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Pattern recognition and classification (MLR-PATT)

Scattering Features for Multimodal Gait Recognition


Gait.pdf

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Authors:
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton
Submitted On:
25 November 2017 - 8:19pm
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Gait.pdf

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[1] Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, "Scattering Features for Multimodal Gait Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2369. Accessed: Dec. 16, 2017.
@article{2369-17,
url = {http://sigport.org/2369},
author = {Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton },
publisher = {IEEE SigPort},
title = {Scattering Features for Multimodal Gait Recognition},
year = {2017} }
TY - EJOUR
T1 - Scattering Features for Multimodal Gait Recognition
AU - Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2369
ER -
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). Scattering Features for Multimodal Gait Recognition. IEEE SigPort. http://sigport.org/2369
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, 2017. Scattering Features for Multimodal Gait Recognition. Available at: http://sigport.org/2369.
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). "Scattering Features for Multimodal Gait Recognition." Web.
1. Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. Scattering Features for Multimodal Gait Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2369

Scattering Features for Multimodal Gait Recognition


Gait.pdf

PDF icon Gait.pdf (14 downloads)

Paper Details

Authors:
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton
Submitted On:
25 November 2017 - 8:19pm
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Gait.pdf

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[1] Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, "Scattering Features for Multimodal Gait Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2368. Accessed: Dec. 16, 2017.
@article{2368-17,
url = {http://sigport.org/2368},
author = {Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton },
publisher = {IEEE SigPort},
title = {Scattering Features for Multimodal Gait Recognition},
year = {2017} }
TY - EJOUR
T1 - Scattering Features for Multimodal Gait Recognition
AU - Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2368
ER -
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). Scattering Features for Multimodal Gait Recognition. IEEE SigPort. http://sigport.org/2368
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, 2017. Scattering Features for Multimodal Gait Recognition. Available at: http://sigport.org/2368.
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). "Scattering Features for Multimodal Gait Recognition." Web.
1. Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. Scattering Features for Multimodal Gait Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2368

Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides


Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network.

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Authors:
Yuewei Na, Siyang Li, C.-C. Jay Kuo
Submitted On:
14 November 2017 - 10:09pm
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GlobalSIP17_Oral_Segmentation-aided_Text_Detection

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[1] Yuewei Na, Siyang Li, C.-C. Jay Kuo, "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2354. Accessed: Dec. 16, 2017.
@article{2354-17,
url = {http://sigport.org/2354},
author = {Yuewei Na; Siyang Li; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides },
year = {2017} }
TY - EJOUR
T1 - Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides
AU - Yuewei Na; Siyang Li; C.-C. Jay Kuo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2354
ER -
Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides . IEEE SigPort. http://sigport.org/2354
Yuewei Na, Siyang Li, C.-C. Jay Kuo, 2017. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides . Available at: http://sigport.org/2354.
Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides ." Web.
1. Yuewei Na, Siyang Li, C.-C. Jay Kuo. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2354

A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury


Assistive technologies such as wheelchairs, canes, and walkers have significantly improved the mobility, function, and quality of life for individuals with spinal cord injury (SCI). In this article, we propose a framework which combines machine learning algorithms with wearable sensors to capture and track mobility in individuals with SCI. Pilot testing in two individuals without SCI indicated that four to seven features obtained from sensors worn on the body or placed on the assistive technology could successfully detect mobility and mobility modes.

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Authors:
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath
Submitted On:
13 November 2017 - 10:28am
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MobilityFramework_0.5_PDF.pdf

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[1] Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath, "A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2337. Accessed: Dec. 16, 2017.
@article{2337-17,
url = {http://sigport.org/2337},
author = {Amir Mohammad Amiri; Noor Shoaib; Shivayogi V. Hiremath },
publisher = {IEEE SigPort},
title = {A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury},
year = {2017} }
TY - EJOUR
T1 - A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury
AU - Amir Mohammad Amiri; Noor Shoaib; Shivayogi V. Hiremath
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2337
ER -
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath. (2017). A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury. IEEE SigPort. http://sigport.org/2337
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath, 2017. A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury. Available at: http://sigport.org/2337.
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath. (2017). "A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury." Web.
1. Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath. A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2337

Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster


Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network.

