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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

MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION


Millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems realizing directive beamforming require reliable estimation of the wireless propagation channel. However, mmWave channels are characterized by high variability that severely challenges their recovery over short training periods. Current channel estimation techniques exploit either the channel sparsity in the beamspace domain or its low-rank property in the antenna domain, nevertheless, they still require large numbers of training symbols for the satisfactory performance.

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Authors:
E. Vlachos, G. Alexandropoulos, J. Thompson
Submitted On:
22 May 2019 - 11:17pm
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IEEE Signal Processing Letters 2018

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ICASSP19___Poster.pdf

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[1] E. Vlachos, G. Alexandropoulos, J. Thompson, "MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3899. Accessed: May. 23, 2019.
@article{3899-19,
url = {http://sigport.org/3899},
author = {E. Vlachos; G. Alexandropoulos; J. Thompson },
publisher = {IEEE SigPort},
title = {MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION},
year = {2019} }
TY - EJOUR
T1 - MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION
AU - E. Vlachos; G. Alexandropoulos; J. Thompson
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3899
ER -
E. Vlachos, G. Alexandropoulos, J. Thompson. (2019). MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION. IEEE SigPort. http://sigport.org/3899
E. Vlachos, G. Alexandropoulos, J. Thompson, 2019. MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION. Available at: http://sigport.org/3899.
E. Vlachos, G. Alexandropoulos, J. Thompson. (2019). "MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION." Web.
1. E. Vlachos, G. Alexandropoulos, J. Thompson. MASSIVE MIMO CHANNEL ESTIMATION FOR MILLIMETER WAVE SYSTEMS VIA MATRIX COMPLETION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3899

IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN


In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach

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Authors:
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras
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7 May 2019 - 12:59pm
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[1] Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras, "IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3898. Accessed: May. 23, 2019.
@article{3898-19,
url = {http://sigport.org/3898},
author = {Panagiotis Tsinganos; Bruno Cornelis; Jan Cornelis; Bart Jansen; Athanassios Skodras },
publisher = {IEEE SigPort},
title = {IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN},
year = {2019} }
TY - EJOUR
T1 - IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN
AU - Panagiotis Tsinganos; Bruno Cornelis; Jan Cornelis; Bart Jansen; Athanassios Skodras
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3898
ER -
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras. (2019). IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN. IEEE SigPort. http://sigport.org/3898
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras, 2019. IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN. Available at: http://sigport.org/3898.
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras. (2019). "IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN." Web.
1. Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras. IMPROVED GESTURE RECOGNITION BASED ON sEMG SIGNALS AND TCN [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3898

Fully Supervised Speaker Diarization


In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different speakers interleave in the time domain. This RNN is naturally integrated with a distance-dependent Chinese restaurant process (ddCRP) to accommodate an unknown number of speakers.

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Authors:
Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang
Submitted On:
24 April 2019 - 11:06am
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[1] Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang, " Fully Supervised Speaker Diarization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3897. Accessed: May. 23, 2019.
@article{3897-19,
url = {http://sigport.org/3897},
author = {Aonan Zhang; Quan Wang; Zhenyao Zhu; John Paisley; Chong Wang },
publisher = {IEEE SigPort},
title = { Fully Supervised Speaker Diarization},
year = {2019} }
TY - EJOUR
T1 - Fully Supervised Speaker Diarization
AU - Aonan Zhang; Quan Wang; Zhenyao Zhu; John Paisley; Chong Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3897
ER -
Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang. (2019). Fully Supervised Speaker Diarization. IEEE SigPort. http://sigport.org/3897
Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang, 2019. Fully Supervised Speaker Diarization. Available at: http://sigport.org/3897.
Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang. (2019). " Fully Supervised Speaker Diarization." Web.
1. Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang. Fully Supervised Speaker Diarization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3897

Tuplemax Loss for Language Identification


In many scenarios of a language identification task, the user will specify a small set of languages which he/she can speak instead of a large set of all possible languages. We want to model such prior knowledge into the way we train our neural networks, by replacing the commonly used softmax loss function with a novel loss function named tuplemax loss. As a matter of fact, a typical language identification system launched in North America has about 95% users who could speak no more than two languages.

