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Image/Video Processing

Tag Tree Creation of Social Image for Personalized Recommendation


The tags are usually tagged by different users in social image sharing websites, which can indicate image semantic information and imply user's preference. Therefore, the tags can contribute to personalized recommendation of social image. However, the present social image tags models only consider single tag,resulting in the relationships among tags are ignored. In this paper, we propose a novel method to create tag tree of social image for personalized recommendation. Firstly, the tag ranking is realized to remove noisy tags.

1496.pdf

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Authors:
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo
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15 September 2017 - 4:21am
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[1] Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo, "Tag Tree Creation of Social Image for Personalized Recommendation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2102. Accessed: Apr. 20, 2018.
@article{2102-17,
url = {http://sigport.org/2102},
author = {Ying Yang; Jing Zhang; Jihong Liu; Jiafeng Li; Li Zhuo },
publisher = {IEEE SigPort},
title = {Tag Tree Creation of Social Image for Personalized Recommendation},
year = {2017} }
TY - EJOUR
T1 - Tag Tree Creation of Social Image for Personalized Recommendation
AU - Ying Yang; Jing Zhang; Jihong Liu; Jiafeng Li; Li Zhuo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2102
ER -
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo. (2017). Tag Tree Creation of Social Image for Personalized Recommendation. IEEE SigPort. http://sigport.org/2102
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo, 2017. Tag Tree Creation of Social Image for Personalized Recommendation. Available at: http://sigport.org/2102.
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo. (2017). "Tag Tree Creation of Social Image for Personalized Recommendation." Web.
1. Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo. Tag Tree Creation of Social Image for Personalized Recommendation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2102

COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY


The technology used to detect community structures in graphs, or graph clustering technology, is important in a
wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community

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Authors:
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato
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15 September 2017 - 4:15am
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20170914okuda.pdf

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[1] Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato, "COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2101. Accessed: Apr. 20, 2018.
@article{2101-17,
url = {http://sigport.org/2101},
author = {Shin'ichi Satoh; Shoichiro Iwasawa; Shunsuke Yoshida; Yutaka Kidawara; Yoichi Sato },
publisher = {IEEE SigPort},
title = {COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY},
year = {2017} }
TY - EJOUR
T1 - COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY
AU - Shin'ichi Satoh; Shoichiro Iwasawa; Shunsuke Yoshida; Yutaka Kidawara; Yoichi Sato
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2101
ER -
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato. (2017). COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY. IEEE SigPort. http://sigport.org/2101
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato, 2017. COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY. Available at: http://sigport.org/2101.
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato. (2017). "COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY." Web.
1. Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato. COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2101

Deep CNN with colorLines model for unmarked road segmentation


Road detection from a monocular camera is a perception module in any advanced driver assistance or autonomous driving system. Traditional techniques work reasonably well for this problem when the roads are well maintained and the boundaries are clearly marked. However, in many developing countries or even for the rural areas in the developed countries, the assumption does not hold which leads to failure of such techniques.

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Authors:
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee
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15 September 2017 - 4:09am
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Deep CNN with colorLines model for unmarked road segmentation.pdf

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[1] Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee, "Deep CNN with colorLines model for unmarked road segmentation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2099. Accessed: Apr. 20, 2018.
@article{2099-17,
url = {http://sigport.org/2099},
author = {Shashank Yadav; Suvam Patra; Chetan Arora; Subhashis Banerjee },
publisher = {IEEE SigPort},
title = {Deep CNN with colorLines model for unmarked road segmentation},
year = {2017} }
TY - EJOUR
T1 - Deep CNN with colorLines model for unmarked road segmentation
AU - Shashank Yadav; Suvam Patra; Chetan Arora; Subhashis Banerjee
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2099
ER -
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee. (2017). Deep CNN with colorLines model for unmarked road segmentation. IEEE SigPort. http://sigport.org/2099
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee, 2017. Deep CNN with colorLines model for unmarked road segmentation. Available at: http://sigport.org/2099.
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee. (2017). "Deep CNN with colorLines model for unmarked road segmentation." Web.
1. Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee. Deep CNN with colorLines model for unmarked road segmentation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2099

