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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website

Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec


Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA).

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Authors:
David McGrath, Stefan Bruhn, Heiko Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy
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7 May 2019 - 3:10pm
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[1] David McGrath, Stefan Bruhn, Heiko Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy, "Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3950. Accessed: Jan. 19, 2020.
@article{3950-19,
url = {http://sigport.org/3950},
author = {David McGrath; Stefan Bruhn; Heiko Purnhagen; Michael Eckert; Juan Torres; Stefanie Brown; Dan Darcy },
publisher = {IEEE SigPort},
title = {Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec},
year = {2019} }
TY - EJOUR
T1 - Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec
AU - David McGrath; Stefan Bruhn; Heiko Purnhagen; Michael Eckert; Juan Torres; Stefanie Brown; Dan Darcy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3950
ER -
David McGrath, Stefan Bruhn, Heiko Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy. (2019). Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec. IEEE SigPort. http://sigport.org/3950
David McGrath, Stefan Bruhn, Heiko Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy, 2019. Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec. Available at: http://sigport.org/3950.
David McGrath, Stefan Bruhn, Heiko Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy. (2019). "Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec." Web.
1. David McGrath, Stefan Bruhn, Heiko Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy. Immersive Audio Coding for Virtual Reality Using a Metadata-Assisted Extension of the 3GPP EVS Codec [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3950

Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters


The paper introduces a novel family of deterministic signals, the orthogonal periodic sequences (OPSs), for the identification of functional link polynomial (FLiP) filters. The novel sequences share many of the characteristics of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of a FLiP filter on a finite time interval with the cross-correlation method. In contrast to the PPSs, OPSs can identify also non-orthogonal FLiP filters, as the Volterra filters.

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Authors:
Alberto Carini, Simone Orcioni, Stefania Cecchi
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7 May 2019 - 3:07pm
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[1] Alberto Carini, Simone Orcioni, Stefania Cecchi, "Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3948. Accessed: Jan. 19, 2020.
@article{3948-19,
url = {http://sigport.org/3948},
author = {Alberto Carini; Simone Orcioni; Stefania Cecchi },
publisher = {IEEE SigPort},
title = {Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters},
year = {2019} }
TY - EJOUR
T1 - Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters
AU - Alberto Carini; Simone Orcioni; Stefania Cecchi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3948
ER -
Alberto Carini, Simone Orcioni, Stefania Cecchi. (2019). Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters. IEEE SigPort. http://sigport.org/3948
Alberto Carini, Simone Orcioni, Stefania Cecchi, 2019. Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters. Available at: http://sigport.org/3948.
Alberto Carini, Simone Orcioni, Stefania Cecchi. (2019). "Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters." Web.
1. Alberto Carini, Simone Orcioni, Stefania Cecchi. Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3948

Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach


Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models.

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Authors:
Sajad Mousavi , Fatemeh Afghah
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7 May 2019 - 3:03pm
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[1] Sajad Mousavi , Fatemeh Afghah, "Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3947. Accessed: Jan. 19, 2020.
@article{3947-19,
url = {http://sigport.org/3947},
author = {Sajad Mousavi ; Fatemeh Afghah },
publisher = {IEEE SigPort},
title = {Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach},
year = {2019} }
TY - EJOUR
T1 - Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach
AU - Sajad Mousavi ; Fatemeh Afghah
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3947
ER -
Sajad Mousavi , Fatemeh Afghah. (2019). Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach. IEEE SigPort. http://sigport.org/3947
Sajad Mousavi , Fatemeh Afghah, 2019. Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach. Available at: http://sigport.org/3947.
Sajad Mousavi , Fatemeh Afghah. (2019). "Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach." Web.
1. Sajad Mousavi , Fatemeh Afghah. Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3947