Paper Details

Authors:
Yuewei Na, Siyang Li, C.-C. Jay Kuo
Submitted On:
14 November 2017 - 10:11pm
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GlobalSIP2017_Segmentation-aided_Text_Detection

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[1] Yuewei Na, Siyang Li, C.-C. Jay Kuo, "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2302. Accessed: Dec. 16, 2017.
@article{2302-17,
url = {http://sigport.org/2302},
author = {Yuewei Na; Siyang Li; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster},
year = {2017} }
TY - EJOUR
T1 - Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster
AU - Yuewei Na; Siyang Li; C.-C. Jay Kuo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2302
ER -
Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster. IEEE SigPort. http://sigport.org/2302
Yuewei Na, Siyang Li, C.-C. Jay Kuo, 2017. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster. Available at: http://sigport.org/2302.
Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster." Web.
1. Yuewei Na, Siyang Li, C.-C. Jay Kuo. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2302

Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection


Introductory derivations are done before introducing the metric of Fisher’s LDA. Then, after introducing Fisher's LDA and its different forms, Fisher’s Metrics and their maximizations are given. The idea of traces in Fisher's LDA is explained. Weighted features method is used for feature selection. Various simple examples are provided to clarify the idea for the feature selection.

Sunu1.pptx

File Sunu1.pptx (38 downloads)

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Authors:
Mehmet Koc, Ozen Yelbasi
Submitted On:
4 October 2017 - 9:26am
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Sunu1.pptx

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[1] Mehmet Koc, Ozen Yelbasi, "Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2255. Accessed: Dec. 16, 2017.
@article{2255-17,
url = {http://sigport.org/2255},
author = {Mehmet Koc; Ozen Yelbasi },
publisher = {IEEE SigPort},
title = {Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection},
year = {2017} }
TY - EJOUR
T1 - Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection
AU - Mehmet Koc; Ozen Yelbasi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2255
ER -
Mehmet Koc, Ozen Yelbasi. (2017). Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection. IEEE SigPort. http://sigport.org/2255
Mehmet Koc, Ozen Yelbasi, 2017. Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection. Available at: http://sigport.org/2255.
Mehmet Koc, Ozen Yelbasi. (2017). "Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection." Web.
1. Mehmet Koc, Ozen Yelbasi. Fisher’s Linear Discriminant Analysis and Its Use in Feature Selection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2255

LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION

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Authors:
Guangyi Chen, Jiwen Lu, Jianjiang Feng, Jie Zhou
Submitted On:
16 September 2017 - 9:31am
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ICIP_presentation.pdf

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[1] Guangyi Chen, Jiwen Lu, Jianjiang Feng, Jie Zhou, "LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2192. Accessed: Dec. 16, 2017.
@article{2192-17,
url = {http://sigport.org/2192},
author = {Guangyi Chen; Jiwen Lu; Jianjiang Feng; Jie Zhou },
publisher = {IEEE SigPort},
title = {LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION},
year = {2017} }
TY - EJOUR
T1 - LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION
AU - Guangyi Chen; Jiwen Lu; Jianjiang Feng; Jie Zhou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2192
ER -
Guangyi Chen, Jiwen Lu, Jianjiang Feng, Jie Zhou. (2017). LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION. IEEE SigPort. http://sigport.org/2192
Guangyi Chen, Jiwen Lu, Jianjiang Feng, Jie Zhou, 2017. LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION. Available at: http://sigport.org/2192.
Guangyi Chen, Jiwen Lu, Jianjiang Feng, Jie Zhou. (2017). "LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION." Web.
1. Guangyi Chen, Jiwen Lu, Jianjiang Feng, Jie Zhou. LOCALIZED MULTI-KERNEL DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS FOR VIDEO-BASED PERSON RE-IDENTIFICATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2192

Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera


Background subtraction from the given image is a widely used method for moving object detection. However, this method is vulnerable to dynamic background in a moving camera video. In this paper, we propose a novel moving object detection approach using deep learning to achieve a robust performance even in a dynamic background. The proposed approach considers appearance features as well as motion features. To this end, we design a deep learning architecture composed of two networks: an appearance network and a motion network.

