Motoaki Kawanabe },
publisher = {IEEE SigPort},
title = {EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS},
year = {2017} }
TY - EJOUR
T1 - EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS
AU - Yujie Li; Atsunori Kanemura; Hideki Asoh; Taiki Miyanishi; Motoaki Kawanabe
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1816
ER -
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. (2017). EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS. IEEE SigPort. http://sigport.org/1816
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe, 2017. EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS. Available at: http://sigport.org/1816.
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. (2017). "EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS." Web.
1. Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. EXTRACTING KEY FRAMES FROM FIRST-PERSON VIDEOS IN THE COMMON SPACE OF MULTIPLE SENSORS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1816

Image Deblurring in the presence of Salt-and-Pepper Noise


This work addresses image recovery problem in the presence of salt-and-pepper noise and image blur. The salt-and-pepper noise reviewed as the impulsive noise, in this paper, is modeled as a sparse signal because of its impulsiveness. To accurately reconstruct the clean image and the blur kernel, the framelet domains are exploited to sparsely represent the image and the blur kernel. From the reformulations conducted, a joint estimation is devised to simultaneously perform the image recovery, the salt-and-pepper noise suppression and the blur kernel estimation under a optimization framework.

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Authors:
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong
Submitted On:
22 August 2017 - 10:21pm
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[1] Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong, "Image Deblurring in the presence of Salt-and-Pepper Noise", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1803. Accessed: Sep. 20, 2020.
@article{1803-17,
url = {http://sigport.org/1803},
author = {Liming Hou; Hongqing Liu; Zhen Luo; Yi Zhou and Trieu-Kien Truong },
publisher = {IEEE SigPort},
title = {Image Deblurring in the presence of Salt-and-Pepper Noise},
year = {2017} }
TY - EJOUR
T1 - Image Deblurring in the presence of Salt-and-Pepper Noise
AU - Liming Hou; Hongqing Liu; Zhen Luo; Yi Zhou and Trieu-Kien Truong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1803
ER -
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong. (2017). Image Deblurring in the presence of Salt-and-Pepper Noise. IEEE SigPort. http://sigport.org/1803
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong, 2017. Image Deblurring in the presence of Salt-and-Pepper Noise. Available at: http://sigport.org/1803.
Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong. (2017). "Image Deblurring in the presence of Salt-and-Pepper Noise." Web.
1. Liming Hou, Hongqing Liu, Zhen Luo, Yi Zhou and Trieu-Kien Truong. Image Deblurring in the presence of Salt-and-Pepper Noise [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1803

How should we evaluate supervised hashing?

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Authors:
Matthijs Douze, Nicolas Usunier, Hervé Jégou
Submitted On:
2 March 2017 - 3:08pm
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[1] Matthijs Douze, Nicolas Usunier, Hervé Jégou, "How should we evaluate supervised hashing?", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1595. Accessed: Sep. 20, 2020.
@article{1595-17,
url = {http://sigport.org/1595},
author = {Matthijs Douze; Nicolas Usunier; Hervé Jégou },
publisher = {IEEE SigPort},
title = {How should we evaluate supervised hashing?},
year = {2017} }
TY - EJOUR
T1 - How should we evaluate supervised hashing?
AU - Matthijs Douze; Nicolas Usunier; Hervé Jégou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1595
ER -
Matthijs Douze, Nicolas Usunier, Hervé Jégou. (2017). How should we evaluate supervised hashing?. IEEE SigPort. http://sigport.org/1595
Matthijs Douze, Nicolas Usunier, Hervé Jégou, 2017. How should we evaluate supervised hashing?. Available at: http://sigport.org/1595.
Matthijs Douze, Nicolas Usunier, Hervé Jégou. (2017). "How should we evaluate supervised hashing?." Web.
1. Matthijs Douze, Nicolas Usunier, Hervé Jégou. How should we evaluate supervised hashing? [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1595

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

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Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
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Summarization of Human Activity Videos Via Low-Rank Approximation

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1548. Accessed: Sep. 20, 2020.
@article{1548-17,
url = {http://sigport.org/1548},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1548
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1548
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1548.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1548

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

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Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
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Summarization of Human Activity Videos Via Low-Rank Approximation

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1547. Accessed: Sep. 20, 2020.
@article{1547-17,
url = {http://sigport.org/1547},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1547
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1547
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1547.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1547

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
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Type:
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Summarization of Human Activity Videos Via Low-Rank Approximation

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1546. Accessed: Sep. 20, 2020.
@article{1546-17,
url = {http://sigport.org/1546},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1546
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1546
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1546.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1546

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
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Summarization of Human Activity Videos Via Low-Rank Approximation

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1545. Accessed: Sep. 20, 2020.
@article{1545-17,
url = {http://sigport.org/1545},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1545
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1545
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1545.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1545

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(868)

Keywords

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1544. Accessed: Sep. 20, 2020.
@article{1544-17,
url = {http://sigport.org/1544},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1544
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1544
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1544.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1544

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