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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.

RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION

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Authors:
Jing Zhao; Jiyu Xie; Ruiqin Xiong
Submitted On:
7 October 2018 - 10:53pm
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Poster_RESIDUAL_SIGNALS_MODELING_FOR_LAYERED_ SOFTCAST_WITH_HYBRID_DIGITAL-ANALOG_TRANSMISSION.pdf

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[1] Jing Zhao; Jiyu Xie; Ruiqin Xiong, " RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3605. Accessed: Dec. 10, 2018.
@article{3605-18,
url = {http://sigport.org/3605},
author = {Jing Zhao; Jiyu Xie; Ruiqin Xiong },
publisher = {IEEE SigPort},
title = { RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION},
year = {2018} }
TY - EJOUR
T1 - RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION
AU - Jing Zhao; Jiyu Xie; Ruiqin Xiong
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3605
ER -
Jing Zhao; Jiyu Xie; Ruiqin Xiong. (2018). RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION. IEEE SigPort. http://sigport.org/3605
Jing Zhao; Jiyu Xie; Ruiqin Xiong, 2018. RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION. Available at: http://sigport.org/3605.
Jing Zhao; Jiyu Xie; Ruiqin Xiong. (2018). " RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION." Web.
1. Jing Zhao; Jiyu Xie; Ruiqin Xiong. RESIDUAL SIGNALS MODELING FOR LAYERED IMAGE/VIDEO SOFTCAST WITH HYBRID DIGITAL-ANALOG TRANSMISSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3605

KNOT MAGNIFY LOSS FOR FACE RECOGNITION

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Authors:
Qiang Rao, Bing Yu, Yun Yang, Bailan Feng
Submitted On:
7 October 2018 - 9:36pm
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Knot Loss For Face Recognition poster.pdf

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[1] Qiang Rao, Bing Yu, Yun Yang, Bailan Feng, "KNOT MAGNIFY LOSS FOR FACE RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3604. Accessed: Dec. 10, 2018.
@article{3604-18,
url = {http://sigport.org/3604},
author = {Qiang Rao; Bing Yu; Yun Yang; Bailan Feng },
publisher = {IEEE SigPort},
title = {KNOT MAGNIFY LOSS FOR FACE RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - KNOT MAGNIFY LOSS FOR FACE RECOGNITION
AU - Qiang Rao; Bing Yu; Yun Yang; Bailan Feng
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3604
ER -
Qiang Rao, Bing Yu, Yun Yang, Bailan Feng. (2018). KNOT MAGNIFY LOSS FOR FACE RECOGNITION. IEEE SigPort. http://sigport.org/3604
Qiang Rao, Bing Yu, Yun Yang, Bailan Feng, 2018. KNOT MAGNIFY LOSS FOR FACE RECOGNITION. Available at: http://sigport.org/3604.
Qiang Rao, Bing Yu, Yun Yang, Bailan Feng. (2018). "KNOT MAGNIFY LOSS FOR FACE RECOGNITION." Web.
1. Qiang Rao, Bing Yu, Yun Yang, Bailan Feng. KNOT MAGNIFY LOSS FOR FACE RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3604

An Interior Point Method for Nonnegative Sparse Signal Reconstruction


We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.

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Authors:
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld
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7 October 2018 - 5:05pm
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2018_Huang_IPAlgorithm_ICIP_Poster.pdf

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[1] Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, "An Interior Point Method for Nonnegative Sparse Signal Reconstruction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3603. Accessed: Dec. 10, 2018.
@article{3603-18,
url = {http://sigport.org/3603},
author = {Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld },
publisher = {IEEE SigPort},
title = {An Interior Point Method for Nonnegative Sparse Signal Reconstruction},
year = {2018} }
TY - EJOUR
T1 - An Interior Point Method for Nonnegative Sparse Signal Reconstruction
AU - Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3603
ER -
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). An Interior Point Method for Nonnegative Sparse Signal Reconstruction. IEEE SigPort. http://sigport.org/3603
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, 2018. An Interior Point Method for Nonnegative Sparse Signal Reconstruction. Available at: http://sigport.org/3603.
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). "An Interior Point Method for Nonnegative Sparse Signal Reconstruction." Web.
1. Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. An Interior Point Method for Nonnegative Sparse Signal Reconstruction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3603