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Authors:
Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno
Submitted On:
24 April 2019 - 11:03am
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[1] Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno, "Tuplemax Loss for Language Identification", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3896. Accessed: May. 23, 2019.
@article{3896-19,
url = {http://sigport.org/3896},
author = {Li Wan; Prashant Sridhar; Yang Yu; Quan Wang; Ignacio Lopez Moreno },
publisher = {IEEE SigPort},
title = {Tuplemax Loss for Language Identification},
year = {2019} }
TY - EJOUR
T1 - Tuplemax Loss for Language Identification
AU - Li Wan; Prashant Sridhar; Yang Yu; Quan Wang; Ignacio Lopez Moreno
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3896
ER -
Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno. (2019). Tuplemax Loss for Language Identification. IEEE SigPort. http://sigport.org/3896
Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno, 2019. Tuplemax Loss for Language Identification. Available at: http://sigport.org/3896.
Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno. (2019). "Tuplemax Loss for Language Identification." Web.
1. Li Wan, Prashant Sridhar, Yang Yu, Quan Wang, Ignacio Lopez Moreno. Tuplemax Loss for Language Identification [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3896

HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN


We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality of state-of-the-art classic wide-band codecs. Moreover, it is demonstrated that the proposed scheme can provide a meaningful rate-distortion trade-off without retraining. We evaluate the proposed scheme in a series of listening tests and discuss limitations of the approach.

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Authors:
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes
Submitted On:
23 May 2019 - 7:33am
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Poster

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[1] Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes, "HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3895. Accessed: May. 23, 2019.
@article{3895-19,
url = {http://sigport.org/3895},
author = {Janusz Klejsa; Per Hedelin; Cong Zhou; Roy Fejgin; Lars Villemoes },
publisher = {IEEE SigPort},
title = {HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN},
year = {2019} }
TY - EJOUR
T1 - HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN
AU - Janusz Klejsa; Per Hedelin; Cong Zhou; Roy Fejgin; Lars Villemoes
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3895
ER -
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes. (2019). HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN. IEEE SigPort. http://sigport.org/3895
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes, 2019. HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN. Available at: http://sigport.org/3895.
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes. (2019). "HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN." Web.
1. Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes. HIGH-QUALITY SPEECH CODING WITH SAMPLE RNN [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3895

LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT


This paper proposes a linear prediction-based part-defined auto-encoder (PAE) network to enhance speech signal. The PAE is a defined decoder or an established encoder network, based on an efficient learning algorithm or classical model. In this paper, the PAE utilizes AR-Wiener filter as the decoder part, and the AR-Wiener filter is modified as a linear prediction (LP) model by incorporating the modified factor from the residual signal. The parameters of line spectral frequency (LSF) of speech and noise and the Wiener filtering mask are utilized for training targets.

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Authors:
Zihao Cui; Changchun Bao
Submitted On:
16 April 2019 - 5:33am
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Poster_ICASSP_2019_LPPAE_zihao.pdf

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[1] Zihao Cui; Changchun Bao, "LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3894. Accessed: May. 23, 2019.
@article{3894-19,
url = {http://sigport.org/3894},
author = {Zihao Cui; Changchun Bao },
publisher = {IEEE SigPort},
title = {LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT},
year = {2019} }
TY - EJOUR
T1 - LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT
AU - Zihao Cui; Changchun Bao
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3894
ER -
Zihao Cui; Changchun Bao. (2019). LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT. IEEE SigPort. http://sigport.org/3894
Zihao Cui; Changchun Bao, 2019. LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT. Available at: http://sigport.org/3894.
Zihao Cui; Changchun Bao. (2019). "LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT." Web.
1. Zihao Cui; Changchun Bao. LINEAR PREDICTION-BASED PART-DEFINED AUTO-ENCODER USED FOR SPEECH ENHANCEMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3894

Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting


With the boosting requirements of realistic 3D modeling for immersive applications, advent of the newly-developed 3D point cloud has attracted great attention. Frankly, immersive experience using high data volume affirms the importance of efficient compression. Inspired by the video-based point cloud compression (V-PCC), we propose a novel point cloud compression algorithm based on polynomial fitting of proper patches. Moreover, the original point cloud is segmented into various patches.