Non-Rigid Image Deformation Algorithm Based on MRLS-TPS


In this paper, we propose a novel closed-form transformation estimation method based on moving regularized least squares optimization with thin-plate spline (MRLS-TPS) for non-rigid image deformation. The method takes the user-controlled point-offset-vectors as the input data, and estimates the spatial transformation about the two control point sets for each pixel. To achieve a realistic deformation, we formulates the transformation estimation as a vector-field interpolation problem by a moving regularized least squares method.

icip_3286.pdf

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Authors:
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma
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15 September 2017 - 4:02am
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icip_3286.pdf

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[1] Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma, "Non-Rigid Image Deformation Algorithm Based on MRLS-TPS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2095. Accessed: Apr. 20, 2018.
@article{2095-17,
url = {http://sigport.org/2095},
author = {Huabing Zhou; Yuyu Kuang; Zhenghong Yu; Shiqiang Ren; Anna Dai; Yanduo Zhang; Tao Lu; Jiayi Ma },
publisher = {IEEE SigPort},
title = {Non-Rigid Image Deformation Algorithm Based on MRLS-TPS},
year = {2017} }
TY - EJOUR
T1 - Non-Rigid Image Deformation Algorithm Based on MRLS-TPS
AU - Huabing Zhou; Yuyu Kuang; Zhenghong Yu; Shiqiang Ren; Anna Dai; Yanduo Zhang; Tao Lu; Jiayi Ma
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2095
ER -
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma. (2017). Non-Rigid Image Deformation Algorithm Based on MRLS-TPS. IEEE SigPort. http://sigport.org/2095
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma, 2017. Non-Rigid Image Deformation Algorithm Based on MRLS-TPS. Available at: http://sigport.org/2095.
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma. (2017). "Non-Rigid Image Deformation Algorithm Based on MRLS-TPS." Web.
1. Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma. Non-Rigid Image Deformation Algorithm Based on MRLS-TPS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2095

Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system

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Authors:
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang
Submitted On:
15 September 2017 - 3:59am
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Report_new.pdf

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[1] Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang, "Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2094. Accessed: Apr. 20, 2018.
@article{2094-17,
url = {http://sigport.org/2094},
author = {Lin Zhang; Ying Fu; Yinqiang Zheng; Hua Huang },
publisher = {IEEE SigPort},
title = {Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system},
year = {2017} }
TY - EJOUR
T1 - Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system
AU - Lin Zhang; Ying Fu; Yinqiang Zheng; Hua Huang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2094
ER -
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang. (2017). Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system. IEEE SigPort. http://sigport.org/2094
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang, 2017. Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system. Available at: http://sigport.org/2094.
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang. (2017). "Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system." Web.
1. Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang. Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2094

MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING


Visual speech recognition (VSR), also known as lip reading is a task that recognizes words or phrases using video clips of lip movement. Traditional VSR methods are limited in that they are based mostly on VSR of frontal-view facial movement. However, for practical application, VSR should include lip movement from all angles. In this paper, we propose a pose-invariant network which can recognize words spoken from any arbitrary view input.

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Authors:
HouJeung Han , Sunghun Kang and Chang D. Yoo
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15 September 2017 - 3:48am
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poster

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[1] HouJeung Han , Sunghun Kang and Chang D. Yoo, "MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2092. Accessed: Apr. 20, 2018.
@article{2092-17,
url = {http://sigport.org/2092},
author = {HouJeung Han ; Sunghun Kang and Chang D. Yoo },
publisher = {IEEE SigPort},
title = {MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING},
year = {2017} }
TY - EJOUR
T1 - MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING
AU - HouJeung Han ; Sunghun Kang and Chang D. Yoo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2092
ER -
HouJeung Han , Sunghun Kang and Chang D. Yoo. (2017). MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING. IEEE SigPort. http://sigport.org/2092
HouJeung Han , Sunghun Kang and Chang D. Yoo, 2017. MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING. Available at: http://sigport.org/2092.
HouJeung Han , Sunghun Kang and Chang D. Yoo. (2017). "MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING." Web.
1. HouJeung Han , Sunghun Kang and Chang D. Yoo. MULTI-VIEW VISUAL SPEECH RECOGNITION BASED ON MULTI TASK LEARNING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2092

LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK

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Authors:
YuehuanWang, Xiaoyun Yan
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15 September 2017 - 3:46am
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LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK .pdf

