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Image, Video, and Multidimensional Signal Processing

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.

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Authors:
Prashant W. Patil, Omkar Thawakar, Akshay Dudhane, Subrahmanyam Murala
Submitted On:
15 September 2019 - 11:02am
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[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/4621. Accessed: Oct. 20, 2019.
@article{4621-19,
url = {http://sigport.org/4621},
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/4621
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/4621
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/4621.
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/4621

Single Image Depth Estimation Using Deep Adversarial Training


Scene understanding is an active area of research in computer vision that encompasses several different problems. The LiDARs and stereo depth sensor have their own restrictions such as light sensitiveness, power consumption and short-range. In this paper, we propose a two-stream deep adversarial network for single image depth estimation in RGB images. For stream I network, we propose a novel encoder-decoder architecture using residual concepts to extract course-level depth features.

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Authors:
Praful Hambarde, Akshay Dudhane, Subrahmanyam Murala
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15 September 2019 - 10:26am
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[1] Praful Hambarde, Akshay Dudhane, Subrahmanyam Murala, "Single Image Depth Estimation Using Deep Adversarial Training", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4620. Accessed: Oct. 20, 2019.
@article{4620-19,
url = {http://sigport.org/4620},
author = {Praful Hambarde; Akshay Dudhane; Subrahmanyam Murala },
publisher = {IEEE SigPort},
title = {Single Image Depth Estimation Using Deep Adversarial Training},
year = {2019} }
TY - EJOUR
T1 - Single Image Depth Estimation Using Deep Adversarial Training
AU - Praful Hambarde; Akshay Dudhane; Subrahmanyam Murala
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4620
ER -
Praful Hambarde, Akshay Dudhane, Subrahmanyam Murala. (2019). Single Image Depth Estimation Using Deep Adversarial Training. IEEE SigPort. http://sigport.org/4620
Praful Hambarde, Akshay Dudhane, Subrahmanyam Murala, 2019. Single Image Depth Estimation Using Deep Adversarial Training. Available at: http://sigport.org/4620.
Praful Hambarde, Akshay Dudhane, Subrahmanyam Murala. (2019). "Single Image Depth Estimation Using Deep Adversarial Training." Web.
1. Praful Hambarde, Akshay Dudhane, Subrahmanyam Murala. Single Image Depth Estimation Using Deep Adversarial Training [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4620

Fashion Recommendation on Street Images


Learning the compatibility relationship is of vital importance to a fashion recommendation system, while existing works achieve this merely on product images but not on street images in the complex daily life scenario. In this paper, we propose a novel fashion recommendation system: Given a query item of interest in the street scenario, the system can return the compatible items. More specifically, a two-stage curriculum learning scheme is developed to transfer the semantics from the product to street outfit images.

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Authors:
Zhan Huijing, Shi Boxin, Chen Jiawei, Zheng Qian, Duan Lingyu Alex C. Kot
Submitted On:
13 September 2019 - 12:13am
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[1] Zhan Huijing, Shi Boxin, Chen Jiawei, Zheng Qian, Duan Lingyu Alex C. Kot, "Fashion Recommendation on Street Images", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4606. Accessed: Oct. 20, 2019.
@article{4606-19,
url = {http://sigport.org/4606},
author = {Zhan Huijing; Shi Boxin; Chen Jiawei; Zheng Qian; Duan Lingyu Alex C. Kot },
publisher = {IEEE SigPort},
title = {Fashion Recommendation on Street Images},
year = {2019} }
TY - EJOUR
T1 - Fashion Recommendation on Street Images
AU - Zhan Huijing; Shi Boxin; Chen Jiawei; Zheng Qian; Duan Lingyu Alex C. Kot
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4606
ER -
Zhan Huijing, Shi Boxin, Chen Jiawei, Zheng Qian, Duan Lingyu Alex C. Kot. (2019). Fashion Recommendation on Street Images. IEEE SigPort. http://sigport.org/4606
Zhan Huijing, Shi Boxin, Chen Jiawei, Zheng Qian, Duan Lingyu Alex C. Kot, 2019. Fashion Recommendation on Street Images. Available at: http://sigport.org/4606.
Zhan Huijing, Shi Boxin, Chen Jiawei, Zheng Qian, Duan Lingyu Alex C. Kot. (2019). "Fashion Recommendation on Street Images." Web.
1. Zhan Huijing, Shi Boxin, Chen Jiawei, Zheng Qian, Duan Lingyu Alex C. Kot. Fashion Recommendation on Street Images [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4606

MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION


The recently-proposed reinforcement learning for mapless visual navigation can generate an optimal policy for searching different targets. However, most state-of-the-art deep reinforcement learning (DRL) models depend on hard rewards to learn the optimal policy, which can lead to the lack of previous diverse experiences. Moreover, these pre-trained DRL models cannot generalize well to un-trained tasks. To overcome these problems above, in this paper, we propose a Memorybased Parameterized Skills Learning (MPSL) model for mapless visual navigation.