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Authors:
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li
Submitted On:
15 April 2019 - 5:18am
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[1] Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li, "Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3892. Accessed: May. 23, 2019.
@article{3892-19,
url = {http://sigport.org/3892},
author = {Yingzhan Xu; Wenjie Zhu; Yiling Xu; Zhu Li },
publisher = {IEEE SigPort},
title = {Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting},
year = {2019} }
TY - EJOUR
T1 - Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting
AU - Yingzhan Xu; Wenjie Zhu; Yiling Xu; Zhu Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3892
ER -
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. (2019). Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. IEEE SigPort. http://sigport.org/3892
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li, 2019. Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. Available at: http://sigport.org/3892.
Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. (2019). "Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting." Web.
1. Yingzhan Xu, Wenjie Zhu, Yiling Xu, Zhu Li. Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3892

Crime event embedding with unsupervised feature selection


We present a novel event embedding algorithm for crime data that can jointly capture time, location, and the complex free-text component of each event. The embedding is achieved by regularized Restricted Boltzmann Machines (RBMs), and we introduce a new way to regularize by imposing a ℓ1 penalty on the conditional distributions of the observed variables of RBMs. This choice of regularization performs feature selection and it also leads to efficient computation since the gradient can be computed in a closed form.

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Authors:
Shixiang Zhu, Yao Xie
Submitted On:
14 April 2019 - 4:12pm
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Poster of the paper "Crime event embedding with unsupervised feature selection"

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[1] Shixiang Zhu, Yao Xie, "Crime event embedding with unsupervised feature selection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3891. Accessed: May. 23, 2019.
@article{3891-19,
url = {http://sigport.org/3891},
author = {Shixiang Zhu; Yao Xie },
publisher = {IEEE SigPort},
title = {Crime event embedding with unsupervised feature selection},
year = {2019} }
TY - EJOUR
T1 - Crime event embedding with unsupervised feature selection
AU - Shixiang Zhu; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3891
ER -
Shixiang Zhu, Yao Xie. (2019). Crime event embedding with unsupervised feature selection. IEEE SigPort. http://sigport.org/3891
Shixiang Zhu, Yao Xie, 2019. Crime event embedding with unsupervised feature selection. Available at: http://sigport.org/3891.
Shixiang Zhu, Yao Xie. (2019). "Crime event embedding with unsupervised feature selection." Web.
1. Shixiang Zhu, Yao Xie. Crime event embedding with unsupervised feature selection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3891

Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'

Paper Details

Authors:
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li
Submitted On:
13 April 2019 - 1:34am
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Poster-DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS.pdf

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[1] Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li, "Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3890. Accessed: May. 23, 2019.
@article{3890-19,
url = {http://sigport.org/3890},
author = {Lijun Zhang; Wenge Rong; Baiwen Li; Qi Li },
publisher = {IEEE SigPort},
title = {Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'},
year = {2019} }
TY - EJOUR
T1 - Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'
AU - Lijun Zhang; Wenge Rong; Baiwen Li; Qi Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3890
ER -
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. (2019). Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'. IEEE SigPort. http://sigport.org/3890
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li, 2019. Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'. Available at: http://sigport.org/3890.
Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. (2019). "Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS'." Web.
1. Lijun Zhang, Wenge Rong, Baiwen Li, Qi Li. Poster of 'DEEP HYBRID NETWORKS BASED RESPONSE SELECTION FOR MULTI-TURN DIALOGUE SYSTEMS' [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3890

GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING


Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging.

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Authors:
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen
Submitted On:
18 April 2019 - 11:15pm
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GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING.pdf

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[1] Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen, "GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3889. Accessed: May. 23, 2019.
@article{3889-19,
url = {http://sigport.org/3889},
author = {Xiong Pan; Zejun Zhang; Yizhang Liu; Changcai Yang; Qiufeng Chen; Li Cheng; Jiaxiang Lin; Riqing Chen },
publisher = {IEEE SigPort},
title = {GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING},
year = {2019} }
TY - EJOUR
T1 - GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING
AU - Xiong Pan; Zejun Zhang; Yizhang Liu; Changcai Yang; Qiufeng Chen; Li Cheng; Jiaxiang Lin; Riqing Chen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3889
ER -
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen. (2019). GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING. IEEE SigPort. http://sigport.org/3889
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen, 2019. GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING. Available at: http://sigport.org/3889.
Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen. (2019). "GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING." Web.
1. Xiong Pan, Zejun Zhang, Yizhang Liu, Changcai Yang, Qiufeng Chen, Li Cheng, Jiaxiang Lin, Riqing Chen. GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL-REGION MERGING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3889

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