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ICIP 2018

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.

Image Fusion and Reconstruction of Compressed Data: A Joint Approach


In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter.
In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources.

Paper Details

Authors:
Laurent Condat, Florian Cotte, Mauro Dalla Mura
Submitted On:
8 October 2018 - 7:37pm
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Presentation_ICIP2018_v3.pdf

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[1] Laurent Condat, Florian Cotte, Mauro Dalla Mura, "Image Fusion and Reconstruction of Compressed Data: A Joint Approach", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3651. Accessed: Oct. 19, 2018.
@article{3651-18,
url = {http://sigport.org/3651},
author = {Laurent Condat; Florian Cotte; Mauro Dalla Mura },
publisher = {IEEE SigPort},
title = {Image Fusion and Reconstruction of Compressed Data: A Joint Approach},
year = {2018} }
TY - EJOUR
T1 - Image Fusion and Reconstruction of Compressed Data: A Joint Approach
AU - Laurent Condat; Florian Cotte; Mauro Dalla Mura
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3651
ER -
Laurent Condat, Florian Cotte, Mauro Dalla Mura. (2018). Image Fusion and Reconstruction of Compressed Data: A Joint Approach. IEEE SigPort. http://sigport.org/3651
Laurent Condat, Florian Cotte, Mauro Dalla Mura, 2018. Image Fusion and Reconstruction of Compressed Data: A Joint Approach. Available at: http://sigport.org/3651.
Laurent Condat, Florian Cotte, Mauro Dalla Mura. (2018). "Image Fusion and Reconstruction of Compressed Data: A Joint Approach." Web.
1. Laurent Condat, Florian Cotte, Mauro Dalla Mura. Image Fusion and Reconstruction of Compressed Data: A Joint Approach [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3651

Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning


• To automatically segment optic disk (OD) and cup regions in fundus images to derive clinical parameters, such as, cup-to-disk diameter ratio (CDR), to assist glaucoma diagnosis. An eye fundus camera is non-invasive and low-cost,
enabling the screening of a large number of patients quickly.

• Discuss various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions during network optimization.

• Evaluate proposed approaches on the Drishti-GS dataset.

Paper Details

Authors:
Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale
Submitted On:
8 October 2018 - 7:18pm
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ICIP_2018_Poster.pdf

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[1] Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale, "Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3650. Accessed: Oct. 19, 2018.
@article{3650-18,
url = {http://sigport.org/3650},
author = {Venkata Gopal Edupuganti; Akshay Chawla and Amit Kale },
publisher = {IEEE SigPort},
title = {Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning},
year = {2018} }
TY - EJOUR
T1 - Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning
AU - Venkata Gopal Edupuganti; Akshay Chawla and Amit Kale
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3650
ER -
Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale. (2018). Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning. IEEE SigPort. http://sigport.org/3650
Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale, 2018. Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning. Available at: http://sigport.org/3650.
Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale. (2018). "Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning." Web.
1. Venkata Gopal Edupuganti, Akshay Chawla and Amit Kale. Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3650

Supervised Deep Sparse Coding Networks

Paper Details

Authors:
Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran
Submitted On:
8 October 2018 - 6:31pm
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TA.L1.4-SupervisedDeepSparseCodingNetworks.pdf

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[1] Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran, "Supervised Deep Sparse Coding Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3649. Accessed: Oct. 19, 2018.
@article{3649-18,
url = {http://sigport.org/3649},
author = {Xiaoxia Sun; Nasser M. Nasrabadi; Trac D. Tran },
publisher = {IEEE SigPort},
title = {Supervised Deep Sparse Coding Networks},
year = {2018} }
TY - EJOUR
T1 - Supervised Deep Sparse Coding Networks
AU - Xiaoxia Sun; Nasser M. Nasrabadi; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3649
ER -
Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran. (2018). Supervised Deep Sparse Coding Networks. IEEE SigPort. http://sigport.org/3649
Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran, 2018. Supervised Deep Sparse Coding Networks. Available at: http://sigport.org/3649.
Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran. (2018). "Supervised Deep Sparse Coding Networks." Web.
1. Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran. Supervised Deep Sparse Coding Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3649

LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER


Image noise filters usually assume noise as white Gaussian. However, in a capturing pipeline, noise often becomes spatially correlated due to in-camera processing that aims to suppress the noise and increase the compression rate. Mostly, only high-frequency noise components are suppressed since the image signal is more likely to appear in the low-frequency components of the captured image. As a result, noise emerges as coarse grain which makes white (all-pass) noise filters ineffective, especially when the resolution of the target display is lower than the captured image.

