Sorry, you need to enable JavaScript to visit this website.

Image, Video, and Multidimensional Signal Processing

SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO


Remote detection of the cardiac pulse has a number of applications in sports and medicine, and can be used to determine an individual’s physiological state. Previous approaches to estimate Heart Rate (HR) from video require the subject to remain stationary and employ background information to eliminate illumination interferences. The present research proposes a spectral reflectance-based novel illumination rectification method to eliminate illumination variations in the video.

Paper Details

Authors:
Arvind Subramaniam, Rajitha K
Submitted On:
18 September 2019 - 2:48pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM.pdf

(7)

Subscribe

[1] Arvind Subramaniam, Rajitha K, "SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4693. Accessed: Oct. 18, 2019.
@article{4693-19,
url = {http://sigport.org/4693},
author = {Arvind Subramaniam; Rajitha K },
publisher = {IEEE SigPort},
title = {SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO},
year = {2019} }
TY - EJOUR
T1 - SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO
AU - Arvind Subramaniam; Rajitha K
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4693
ER -
Arvind Subramaniam, Rajitha K. (2019). SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO. IEEE SigPort. http://sigport.org/4693
Arvind Subramaniam, Rajitha K, 2019. SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO. Available at: http://sigport.org/4693.
Arvind Subramaniam, Rajitha K. (2019). "SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO." Web.
1. Arvind Subramaniam, Rajitha K. SPECTRAL REFLECTANCE BASED HEART RATE MEASUREMENT FROM FACIAL VIDEO [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4693

SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS


Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve state-of-the-art performance in many challenging video datasets. Because of the high level of complexity of these methods, as the convolution operations are also extended to an additional dimension in order to extract features from it as well, providing a visualization for the signals that the network interpret as informative, is a challenging task.

Paper Details

Authors:
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe
Submitted On:
18 September 2019 - 9:52am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Saliency_Tubes_Visual_Explanations_for_Spatio-Temporal_Convolutions.pdf

(9)

Subscribe

[1] Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe, "SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4677. Accessed: Oct. 18, 2019.
@article{4677-19,
url = {http://sigport.org/4677},
author = {Georgios Kapidis; Grigorios Kalliatakis; Christos Chrysoulas; Remco Veltkamp and Ronald Poppe },
publisher = {IEEE SigPort},
title = {SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS},
year = {2019} }
TY - EJOUR
T1 - SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS
AU - Georgios Kapidis; Grigorios Kalliatakis; Christos Chrysoulas; Remco Veltkamp and Ronald Poppe
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4677
ER -
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe. (2019). SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS. IEEE SigPort. http://sigport.org/4677
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe, 2019. SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS. Available at: http://sigport.org/4677.
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe. (2019). "SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS." Web.
1. Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe. SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4677

End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric

Paper Details

Authors:
Chenyang Zhao, Runwei Ding, Hong Liu
Submitted On:
18 September 2019 - 7:26am
Short Link:
Type:
Event:
Paper Code:

Document Files

ICIP2019_poster_zhaocy.pdf

(6)

Subscribe

[1] Chenyang Zhao, Runwei Ding, Hong Liu, "End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4672. Accessed: Oct. 18, 2019.
@article{4672-19,
url = {http://sigport.org/4672},
author = {Chenyang Zhao; Runwei Ding; Hong Liu },
publisher = {IEEE SigPort},
title = {End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric},
year = {2019} }
TY - EJOUR
T1 - End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric
AU - Chenyang Zhao; Runwei Ding; Hong Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4672
ER -
Chenyang Zhao, Runwei Ding, Hong Liu. (2019). End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric. IEEE SigPort. http://sigport.org/4672
Chenyang Zhao, Runwei Ding, Hong Liu, 2019. End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric. Available at: http://sigport.org/4672.
Chenyang Zhao, Runwei Ding, Hong Liu. (2019). "End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric." Web.
1. Chenyang Zhao, Runwei Ding, Hong Liu. End-To-End Visual Place Recognition Based on Deep Metric Learning and Self-Adaptively Enhanced Similarity Metric [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4672