Bluetooth based Indoor Localization using Triplet Embeddings

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Authors:
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan
Submitted On:
7 May 2019 - 2:43pm
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[1] Karel Mundnich, Benjamin Girault, Shrikanth Narayanan, "Bluetooth based Indoor Localization using Triplet Embeddings", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3945. Accessed: Jan. 19, 2020.
@article{3945-19,
url = {http://sigport.org/3945},
author = {Karel Mundnich; Benjamin Girault; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Bluetooth based Indoor Localization using Triplet Embeddings},
year = {2019} }
TY - EJOUR
T1 - Bluetooth based Indoor Localization using Triplet Embeddings
AU - Karel Mundnich; Benjamin Girault; Shrikanth Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3945
ER -
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan. (2019). Bluetooth based Indoor Localization using Triplet Embeddings. IEEE SigPort. http://sigport.org/3945
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan, 2019. Bluetooth based Indoor Localization using Triplet Embeddings. Available at: http://sigport.org/3945.
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan. (2019). "Bluetooth based Indoor Localization using Triplet Embeddings." Web.
1. Karel Mundnich, Benjamin Girault, Shrikanth Narayanan. Bluetooth based Indoor Localization using Triplet Embeddings [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3945

Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders

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Submitted On:
7 May 2019 - 2:36pm
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[1] , "Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3944. Accessed: Jan. 19, 2020.
@article{3944-19,
url = {http://sigport.org/3944},
author = { },
publisher = {IEEE SigPort},
title = {Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders},
year = {2019} }
TY - EJOUR
T1 - Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3944
ER -
. (2019). Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders. IEEE SigPort. http://sigport.org/3944
, 2019. Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders. Available at: http://sigport.org/3944.
. (2019). "Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders." Web.
1. . Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3944

End-to-End Anchored Speech Recognition


Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device- directed speech in the presence of interfering background speech, i.e., background noise and interfering speech from another person or media device in proximity need to be ignored. We propose two end-to-end models to tackle this problem with information extracted from the “anchored segment”.

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Authors:
Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister
Submitted On:
7 May 2019 - 2:33pm
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[1] Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister, "End-to-End Anchored Speech Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3943. Accessed: Jan. 19, 2020.
@article{3943-19,
url = {http://sigport.org/3943},
author = {Yiming Wang; Xing Fan; I-Fan Chen; Yuzong Liu; Tongfei Chen; Björn Hoffmeister },
publisher = {IEEE SigPort},
title = {End-to-End Anchored Speech Recognition},
year = {2019} }
TY - EJOUR
T1 - End-to-End Anchored Speech Recognition
AU - Yiming Wang; Xing Fan; I-Fan Chen; Yuzong Liu; Tongfei Chen; Björn Hoffmeister
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3943
ER -
Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister. (2019). End-to-End Anchored Speech Recognition. IEEE SigPort. http://sigport.org/3943
Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister, 2019. End-to-End Anchored Speech Recognition. Available at: http://sigport.org/3943.
Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister. (2019). "End-to-End Anchored Speech Recognition." Web.
1. Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister. End-to-End Anchored Speech Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3943

EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING


Code Switching refers to the phenomenon of changing languages within a sentence or discourse, and it represents a challenge for conventional automatic speech recognition systems deployed to tackle a single target language. The code switching problem is complicated by the lack of multi-lingual training data needed to build new and ad hoc multi-lingual acoustic and language models. In this work, we present a prototype research code-switching speech recognition system that leverages existing monolingual acoustic and language models, i.e., no ad hoc training is needed.

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Authors:
Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi
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7 May 2019 - 2:28pm
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[1] Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi, "EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3942. Accessed: Jan. 19, 2020.
@article{3942-19,
url = {http://sigport.org/3942},
author = {Zhen Huang; Xiaodan Zhuang; Daben Liu; Xiaoqiang Xiao; Yuchen Zhang; Sabato Marco Siniscalchi },
publisher = {IEEE SigPort},
title = {EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING},
year = {2019} }
TY - EJOUR
T1 - EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING
AU - Zhen Huang; Xiaodan Zhuang; Daben Liu; Xiaoqiang Xiao; Yuchen Zhang; Sabato Marco Siniscalchi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3942
ER -
Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi. (2019). EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING. IEEE SigPort. http://sigport.org/3942
Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi, 2019. EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING. Available at: http://sigport.org/3942.
Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi. (2019). "EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING." Web.
1. Zhen Huang, Xiaodan Zhuang, Daben Liu, Xiaoqiang Xiao, Yuchen Zhang, Sabato Marco Siniscalchi. EXPLORING RETRAINING-FREE SPEECH RECOGNITION FOR INTRA-SENTENTIAL CODE-SWITCHING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3942