Paper Details

Authors:
Meisam Rakhshanfar and Maria A. Amer
Submitted On:
8 October 2018 - 6:24pm
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icip18Poster.pdf

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[1] Meisam Rakhshanfar and Maria A. Amer, "LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3648. Accessed: Oct. 19, 2018.
@article{3648-18,
url = {http://sigport.org/3648},
author = {Meisam Rakhshanfar and Maria A. Amer },
publisher = {IEEE SigPort},
title = {LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER},
year = {2018} }
TY - EJOUR
T1 - LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER
AU - Meisam Rakhshanfar and Maria A. Amer
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3648
ER -
Meisam Rakhshanfar and Maria A. Amer. (2018). LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER. IEEE SigPort. http://sigport.org/3648
Meisam Rakhshanfar and Maria A. Amer, 2018. LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER. Available at: http://sigport.org/3648.
Meisam Rakhshanfar and Maria A. Amer. (2018). "LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER." Web.
1. Meisam Rakhshanfar and Maria A. Amer. LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3648

Deep 3D Human Pose Estimation under Partial Body Presence


This paper addresses the problem of 3D human pose estimation when not all body parts are present in the input image, i.e., when some body joints are present while other joints are fully absent (we exclude self-occlusion). State-of-the-art is not designed and thus not effective for such cases. We propose a deep CNN to regress the human pose directly from an input image; we design and train this network to work under partial body presence. Parallel to this, we train a detection network to classify the presence or absence of each of the main body joints in the input image.

Paper Details

Authors:
Saeid Vosoughi and Maria A. Amer
Submitted On:
8 October 2018 - 6:20pm
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3D_humanPose_demo.mp4_.avi_.zip

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[1] Saeid Vosoughi and Maria A. Amer, "Deep 3D Human Pose Estimation under Partial Body Presence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3647. Accessed: Oct. 19, 2018.
@article{3647-18,
url = {http://sigport.org/3647},
author = {Saeid Vosoughi and Maria A. Amer },
publisher = {IEEE SigPort},
title = {Deep 3D Human Pose Estimation under Partial Body Presence},
year = {2018} }
TY - EJOUR
T1 - Deep 3D Human Pose Estimation under Partial Body Presence
AU - Saeid Vosoughi and Maria A. Amer
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3647
ER -
Saeid Vosoughi and Maria A. Amer. (2018). Deep 3D Human Pose Estimation under Partial Body Presence. IEEE SigPort. http://sigport.org/3647
Saeid Vosoughi and Maria A. Amer, 2018. Deep 3D Human Pose Estimation under Partial Body Presence. Available at: http://sigport.org/3647.
Saeid Vosoughi and Maria A. Amer. (2018). "Deep 3D Human Pose Estimation under Partial Body Presence." Web.
1. Saeid Vosoughi and Maria A. Amer. Deep 3D Human Pose Estimation under Partial Body Presence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3647

Deep 3D Human Pose Estimation under Partial Body Presence


This paper addresses the problem of 3D human pose estimation when not all body parts are present in the input image, i.e., when some body joints are present while other joints are fully absent (we exclude self-occlusion). State-of-the-art is not designed and thus not effective for such cases. We propose a deep CNN to regress the human pose directly from an input image; we design and train this network to work under partial body presence. Parallel to this, we train a detection network to classify the presence or absence of each of the main body joints in the input image.

Paper Details

Authors:
Saeid Vosoughi and Maria A. Amer
Submitted On:
8 October 2018 - 6:09pm
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icip2018_3dpose_slides.pdf

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[1] Saeid Vosoughi and Maria A. Amer, "Deep 3D Human Pose Estimation under Partial Body Presence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3646. Accessed: Oct. 19, 2018.
@article{3646-18,
url = {http://sigport.org/3646},
author = {Saeid Vosoughi and Maria A. Amer },
publisher = {IEEE SigPort},
title = {Deep 3D Human Pose Estimation under Partial Body Presence},
year = {2018} }
TY - EJOUR
T1 - Deep 3D Human Pose Estimation under Partial Body Presence
AU - Saeid Vosoughi and Maria A. Amer
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3646
ER -
Saeid Vosoughi and Maria A. Amer. (2018). Deep 3D Human Pose Estimation under Partial Body Presence. IEEE SigPort. http://sigport.org/3646
Saeid Vosoughi and Maria A. Amer, 2018. Deep 3D Human Pose Estimation under Partial Body Presence. Available at: http://sigport.org/3646.
Saeid Vosoughi and Maria A. Amer. (2018). "Deep 3D Human Pose Estimation under Partial Body Presence." Web.
1. Saeid Vosoughi and Maria A. Amer. Deep 3D Human Pose Estimation under Partial Body Presence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3646

ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES


Object tracking is an active research area and numerous
techniques have been proposed recently. To evaluate a new
tracker, its performance is compared against existing ones
typically by averaging its quality based on a performance
measure, over all test video sequences. Such averaging is,
however, not representative as it does not account for outliers
(or similarities) between trackers. This paper presents a
framework for scoring and ranking of trackers using uncorrelated
quality metrics (overlap ratio and failure rate), coupled