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Authors:
Yuyang Liu, Yang Cong and Gan Sun
Submitted On:
11 September 2019 - 11:06pm
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[1] Yuyang Liu, Yang Cong and Gan Sun, "MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4598. Accessed: Oct. 20, 2019.
@article{4598-19,
url = {http://sigport.org/4598},
author = {Yuyang Liu; Yang Cong and Gan Sun },
publisher = {IEEE SigPort},
title = {MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION},
year = {2019} }
TY - EJOUR
T1 - MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION
AU - Yuyang Liu; Yang Cong and Gan Sun
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4598
ER -
Yuyang Liu, Yang Cong and Gan Sun. (2019). MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION. IEEE SigPort. http://sigport.org/4598
Yuyang Liu, Yang Cong and Gan Sun, 2019. MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION. Available at: http://sigport.org/4598.
Yuyang Liu, Yang Cong and Gan Sun. (2019). "MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION." Web.
1. Yuyang Liu, Yang Cong and Gan Sun. MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4598

COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION


Non-local sparsity has been widely concerned in image compressive
sensing. Considering the difference of distribution
characteristic of among group-based sparse coefficients of
image, a new method for image compressive sensing reconstruction
(ICSR) is proposed based on the z-scores standardized
group sparse representation (ZSGSR). Here, the
similar patch groups of the image are firstly extracted and
decomposed by adaptive PCA dictionary, then the resulting
coefficients are normalized using z-score standardization in

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Authors:
Zhirong Gao, Lixin Ding, Zhongyi Gong, Qiming Xiong
Submitted On:
10 September 2019 - 10:21pm
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[1] Zhirong Gao, Lixin Ding, Zhongyi Gong, Qiming Xiong, "COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4579. Accessed: Oct. 20, 2019.
@article{4579-19,
url = {http://sigport.org/4579},
author = {Zhirong Gao; Lixin Ding; Zhongyi Gong; Qiming Xiong },
publisher = {IEEE SigPort},
title = {COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION},
year = {2019} }
TY - EJOUR
T1 - COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION
AU - Zhirong Gao; Lixin Ding; Zhongyi Gong; Qiming Xiong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4579
ER -
Zhirong Gao, Lixin Ding, Zhongyi Gong, Qiming Xiong. (2019). COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION. IEEE SigPort. http://sigport.org/4579
Zhirong Gao, Lixin Ding, Zhongyi Gong, Qiming Xiong, 2019. COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION. Available at: http://sigport.org/4579.
Zhirong Gao, Lixin Ding, Zhongyi Gong, Qiming Xiong. (2019). "COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION." Web.
1. Zhirong Gao, Lixin Ding, Zhongyi Gong, Qiming Xiong. COMPRESSIVE SENSING RECONSTRUCTION BASED ON STANDARDIZED GROUP SPARSE REPRESENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4579

Denoising Adversarial Networks for Rain Removal and Reflection Removal

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Authors:
Qian Zheng, Boxin Shi, Xudong Jiang, Ling-Yu Duan, and Alex C. Kot
Submitted On:
10 September 2019 - 10:14pm
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[1] Qian Zheng, Boxin Shi, Xudong Jiang, Ling-Yu Duan, and Alex C. Kot, "Denoising Adversarial Networks for Rain Removal and Reflection Removal", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4578. Accessed: Oct. 20, 2019.
@article{4578-19,
url = {http://sigport.org/4578},
author = {Qian Zheng; Boxin Shi; Xudong Jiang; Ling-Yu Duan; and Alex C. Kot },
publisher = {IEEE SigPort},
title = {Denoising Adversarial Networks for Rain Removal and Reflection Removal},
year = {2019} }
TY - EJOUR
T1 - Denoising Adversarial Networks for Rain Removal and Reflection Removal
AU - Qian Zheng; Boxin Shi; Xudong Jiang; Ling-Yu Duan; and Alex C. Kot
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4578
ER -
Qian Zheng, Boxin Shi, Xudong Jiang, Ling-Yu Duan, and Alex C. Kot. (2019). Denoising Adversarial Networks for Rain Removal and Reflection Removal. IEEE SigPort. http://sigport.org/4578
Qian Zheng, Boxin Shi, Xudong Jiang, Ling-Yu Duan, and Alex C. Kot, 2019. Denoising Adversarial Networks for Rain Removal and Reflection Removal. Available at: http://sigport.org/4578.
Qian Zheng, Boxin Shi, Xudong Jiang, Ling-Yu Duan, and Alex C. Kot. (2019). "Denoising Adversarial Networks for Rain Removal and Reflection Removal." Web.
1. Qian Zheng, Boxin Shi, Xudong Jiang, Ling-Yu Duan, and Alex C. Kot. Denoising Adversarial Networks for Rain Removal and Reflection Removal [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4578

CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE

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10 September 2019 - 2:54pm
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[1] , "CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4571. Accessed: Oct. 20, 2019.
@article{4571-19,
url = {http://sigport.org/4571},
author = { },
publisher = {IEEE SigPort},
title = {CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE},
year = {2019} }
TY - EJOUR
T1 - CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4571
ER -
. (2019). CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE. IEEE SigPort. http://sigport.org/4571
, 2019. CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE. Available at: http://sigport.org/4571.
. (2019). "CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE." Web.
1. . CLUSTERING IMAGES BY UNMASKING - A NEW BASELINE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4571

EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS


Immediate and accurate detection of wildfire is essentially important in forest monitoring systems
•One of the most harmful hazards in rural areas
•For wildfire detection, the use of visible-range video captured by surveillance cameras are suitable
•They can be deployed and operated in a cost-effective manner
•The challenge is to provide a robust detection system with negligible false positive rates
•If the flames are visible, they can be detected by analyzing the motion and color clues of a video

Paper Details

Authors:
Suleyman Aslan, Ugur Gudukbay, Behçet Uğur Töreyin, Ahmet Enis Çetin
Submitted On:
10 May 2019 - 10:49pm
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[1] Suleyman Aslan, Ugur Gudukbay, Behçet Uğur Töreyin, Ahmet Enis Çetin, "EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4428. Accessed: Oct. 20, 2019.
@article{4428-19,
url = {http://sigport.org/4428},
author = {Suleyman Aslan; Ugur Gudukbay; Behçet Uğur Töreyin; Ahmet Enis Çetin },
publisher = {IEEE SigPort},
title = {EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS
AU - Suleyman Aslan; Ugur Gudukbay; Behçet Uğur Töreyin; Ahmet Enis Çetin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4428
ER -
Suleyman Aslan, Ugur Gudukbay, Behçet Uğur Töreyin, Ahmet Enis Çetin. (2019). EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/4428
Suleyman Aslan, Ugur Gudukbay, Behçet Uğur Töreyin, Ahmet Enis Çetin, 2019. EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/4428.
Suleyman Aslan, Ugur Gudukbay, Behçet Uğur Töreyin, Ahmet Enis Çetin. (2019). "EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS." Web.
1. Suleyman Aslan, Ugur Gudukbay, Behçet Uğur Töreyin, Ahmet Enis Çetin. EARLY WILDFIRE SMOKE DETECTION BASED ON MOTION-BASED GEOMETRIC IMAGE TRANSFORMATION AND DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4428

Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity


Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the “blind” environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR.

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Authors:
Guangcheng Wang, Zhongyuan Wang, Ke Gu, Zhifang Xia
Submitted On:
9 May 2019 - 10:49pm
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[1] Guangcheng Wang, Zhongyuan Wang, Ke Gu, Zhifang Xia, "Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4254. Accessed: Oct. 20, 2019.
@article{4254-19,
url = {http://sigport.org/4254},
author = {Guangcheng Wang; Zhongyuan Wang; Ke Gu; Zhifang Xia },
publisher = {IEEE SigPort},
title = {Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity},
year = {2019} }
TY - EJOUR
T1 - Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity
AU - Guangcheng Wang; Zhongyuan Wang; Ke Gu; Zhifang Xia
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4254
ER -
Guangcheng Wang, Zhongyuan Wang, Ke Gu, Zhifang Xia. (2019). Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity. IEEE SigPort. http://sigport.org/4254
Guangcheng Wang, Zhongyuan Wang, Ke Gu, Zhifang Xia, 2019. Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity. Available at: http://sigport.org/4254.
Guangcheng Wang, Zhongyuan Wang, Ke Gu, Zhifang Xia. (2019). "Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity." Web.
1. Guangcheng Wang, Zhongyuan Wang, Ke Gu, Zhifang Xia. Blind Quality Assessment for 3D-Synthesized Images by Measuring Geometric Distortions and Image Complexity [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4254

Learning Motion Disfluencies for Automatic Sign Language Segmentation


We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features.

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Authors:
Iva Farag
Submitted On:
9 May 2019 - 2:18am
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[1] Iva Farag, "Learning Motion Disfluencies for Automatic Sign Language Segmentation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4154. Accessed: Oct. 20, 2019.
@article{4154-19,
url = {http://sigport.org/4154},
author = {Iva Farag },
publisher = {IEEE SigPort},
title = {Learning Motion Disfluencies for Automatic Sign Language Segmentation},
year = {2019} }
TY - EJOUR
T1 - Learning Motion Disfluencies for Automatic Sign Language Segmentation
AU - Iva Farag
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4154
ER -
Iva Farag. (2019). Learning Motion Disfluencies for Automatic Sign Language Segmentation. IEEE SigPort. http://sigport.org/4154
Iva Farag, 2019. Learning Motion Disfluencies for Automatic Sign Language Segmentation. Available at: http://sigport.org/4154.
Iva Farag. (2019). "Learning Motion Disfluencies for Automatic Sign Language Segmentation." Web.
1. Iva Farag. Learning Motion Disfluencies for Automatic Sign Language Segmentation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4154

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