ICIP17_heo.pdf

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Authors:
Byeongho Heo, Kimin Yun, Jin Young Choi
Submitted On:
3 November 2017 - 7:15pm
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slide

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paper

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[1] Byeongho Heo, Kimin Yun, Jin Young Choi, "Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2144. Accessed: Dec. 16, 2017.
@article{2144-17,
url = {http://sigport.org/2144},
author = {Byeongho Heo; Kimin Yun; Jin Young Choi },
publisher = {IEEE SigPort},
title = {Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera},
year = {2017} }
TY - EJOUR
T1 - Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera
AU - Byeongho Heo; Kimin Yun; Jin Young Choi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2144
ER -
Byeongho Heo, Kimin Yun, Jin Young Choi. (2017). Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera. IEEE SigPort. http://sigport.org/2144
Byeongho Heo, Kimin Yun, Jin Young Choi, 2017. Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera. Available at: http://sigport.org/2144.
Byeongho Heo, Kimin Yun, Jin Young Choi. (2017). "Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera." Web.
1. Byeongho Heo, Kimin Yun, Jin Young Choi. Appearance and Motion based Deep Learning Architecture for Moving Object Detection in Moving Camera [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2144

REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK

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Authors:
Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Aidong Men, Yun Zhou
Submitted On:
15 September 2017 - 6:19am
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2259-REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK.pdf

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[1] Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Aidong Men, Yun Zhou, "REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2117. Accessed: Dec. 16, 2017.
@article{2117-17,
url = {http://sigport.org/2117},
author = {Yajing Guo; Xiaoqiang Guo; Zhuqing Jiang; Aidong Men; Yun Zhou },
publisher = {IEEE SigPort},
title = {REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK},
year = {2017} }
TY - EJOUR
T1 - REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK
AU - Yajing Guo; Xiaoqiang Guo; Zhuqing Jiang; Aidong Men; Yun Zhou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2117
ER -
Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Aidong Men, Yun Zhou. (2017). REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK. IEEE SigPort. http://sigport.org/2117
Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Aidong Men, Yun Zhou, 2017. REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK. Available at: http://sigport.org/2117.
Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Aidong Men, Yun Zhou. (2017). "REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK." Web.
1. Yajing Guo, Xiaoqiang Guo, Zhuqing Jiang, Aidong Men, Yun Zhou. REAL-TIME OBJECT DETECTION BY A MULTI-FEATURE FULLY CONVOLUTIONAL NETWORK [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2117

ICIP2017 poster

Paper Details

Authors:
Zhongxing Han, Hui Zhang, Jinfang Zhang, Xiaohui Hu
Submitted On:
15 September 2017 - 4:27am
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ICIP2017 poster

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[1] Zhongxing Han, Hui Zhang, Jinfang Zhang, Xiaohui Hu, "ICIP2017 poster", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2103. Accessed: Dec. 16, 2017.
@article{2103-17,
url = {http://sigport.org/2103},
author = {Zhongxing Han; Hui Zhang; Jinfang Zhang; Xiaohui Hu },
publisher = {IEEE SigPort},
title = {ICIP2017 poster},
year = {2017} }
TY - EJOUR
T1 - ICIP2017 poster
AU - Zhongxing Han; Hui Zhang; Jinfang Zhang; Xiaohui Hu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2103
ER -
Zhongxing Han, Hui Zhang, Jinfang Zhang, Xiaohui Hu. (2017). ICIP2017 poster. IEEE SigPort. http://sigport.org/2103
Zhongxing Han, Hui Zhang, Jinfang Zhang, Xiaohui Hu, 2017. ICIP2017 poster. Available at: http://sigport.org/2103.
Zhongxing Han, Hui Zhang, Jinfang Zhang, Xiaohui Hu. (2017). "ICIP2017 poster." Web.
1. Zhongxing Han, Hui Zhang, Jinfang Zhang, Xiaohui Hu. ICIP2017 poster [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2103

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