Paper Details

Authors:
Tarek Ghoniemy, Julien Valognes, and Maria A. Amer
Submitted On:
8 October 2018 - 6:00pm
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icip18_RankingPaper_Slides.pdf

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[1] Tarek Ghoniemy, Julien Valognes, and Maria A. Amer, "ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3645. Accessed: Oct. 19, 2018.
@article{3645-18,
url = {http://sigport.org/3645},
author = {Tarek Ghoniemy; Julien Valognes; and Maria A. Amer },
publisher = {IEEE SigPort},
title = {ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES},
year = {2018} }
TY - EJOUR
T1 - ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES
AU - Tarek Ghoniemy; Julien Valognes; and Maria A. Amer
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3645
ER -
Tarek Ghoniemy, Julien Valognes, and Maria A. Amer. (2018). ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES. IEEE SigPort. http://sigport.org/3645
Tarek Ghoniemy, Julien Valognes, and Maria A. Amer, 2018. ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES. Available at: http://sigport.org/3645.
Tarek Ghoniemy, Julien Valognes, and Maria A. Amer. (2018). "ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES." Web.
1. Tarek Ghoniemy, Julien Valognes, and Maria A. Amer. ROBUST SCORING AND RANKING OF OBJECT TRACKING TECHNIQUES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3645

Background Light Estimation For Depth-dependent Underwater Image Restoration


Light undergoes a wavelength-dependent attenuation and loses energy along its propagation path in water. In particular, the absorption of red wavelengths is greater than that of green and blue wavelengths in open ocean waters. This reduces the red intensity of the scene radiance reaching the camera and results in non-uniform light, known as background light, due to the scene depth. Restoration methods that compensate for this colour loss often assume constant background light and distort the colour of the water region(s).

Paper Details

Authors:
Chau Yi Li, Andrea Cavallaro
Submitted On:
8 October 2018 - 5:49pm
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ICIP_BackgroundLight_Li_Cavallaro.pdf

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[1] Chau Yi Li, Andrea Cavallaro, "Background Light Estimation For Depth-dependent Underwater Image Restoration", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3644. Accessed: Oct. 19, 2018.
@article{3644-18,
url = {http://sigport.org/3644},
author = {Chau Yi Li; Andrea Cavallaro },
publisher = {IEEE SigPort},
title = {Background Light Estimation For Depth-dependent Underwater Image Restoration},
year = {2018} }
TY - EJOUR
T1 - Background Light Estimation For Depth-dependent Underwater Image Restoration
AU - Chau Yi Li; Andrea Cavallaro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3644
ER -
Chau Yi Li, Andrea Cavallaro. (2018). Background Light Estimation For Depth-dependent Underwater Image Restoration. IEEE SigPort. http://sigport.org/3644
Chau Yi Li, Andrea Cavallaro, 2018. Background Light Estimation For Depth-dependent Underwater Image Restoration. Available at: http://sigport.org/3644.
Chau Yi Li, Andrea Cavallaro. (2018). "Background Light Estimation For Depth-dependent Underwater Image Restoration." Web.
1. Chau Yi Li, Andrea Cavallaro. Background Light Estimation For Depth-dependent Underwater Image Restoration [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3644

presentation for icip 2018

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Submitted On:
8 October 2018 - 4:53pm
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Presentation for ICIP 2018.pdf

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[1] , "presentation for icip 2018", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3643. Accessed: Oct. 19, 2018.
@article{3643-18,
url = {http://sigport.org/3643},
author = { },
publisher = {IEEE SigPort},
title = {presentation for icip 2018},
year = {2018} }
TY - EJOUR
T1 - presentation for icip 2018
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3643
ER -
. (2018). presentation for icip 2018. IEEE SigPort. http://sigport.org/3643
, 2018. presentation for icip 2018. Available at: http://sigport.org/3643.
. (2018). "presentation for icip 2018." Web.
1. . presentation for icip 2018 [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3643

A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS

Paper Details

Authors:
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini
Submitted On:
9 October 2018 - 4:13pm
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ICIP_2018.pdf

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[1] Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini, "A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3642. Accessed: Oct. 19, 2018.
@article{3642-18,
url = {http://sigport.org/3642},
author = {Melpomeni Dimopoulou; Effrosyni Doutsi; Marc Antonini },
publisher = {IEEE SigPort},
title = {A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS},
year = {2018} }
TY - EJOUR
T1 - A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS
AU - Melpomeni Dimopoulou; Effrosyni Doutsi; Marc Antonini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3642
ER -
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. (2018). A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS. IEEE SigPort. http://sigport.org/3642
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini, 2018. A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS. Available at: http://sigport.org/3642.
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. (2018). "A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS." Web.
1. Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3642

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