A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS

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Authors:
Ron Op het Veld, Tobias Jaschke, Michel Bätz, Joachim Keinert
Submitted On:
7 October 2018 - 4:32pm
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Presentation_ICIP_v2.pptx

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[1] Ron Op het Veld, Tobias Jaschke, Michel Bätz, Joachim Keinert, "A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3602. Accessed: Dec. 10, 2018.
@article{3602-18,
url = {http://sigport.org/3602},
author = {Ron Op het Veld; Tobias Jaschke; Michel Bätz; Joachim Keinert },
publisher = {IEEE SigPort},
title = {A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS
AU - Ron Op het Veld; Tobias Jaschke; Michel Bätz; Joachim Keinert
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3602
ER -
Ron Op het Veld, Tobias Jaschke, Michel Bätz, Joachim Keinert. (2018). A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS. IEEE SigPort. http://sigport.org/3602
Ron Op het Veld, Tobias Jaschke, Michel Bätz, Joachim Keinert, 2018. A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS. Available at: http://sigport.org/3602.
Ron Op het Veld, Tobias Jaschke, Michel Bätz, Joachim Keinert. (2018). "A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS." Web.
1. Ron Op het Veld, Tobias Jaschke, Michel Bätz, Joachim Keinert. A NOVEL CONFIDENCE MEASURE FOR DISPARITY MAPS BY PIXEL-WISE COST FUNCTION ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3602

UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO

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Authors:
Baolin Zhu,Jiangqun Ni
Submitted On:
7 October 2018 - 4:32pm
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ICIP海报.pdf

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[1] Baolin Zhu,Jiangqun Ni, "UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3601. Accessed: Dec. 10, 2018.
@article{3601-18,
url = {http://sigport.org/3601},
author = {Baolin Zhu;Jiangqun Ni },
publisher = {IEEE SigPort},
title = {UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO},
year = {2018} }
TY - EJOUR
T1 - UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO
AU - Baolin Zhu;Jiangqun Ni
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3601
ER -
Baolin Zhu,Jiangqun Ni. (2018). UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO. IEEE SigPort. http://sigport.org/3601
Baolin Zhu,Jiangqun Ni, 2018. UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO. Available at: http://sigport.org/3601.
Baolin Zhu,Jiangqun Ni. (2018). "UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO." Web.
1. Baolin Zhu,Jiangqun Ni. UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3601

RTSeg: Real-time Semantic Segmentation Comparative Study

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7 October 2018 - 3:57pm
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ICIP_Poster.pdf

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[1] , "RTSeg: Real-time Semantic Segmentation Comparative Study", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3600. Accessed: Dec. 10, 2018.
@article{3600-18,
url = {http://sigport.org/3600},
author = { },
publisher = {IEEE SigPort},
title = {RTSeg: Real-time Semantic Segmentation Comparative Study},
year = {2018} }
TY - EJOUR
T1 - RTSeg: Real-time Semantic Segmentation Comparative Study
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3600
ER -
. (2018). RTSeg: Real-time Semantic Segmentation Comparative Study. IEEE SigPort. http://sigport.org/3600
, 2018. RTSeg: Real-time Semantic Segmentation Comparative Study. Available at: http://sigport.org/3600.
. (2018). "RTSeg: Real-time Semantic Segmentation Comparative Study." Web.
1. . RTSeg: Real-time Semantic Segmentation Comparative Study [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3600

A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting


Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects’ sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting.

