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ICIP 2020

ICIP 2020 is a fully virtual conference. The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website

Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia


Steganography is the art and science of hiding data within innocent-looking objects (cover objects). Multimedia objects such as images and videos are an attractive type of cover objects due to their high embedding rates. There exist many techniques for performing steganography in both the literature and the practical world. Meanwhile, the definition of the steganographic capacity for multimedia and how to be calculated has not taken full attention.

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Authors:
Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly
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6 November 2020 - 1:27pm
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[1] Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly, "Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5547. Accessed: Nov. 29, 2020.
@article{5547-20,
url = {http://sigport.org/5547},
author = {Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly },
publisher = {IEEE SigPort},
title = {Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia},
year = {2020} }
TY - EJOUR
T1 - Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia
AU - Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5547
ER -
Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly. (2020). Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia. IEEE SigPort. http://sigport.org/5547
Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly, 2020. Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia. Available at: http://sigport.org/5547.
Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly. (2020). "Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia." Web.
1. Hassan Y. El-Arsh; Amr Abdelaziz; Ahmed Elliethy; Hussein A. Aly. Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5547

The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG


Over the past decade, convolutional neural networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. They can learn robust representations of image data by processing RGB pixels. Since image data are often stored in a compressed format, from which JPEG is the most widespread, a preliminary decoding process is demanded. Recently, the design of CNNs for processing JPEG compressed data has gained attention from the research community.

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Authors:
Samuel Felipe dos Santos , Nicu Sebe , and Jurandy Almeida
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6 November 2020 - 9:30am
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[1] Samuel Felipe dos Santos , Nicu Sebe , and Jurandy Almeida, "The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5546. Accessed: Nov. 29, 2020.
@article{5546-20,
url = {http://sigport.org/5546},
author = {Samuel Felipe dos Santos ; Nicu Sebe ; and Jurandy Almeida },
publisher = {IEEE SigPort},
title = {The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG},
year = {2020} }
TY - EJOUR
T1 - The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG
AU - Samuel Felipe dos Santos ; Nicu Sebe ; and Jurandy Almeida
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5546
ER -
Samuel Felipe dos Santos , Nicu Sebe , and Jurandy Almeida. (2020). The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG. IEEE SigPort. http://sigport.org/5546
Samuel Felipe dos Santos , Nicu Sebe , and Jurandy Almeida, 2020. The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG. Available at: http://sigport.org/5546.
Samuel Felipe dos Santos , Nicu Sebe , and Jurandy Almeida. (2020). "The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG." Web.
1. Samuel Felipe dos Santos , Nicu Sebe , and Jurandy Almeida. The Good, the Bad, and the Ugly: Neural Networks Straight from JPEG [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5546

Unsupervised learning from limited available data by β-NMF and dual autoencoder


Unsupervised Learning (UL) models are a class of Machine Learning (ML) which concerns with reducing dimensionality, data factorization, disentangling and learning the representations among the data. The UL models gain their popularity due to their abilities to learn without any predefined label, and they are able to reduce the noise and redundancy among the data samples.

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Authors:
Mohanad Abukmeil,Stefano Ferrari, Vincenzo Piuri, Fabio Scotti
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6 November 2020 - 4:26am
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Unsupervised learning from limited available data by β-NMF and dual autoencoder (final published).pdf

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[1] Mohanad Abukmeil,Stefano Ferrari, Vincenzo Piuri, Fabio Scotti, "Unsupervised learning from limited available data by β-NMF and dual autoencoder", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5545. Accessed: Nov. 29, 2020.
@article{5545-20,
url = {http://sigport.org/5545},
author = {Mohanad Abukmeil;Stefano Ferrari; Vincenzo Piuri; Fabio Scotti },
publisher = {IEEE SigPort},
title = {Unsupervised learning from limited available data by β-NMF and dual autoencoder},
year = {2020} }
TY - EJOUR
T1 - Unsupervised learning from limited available data by β-NMF and dual autoencoder
AU - Mohanad Abukmeil;Stefano Ferrari; Vincenzo Piuri; Fabio Scotti
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5545
ER -
Mohanad Abukmeil,Stefano Ferrari, Vincenzo Piuri, Fabio Scotti. (2020). Unsupervised learning from limited available data by β-NMF and dual autoencoder. IEEE SigPort. http://sigport.org/5545
Mohanad Abukmeil,Stefano Ferrari, Vincenzo Piuri, Fabio Scotti, 2020. Unsupervised learning from limited available data by β-NMF and dual autoencoder. Available at: http://sigport.org/5545.
Mohanad Abukmeil,Stefano Ferrari, Vincenzo Piuri, Fabio Scotti. (2020). "Unsupervised learning from limited available data by β-NMF and dual autoencoder." Web.
1. Mohanad Abukmeil,Stefano Ferrari, Vincenzo Piuri, Fabio Scotti. Unsupervised learning from limited available data by β-NMF and dual autoencoder [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5545

DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION

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Authors:
Alin Achim
Submitted On:
6 November 2020 - 2:37am
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[1] Alin Achim, "DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5544. Accessed: Nov. 29, 2020.
@article{5544-20,
url = {http://sigport.org/5544},
author = {Alin Achim },
publisher = {IEEE SigPort},
title = {DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION},
year = {2020} }
TY - EJOUR
T1 - DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION
AU - Alin Achim
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5544
ER -
Alin Achim. (2020). DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION. IEEE SigPort. http://sigport.org/5544
Alin Achim, 2020. DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION. Available at: http://sigport.org/5544.
Alin Achim. (2020). "DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION." Web.
1. Alin Achim. DETECTION OF SHIP WAKES IN SAR IMAGERY USING CAUCHY REGULARISATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5544

FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network

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Authors:
Jie Yang
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5 November 2020 - 9:00am
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FDFlowNet.pdf

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[1] Jie Yang, "FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5543. Accessed: Nov. 29, 2020.
@article{5543-20,
url = {http://sigport.org/5543},
author = {Jie Yang },
publisher = {IEEE SigPort},
title = {FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network},
year = {2020} }
TY - EJOUR
T1 - FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
AU - Jie Yang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5543
ER -
Jie Yang. (2020). FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network. IEEE SigPort. http://sigport.org/5543
Jie Yang, 2020. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network. Available at: http://sigport.org/5543.
Jie Yang. (2020). "FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network." Web.
1. Jie Yang. FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5543

CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING

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5 November 2020 - 8:45am
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[1] , "CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5542. Accessed: Nov. 29, 2020.
@article{5542-20,
url = {http://sigport.org/5542},
author = { },
publisher = {IEEE SigPort},
title = {CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING},
year = {2020} }
TY - EJOUR
T1 - CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5542
ER -
. (2020). CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING. IEEE SigPort. http://sigport.org/5542
, 2020. CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING. Available at: http://sigport.org/5542.
. (2020). "CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING." Web.
1. . CHANNEL SHUFFLE RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5542

LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK

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5 November 2020 - 8:41am
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Li.pdf

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[1] , "LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5541. Accessed: Nov. 29, 2020.
@article{5541-20,
url = {http://sigport.org/5541},
author = { },
publisher = {IEEE SigPort},
title = {LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK},
year = {2020} }
TY - EJOUR
T1 - LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5541
ER -
. (2020). LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK. IEEE SigPort. http://sigport.org/5541
, 2020. LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK. Available at: http://sigport.org/5541.
. (2020). "LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK." Web.
1. . LIGHTWEIGHT IMAGE SUPER-RESOLUTION RECONSTRUCTION WITH HIERARCHICAL FEATURE-DRIVEN NETWORK [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5541

Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution

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5 November 2020 - 4:54am
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[1] , "Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5540. Accessed: Nov. 29, 2020.
@article{5540-20,
url = {http://sigport.org/5540},
author = { },
publisher = {IEEE SigPort},
title = {Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution},
year = {2020} }
TY - EJOUR
T1 - Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5540
ER -
. (2020). Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution. IEEE SigPort. http://sigport.org/5540
, 2020. Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution. Available at: http://sigport.org/5540.
. (2020). "Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution." Web.
1. . Pose Guided Person Image Generation Based on Pose Skeleton Sequence and 3D Convolution [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5540

Video Embed: This video provider is not currently supported.

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Submitted On:
5 November 2020 - 4:17am
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Scale-invariant siamese network for person re-identification(paper code 3023).pdf

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[1] , "Video Embed: This video provider is not currently supported.", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5539. Accessed: Nov. 29, 2020.
@article{5539-20,
url = {http://sigport.org/5539},
author = { },
publisher = {IEEE SigPort},
title = {Video Embed: This video provider is not currently supported.},
year = {2020} }
TY - EJOUR
T1 - Video Embed: This video provider is not currently supported.
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5539
ER -
. (2020). Video Embed: This video provider is not currently supported.. IEEE SigPort. http://sigport.org/5539
, 2020. Video Embed: This video provider is not currently supported.. Available at: http://sigport.org/5539.
. (2020). "Video Embed: This video provider is not currently supported.." Web.
1. . Video Embed: This video provider is not currently supported. [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5539

Sparsity preserved canonical correlation analysis


Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. Recently, under the assumption that the leading canonical correlation directions are sparse, various procedures have been proposed for many high-dimensional applications to improve the interpretability of CCA. However all these procedures have the inconvenience of not preserving the sparsity among the retained leading canonical directions. To address this issue, a new sparse CCA method is proposed in this paper.

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Authors:
Abd-Krim Seghouane, Muhammad Ali Qadar
Submitted On:
5 November 2020 - 3:01am
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ICIP 2020 talk

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[1] Abd-Krim Seghouane, Muhammad Ali Qadar, "Sparsity preserved canonical correlation analysis", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5538. Accessed: Nov. 29, 2020.
@article{5538-20,
url = {http://sigport.org/5538},
author = {Abd-Krim Seghouane; Muhammad Ali Qadar },
publisher = {IEEE SigPort},
title = {Sparsity preserved canonical correlation analysis},
year = {2020} }
TY - EJOUR
T1 - Sparsity preserved canonical correlation analysis
AU - Abd-Krim Seghouane; Muhammad Ali Qadar
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5538
ER -
Abd-Krim Seghouane, Muhammad Ali Qadar. (2020). Sparsity preserved canonical correlation analysis. IEEE SigPort. http://sigport.org/5538
Abd-Krim Seghouane, Muhammad Ali Qadar, 2020. Sparsity preserved canonical correlation analysis. Available at: http://sigport.org/5538.
Abd-Krim Seghouane, Muhammad Ali Qadar. (2020). "Sparsity preserved canonical correlation analysis." Web.
1. Abd-Krim Seghouane, Muhammad Ali Qadar. Sparsity preserved canonical correlation analysis [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5538

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