Two-Stream Multi-Task Network for Fashion Recognition

Paper Details

Authors:
Peizhao Li,Yanjing Li,Xiaolong Jiang,Xiantong Zhen
Submitted On:
17 September 2019 - 10:59am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

WA.L1.2.pdf

(5)

Subscribe

[1] Peizhao Li,Yanjing Li,Xiaolong Jiang,Xiantong Zhen, "Two-Stream Multi-Task Network for Fashion Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4660. Accessed: Oct. 18, 2019.
@article{4660-19,
url = {http://sigport.org/4660},
author = {Peizhao Li;Yanjing Li;Xiaolong Jiang;Xiantong Zhen },
publisher = {IEEE SigPort},
title = {Two-Stream Multi-Task Network for Fashion Recognition},
year = {2019} }
TY - EJOUR
T1 - Two-Stream Multi-Task Network for Fashion Recognition
AU - Peizhao Li;Yanjing Li;Xiaolong Jiang;Xiantong Zhen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4660
ER -
Peizhao Li,Yanjing Li,Xiaolong Jiang,Xiantong Zhen. (2019). Two-Stream Multi-Task Network for Fashion Recognition. IEEE SigPort. http://sigport.org/4660
Peizhao Li,Yanjing Li,Xiaolong Jiang,Xiantong Zhen, 2019. Two-Stream Multi-Task Network for Fashion Recognition. Available at: http://sigport.org/4660.
Peizhao Li,Yanjing Li,Xiaolong Jiang,Xiantong Zhen. (2019). "Two-Stream Multi-Task Network for Fashion Recognition." Web.
1. Peizhao Li,Yanjing Li,Xiaolong Jiang,Xiantong Zhen. Two-Stream Multi-Task Network for Fashion Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4660

RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION

Paper Details

Authors:
Submitted On:
17 September 2019 - 10:47am
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

yugangw_ICIP2019.pdf

(6)

Subscribe

[1] , "RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4659. Accessed: Oct. 18, 2019.
@article{4659-19,
url = {http://sigport.org/4659},
author = { },
publisher = {IEEE SigPort},
title = {RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION},
year = {2019} }
TY - EJOUR
T1 - RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4659
ER -
. (2019). RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION. IEEE SigPort. http://sigport.org/4659
, 2019. RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION. Available at: http://sigport.org/4659.
. (2019). "RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION." Web.
1. . RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4659

Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION


Gait recognition is a leading remote-based identification method, suitable for applications in forensic cases, surveillance, and medical studies. We present Glidar3DJ, a model-based gait recognition methodology, using a skeleton model extracted from sequences generated by a single flash lidar camera. Compared with Kinect, a flash lidar camera has a drastically extended range (> 1000 meters) and its performance is not affected in outdoor.

Paper Details

Authors:
Nasrin Sadeghzadehyazdi, Tamal Batabyal†, A. Glandon, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton
Submitted On:
16 September 2019 - 4:16pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icip2019_Glidar3DJ sa.pdf

(11)

Subscribe

[1] Nasrin Sadeghzadehyazdi, Tamal Batabyal†, A. Glandon, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton, "Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION ", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4645. Accessed: Oct. 18, 2019.
@article{4645-19,
url = {http://sigport.org/4645},
author = {Nasrin Sadeghzadehyazdi; Tamal Batabyal†; A. Glandon; Nibir K. Dhar; B. O. Familoni; K. M. Iftekharuddin; Scott T. Acton },
publisher = {IEEE SigPort},
title = {Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION },
year = {2019} }
TY - EJOUR
T1 - Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION
AU - Nasrin Sadeghzadehyazdi; Tamal Batabyal†; A. Glandon; Nibir K. Dhar; B. O. Familoni; K. M. Iftekharuddin; Scott T. Acton
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4645
ER -
Nasrin Sadeghzadehyazdi, Tamal Batabyal†, A. Glandon, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton. (2019). Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION . IEEE SigPort. http://sigport.org/4645
Nasrin Sadeghzadehyazdi, Tamal Batabyal†, A. Glandon, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton, 2019. Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION . Available at: http://sigport.org/4645.
Nasrin Sadeghzadehyazdi, Tamal Batabyal†, A. Glandon, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton. (2019). "Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION ." Web.
1. Nasrin Sadeghzadehyazdi, Tamal Batabyal†, A. Glandon, Nibir K. Dhar, B. O. Familoni, K. M. Iftekharuddin, Scott T. Acton. Glidar3DJ: A VIEW-INVARIANT GAIT IDENTIFICATION VIA FLASH LIDAR DATA CORRECTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4645

LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS


Bacterial segmentation poses significant challenges due to
lack of structure, poor imaging resolution, limited contrast
between touching cells and high density of cells that overlap.
Although there exist bacterial segmentation algorithms in the

existing art, they fail to delineate cells in dense biofilms,
especially in 3D imaging scenarios in which the cells are growing

and subdividing in an unstructured manner. A graph-based

Paper Details

Authors:
Submitted On:
16 September 2019 - 2:59pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

eposter_icip2019——lcuts sa.pdf

(7)

Subscribe

[1] , "LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4644. Accessed: Oct. 18, 2019.
@article{4644-19,
url = {http://sigport.org/4644},
author = { },
publisher = {IEEE SigPort},
title = {LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS},
year = {2019} }
TY - EJOUR
T1 - LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4644
ER -
. (2019). LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS. IEEE SigPort. http://sigport.org/4644
, 2019. LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS. Available at: http://sigport.org/4644.
. (2019). "LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS." Web.
1. . LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4644

MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION


The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

Paper Details

Authors:
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala
Submitted On:
16 September 2019 - 12:30am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Underwtaer_MOS_Poaster.pdf

(11)

Subscribe

[1] Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala, "MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4628. Accessed: Oct. 18, 2019.
@article{4628-19,
url = {http://sigport.org/4628},
author = {Prashant W. Patil; Omkar Thawakar; Akshay Dudhane; Subrahmanyam Murala },
publisher = {IEEE SigPort},
title = {MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION},
year = {2019} }
TY - EJOUR
T1 - MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION
AU - Prashant W. Patil; Omkar Thawakar; Akshay Dudhane; Subrahmanyam Murala
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4628
ER -
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. (2019). MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION. IEEE SigPort. http://sigport.org/4628
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala, 2019. MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION. Available at: http://sigport.org/4628.
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. (2019). "MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION." Web.
1. Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4628

MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION


The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

Paper Details

Authors:
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala
Submitted On:
16 September 2019 - 12:27am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster.pdf

(156)

Subscribe

[1] Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala, "MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4627. Accessed: Oct. 18, 2019.
@article{4627-19,
url = {http://sigport.org/4627},
author = {Prashant W. Patil; Omkar Thawakar; Akshay Dudhane; Subrahmanyam Murala },
publisher = {IEEE SigPort},
title = {MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION},
year = {2019} }
TY - EJOUR
T1 - MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION
AU - Prashant W. Patil; Omkar Thawakar; Akshay Dudhane; Subrahmanyam Murala
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4627
ER -
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. (2019). MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION. IEEE SigPort. http://sigport.org/4627
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala, 2019. MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION. Available at: http://sigport.org/4627.
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. (2019). "MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION." Web.
1. Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4627

MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION


The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

Paper Details

Authors:
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala
Submitted On:
16 September 2019 - 12:21am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster.pdf

(276)

Subscribe

[1] Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala, "MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4622. Accessed: Oct. 18, 2019.
@article{4622-19,
url = {http://sigport.org/4622},
author = {Prashant W. Patil; Omkar Thawakar; Akshay Dudhane; Subrahmanyam Murala },
publisher = {IEEE SigPort},
title = {MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION},
year = {2019} }
TY - EJOUR
T1 - MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION
AU - Prashant W. Patil; Omkar Thawakar; Akshay Dudhane; Subrahmanyam Murala
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4622
ER -
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. (2019). MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION. IEEE SigPort. http://sigport.org/4622
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala, 2019. MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION. Available at: http://sigport.org/4622.
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. (2019). "MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION." Web.
1. Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala. MOTION SALIENCY BASED GENERATIVE ADVERSARIAL NETWORK FOR UNDERWATER MOVING OBJECT SEGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4622

Pages