ICIP2018.pptx

File ICIP2018.pptx (13 downloads)

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Authors:
Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots
Submitted On:
10 October 2018 - 7:26am
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ICIP2018.pptx

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[1] Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots, "A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3599. Accessed: Dec. 10, 2018.
@article{3599-18,
url = {http://sigport.org/3599},
author = {Saeed Amirgholipour; Xiangjian He; Wenjing Jia; Dadong Wang; Michelle Zeibots },
publisher = {IEEE SigPort},
title = {A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting},
year = {2018} }
TY - EJOUR
T1 - A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting
AU - Saeed Amirgholipour; Xiangjian He; Wenjing Jia; Dadong Wang; Michelle Zeibots
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3599
ER -
Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots. (2018). A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting. IEEE SigPort. http://sigport.org/3599
Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots, 2018. A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting. Available at: http://sigport.org/3599.
Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots. (2018). "A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting." Web.
1. Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots. A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3599

SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU

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Submitted On:
8 October 2018 - 5:17am
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icip2018_oral_shirahata.pdf

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[1] , "SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3598. Accessed: Dec. 10, 2018.
@article{3598-18,
url = {http://sigport.org/3598},
author = { },
publisher = {IEEE SigPort},
title = {SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU},
year = {2018} }
TY - EJOUR
T1 - SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3598
ER -
. (2018). SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU. IEEE SigPort. http://sigport.org/3598
, 2018. SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU. Available at: http://sigport.org/3598.
. (2018). "SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU." Web.
1. . SPEED-UP OF OBJECT DETECTION NEURAL NETWORK WITH GPU [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3598

IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES


A novel extension to proposal-based detection is proposed in order to learn convolutional context features for determining boundaries of objects better. Objects and their context are aimed to be learned through parallel convolutional stages. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection benchmark dataset and yielded improvements in performance over baseline, for all classes, especially the ones with distinctive context.

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Authors:
Emre Can Kaya, A. Aydın Alatan
Submitted On:
7 October 2018 - 2:01pm
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PosterPresentations.com-36x56-Template-V6.pdf

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[1] Emre Can Kaya, A. Aydın Alatan, "IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3597. Accessed: Dec. 10, 2018.
@article{3597-18,
url = {http://sigport.org/3597},
author = {Emre Can Kaya; A. Aydın Alatan },
publisher = {IEEE SigPort},
title = {IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES},
year = {2018} }
TY - EJOUR
T1 - IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES
AU - Emre Can Kaya; A. Aydın Alatan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3597
ER -
Emre Can Kaya, A. Aydın Alatan. (2018). IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES. IEEE SigPort. http://sigport.org/3597
Emre Can Kaya, A. Aydın Alatan, 2018. IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES. Available at: http://sigport.org/3597.
Emre Can Kaya, A. Aydın Alatan. (2018). "IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES." Web.
1. Emre Can Kaya, A. Aydın Alatan. IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3597

INDOOR DENSE DEPTH MAP AT DRONE HOVERING

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Authors:
Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick
Submitted On:
7 October 2018 - 1:10pm
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Presentation Slides

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[1] Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick, "INDOOR DENSE DEPTH MAP AT DRONE HOVERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3596. Accessed: Dec. 10, 2018.
@article{3596-18,
url = {http://sigport.org/3596},
author = {Arindam Saha; Soumyadip Maity; Brojeshwar Bhowmick },
publisher = {IEEE SigPort},
title = {INDOOR DENSE DEPTH MAP AT DRONE HOVERING},
year = {2018} }
TY - EJOUR
T1 - INDOOR DENSE DEPTH MAP AT DRONE HOVERING
AU - Arindam Saha; Soumyadip Maity; Brojeshwar Bhowmick
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3596
ER -
Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick. (2018). INDOOR DENSE DEPTH MAP AT DRONE HOVERING. IEEE SigPort. http://sigport.org/3596
Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick, 2018. INDOOR DENSE DEPTH MAP AT DRONE HOVERING. Available at: http://sigport.org/3596.
Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick. (2018). "INDOOR DENSE DEPTH MAP AT DRONE HOVERING." Web.
1. Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick. INDOOR DENSE DEPTH MAP AT DRONE HOVERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3596

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