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Image/Video Storage, Retrieval

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: Feb. 18, 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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(868)

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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: Feb. 18, 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:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(868)

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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: Feb. 18, 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

Additional Categories

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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: Feb. 18, 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

EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING


Edited film alignment is the post-production process of finding small parts of unedited footage that temporally and spatially match an edited film. The huge amount of data to be processed makes significant downsampling of the videos essential in real-life applications. Simultaneously, professional users demand that the task be achieved with frame and pixel-level accuracy. We propose a novel selective Hough transform (SHT) and an accurate template matching method to address the difficult trade-off between accuracy and scalability.

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Authors:
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino
Submitted On:
28 February 2017 - 11:28pm
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1232.pdf

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[1] Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino, "EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1528. Accessed: Feb. 18, 2020.
@article{1528-17,
url = {http://sigport.org/1528},
author = {Xiaomeng Wu; Takahito Kawanishi; Minoru Mori; Kaoru Hiramatsu; and Kunio Kashino },
publisher = {IEEE SigPort},
title = {EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING},
year = {2017} }
TY - EJOUR
T1 - EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING
AU - Xiaomeng Wu; Takahito Kawanishi; Minoru Mori; Kaoru Hiramatsu; and Kunio Kashino
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1528
ER -
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino. (2017). EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING. IEEE SigPort. http://sigport.org/1528
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino, 2017. EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING. Available at: http://sigport.org/1528.
Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino. (2017). "EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING." Web.
1. Xiaomeng Wu, Takahito Kawanishi, Minoru Mori, Kaoru Hiramatsu, and Kunio Kashino. EDITED FILM ALIGNMENT VIA SELECTIVE HOUGH TRANSFORM AND ACCURATE TEMPLATE MATCHING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1528

Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning

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28 February 2017 - 9:31pm
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Image Retrieval

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[1] , "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1520. Accessed: Feb. 18, 2020.
@article{1520-17,
url = {http://sigport.org/1520},
author = { },
publisher = {IEEE SigPort},
title = {Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning},
year = {2017} }
TY - EJOUR
T1 - Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1520
ER -
. (2017). Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. IEEE SigPort. http://sigport.org/1520
, 2017. Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. Available at: http://sigport.org/1520.
. (2017). "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning." Web.
1. . Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1520

Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning

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28 February 2017 - 9:17pm
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Image Retrieval

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[1] , "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1519. Accessed: Feb. 18, 2020.
@article{1519-17,
url = {http://sigport.org/1519},
author = { },
publisher = {IEEE SigPort},
title = {Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning},
year = {2017} }
TY - EJOUR
T1 - Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1519
ER -
. (2017). Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. IEEE SigPort. http://sigport.org/1519
, 2017. Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning. Available at: http://sigport.org/1519.
. (2017). "Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning." Web.
1. . Image Retrieval Based on Deep Convolutional Neural Networks and Binary Hashing Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1519

Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search

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Authors:
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu
Submitted On:
20 March 2016 - 6:59am
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EVC_ICASSP2016_v1a_printout.pdf

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[1] Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu, "Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/868. Accessed: Feb. 18, 2020.
@article{868-16,
url = {http://sigport.org/868},
author = {Haiyan Shu; Wenyu Jiang; Xiaoming Bao; Huan Zhou; Rongshan Yu },
publisher = {IEEE SigPort},
title = {Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search},
year = {2016} }
TY - EJOUR
T1 - Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search
AU - Haiyan Shu; Wenyu Jiang; Xiaoming Bao; Huan Zhou; Rongshan Yu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/868
ER -
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu. (2016). Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search. IEEE SigPort. http://sigport.org/868
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu, 2016. Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search. Available at: http://sigport.org/868.
Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu. (2016). "Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search." Web.
1. Haiyan Shu, Wenyu Jiang, Xiaoming Bao, Huan Zhou, Rongshan Yu. Enhanced Vote Count Circuit based on NOR Flash Memory for Fast Similarity Search [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/868

Adaptive algorithms for hypergraph learning

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Authors:
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos
Submitted On:
17 March 2016 - 7:18am
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ICASSP2016_Presenation1.pdf

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[1] Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos, "Adaptive algorithms for hypergraph learning ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/741. Accessed: Feb. 18, 2020.
@article{741-16,
url = {http://sigport.org/741},
author = {Aikaterini Chasapi; Constantine Kotropoulos; Konstantinos Pliakos },
publisher = {IEEE SigPort},
title = {Adaptive algorithms for hypergraph learning },
year = {2016} }
TY - EJOUR
T1 - Adaptive algorithms for hypergraph learning
AU - Aikaterini Chasapi; Constantine Kotropoulos; Konstantinos Pliakos
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/741
ER -
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos. (2016). Adaptive algorithms for hypergraph learning . IEEE SigPort. http://sigport.org/741
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos, 2016. Adaptive algorithms for hypergraph learning . Available at: http://sigport.org/741.
Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos. (2016). "Adaptive algorithms for hypergraph learning ." Web.
1. Aikaterini Chasapi, Constantine Kotropoulos, Konstantinos Pliakos. Adaptive algorithms for hypergraph learning [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/741

A Data Set Providing Synthetic and Real-World Fisheye Video Sequences


Synthetic fisheye image

In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The sequences are freely available via www.lms.lnt.de/fisheyedataset/.

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Authors:
Andrea Eichenseer, André Kaup
Submitted On:
16 March 2016 - 5:36am
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posterICASSP2016fisheyedataset.pdf

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[1] Andrea Eichenseer, André Kaup, "A Data Set Providing Synthetic and Real-World Fisheye Video Sequences", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/709. Accessed: Feb. 18, 2020.
@article{709-16,
url = {http://sigport.org/709},
author = {Andrea Eichenseer; André Kaup },
publisher = {IEEE SigPort},
title = {A Data Set Providing Synthetic and Real-World Fisheye Video Sequences},
year = {2016} }
TY - EJOUR
T1 - A Data Set Providing Synthetic and Real-World Fisheye Video Sequences
AU - Andrea Eichenseer; André Kaup
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/709
ER -
Andrea Eichenseer, André Kaup. (2016). A Data Set Providing Synthetic and Real-World Fisheye Video Sequences. IEEE SigPort. http://sigport.org/709
Andrea Eichenseer, André Kaup, 2016. A Data Set Providing Synthetic and Real-World Fisheye Video Sequences. Available at: http://sigport.org/709.
Andrea Eichenseer, André Kaup. (2016). "A Data Set Providing Synthetic and Real-World Fisheye Video Sequences." Web.
1. Andrea Eichenseer, André Kaup. A Data Set Providing Synthetic and Real-World Fisheye Video Sequences [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/709

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