Directional interference suppression using a spatial relative transfer function feature

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Authors:
Sebastian Braun, Ivan Tashev
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7 May 2019 - 2:23pm
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[1] Sebastian Braun, Ivan Tashev, "Directional interference suppression using a spatial relative transfer function feature", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3941. Accessed: Jan. 19, 2020.
@article{3941-19,
url = {http://sigport.org/3941},
author = {Sebastian Braun; Ivan Tashev },
publisher = {IEEE SigPort},
title = {Directional interference suppression using a spatial relative transfer function feature},
year = {2019} }
TY - EJOUR
T1 - Directional interference suppression using a spatial relative transfer function feature
AU - Sebastian Braun; Ivan Tashev
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3941
ER -
Sebastian Braun, Ivan Tashev. (2019). Directional interference suppression using a spatial relative transfer function feature. IEEE SigPort. http://sigport.org/3941
Sebastian Braun, Ivan Tashev, 2019. Directional interference suppression using a spatial relative transfer function feature. Available at: http://sigport.org/3941.
Sebastian Braun, Ivan Tashev. (2019). "Directional interference suppression using a spatial relative transfer function feature." Web.
1. Sebastian Braun, Ivan Tashev. Directional interference suppression using a spatial relative transfer function feature [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3941

Multiple Linear Regression for High Efficiency Video Intra Coding


In video coding frameworks, the essence of intra coding is leveraging the spatial correlation within a frame to remove redundancy thus achieving compact transmitting data. With modern video acquisition devices improvement, more high-definition videos emerge into people’s lives which has set a new challenge for high-efficiency video coding. In this paper, we propose a novel intra video coding scheme based on Multiple Linear Regression (MLR), named Multiple linear regression Intra Prediction (MIP).

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Authors:
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu
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7 May 2019 - 2:19pm
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[1] Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu, "Multiple Linear Regression for High Efficiency Video Intra Coding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3940. Accessed: Jan. 19, 2020.
@article{3940-19,
url = {http://sigport.org/3940},
author = {Zhaobin Zhang; Yue Li; Li Li; Zhu Li; Shan Liu },
publisher = {IEEE SigPort},
title = {Multiple Linear Regression for High Efficiency Video Intra Coding},
year = {2019} }
TY - EJOUR
T1 - Multiple Linear Regression for High Efficiency Video Intra Coding
AU - Zhaobin Zhang; Yue Li; Li Li; Zhu Li; Shan Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3940
ER -
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu. (2019). Multiple Linear Regression for High Efficiency Video Intra Coding. IEEE SigPort. http://sigport.org/3940
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu, 2019. Multiple Linear Regression for High Efficiency Video Intra Coding. Available at: http://sigport.org/3940.
Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu. (2019). "Multiple Linear Regression for High Efficiency Video Intra Coding." Web.
1. Zhaobin Zhang, Yue Li, Li Li, Zhu Li, Shan Liu. Multiple Linear Regression for High Efficiency Video Intra Coding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3940

Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding

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Authors:
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO
Submitted On:
7 May 2019 - 2:14pm
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[1] CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO, "Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3939. Accessed: Jan. 19, 2020.
@article{3939-19,
url = {http://sigport.org/3939},
author = {CHUNBO LUO; GEYONG MIN; WANG MIAO; LIANG WU; TIANXIAO ZHAO },
publisher = {IEEE SigPort},
title = {Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding},
year = {2019} }
TY - EJOUR
T1 - Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding
AU - CHUNBO LUO; GEYONG MIN; WANG MIAO; LIANG WU; TIANXIAO ZHAO
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3939
ER -
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. (2019). Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding. IEEE SigPort. http://sigport.org/3939
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO, 2019. Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding. Available at: http://sigport.org/3939.
CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. (2019). "Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding." Web.
1. CHUNBO LUO, GEYONG MIN, WANG MIAO, LIANG WU, TIANXIAO ZHAO. Sensor-Assisted Global Motion Estimation for Efficient UAV Video Coding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3939

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