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[1] YuehuanWang, Xiaoyun Yan, "LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2089. Accessed: Apr. 20, 2018.
@article{2089-17,
url = {http://sigport.org/2089},
author = {YuehuanWang; Xiaoyun Yan },
publisher = {IEEE SigPort},
title = {LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK},
year = {2017} }
TY - EJOUR
T1 - LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK
AU - YuehuanWang; Xiaoyun Yan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2089
ER -
YuehuanWang, Xiaoyun Yan. (2017). LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK. IEEE SigPort. http://sigport.org/2089
YuehuanWang, Xiaoyun Yan, 2017. LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK. Available at: http://sigport.org/2089.
YuehuanWang, Xiaoyun Yan. (2017). "LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK." Web.
1. YuehuanWang, Xiaoyun Yan. LONG-TERM OBJECT TRACKING BASED ON SIAMESE NETWORK [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2089

stereo-plus-depth imaging system

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Authors:
Cheolkon Jung, Joongkyu Kim
Submitted On:
15 September 2017 - 3:41am
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ICIP2017

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[1] Cheolkon Jung, Joongkyu Kim, "stereo-plus-depth imaging system", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2088. Accessed: Apr. 20, 2018.
@article{2088-17,
url = {http://sigport.org/2088},
author = {Cheolkon Jung; Joongkyu Kim },
publisher = {IEEE SigPort},
title = {stereo-plus-depth imaging system},
year = {2017} }
TY - EJOUR
T1 - stereo-plus-depth imaging system
AU - Cheolkon Jung; Joongkyu Kim
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2088
ER -
Cheolkon Jung, Joongkyu Kim. (2017). stereo-plus-depth imaging system. IEEE SigPort. http://sigport.org/2088
Cheolkon Jung, Joongkyu Kim, 2017. stereo-plus-depth imaging system. Available at: http://sigport.org/2088.
Cheolkon Jung, Joongkyu Kim. (2017). "stereo-plus-depth imaging system." Web.
1. Cheolkon Jung, Joongkyu Kim. stereo-plus-depth imaging system [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2088

ICIP2017_Incremental zero-shot learning based on attributes for image classification


Instead of assuming a closed-world environment comprising a fixed number of objects, modern pattern recognition systems need to recognize outliers, identify anomalies, or discover entirely new objects, which is known as zero-shot object recognition. However, many existing zero-shot learning methods are not efficient enough to incrementally update themselves with new samples mixed with known or novel class labels. In this paper, we propose an incremental zero-shot learning framework (IIAP/QR) based on indirect-attribute-prediction (IAP) model. Firstly, a fast incremental

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Authors:
Nan Xue, Yi Wang, Xin Fan, Maomao Min
Submitted On:
15 September 2017 - 3:07am
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ICIP2017 conference slide of paper 1688

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[1] Nan Xue, Yi Wang, Xin Fan, Maomao Min, "ICIP2017_Incremental zero-shot learning based on attributes for image classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2087. Accessed: Apr. 20, 2018.
@article{2087-17,
url = {http://sigport.org/2087},
author = {Nan Xue; Yi Wang; Xin Fan; Maomao Min },
publisher = {IEEE SigPort},
title = {ICIP2017_Incremental zero-shot learning based on attributes for image classification},
year = {2017} }
TY - EJOUR
T1 - ICIP2017_Incremental zero-shot learning based on attributes for image classification
AU - Nan Xue; Yi Wang; Xin Fan; Maomao Min
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2087
ER -
Nan Xue, Yi Wang, Xin Fan, Maomao Min. (2017). ICIP2017_Incremental zero-shot learning based on attributes for image classification. IEEE SigPort. http://sigport.org/2087
Nan Xue, Yi Wang, Xin Fan, Maomao Min, 2017. ICIP2017_Incremental zero-shot learning based on attributes for image classification. Available at: http://sigport.org/2087.
Nan Xue, Yi Wang, Xin Fan, Maomao Min. (2017). "ICIP2017_Incremental zero-shot learning based on attributes for image classification." Web.
1. Nan Xue, Yi Wang, Xin Fan, Maomao Min. ICIP2017_Incremental zero-shot learning based on attributes for image classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2087

ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION

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Authors:
Huixu Dong, Dilip K. Prasad, I-Ming Chen
Submitted On:
15 September 2017 - 2:04am
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ICIP2017-ICIP1701

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[1] Huixu Dong, Dilip K. Prasad, I-Ming Chen, " ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2086. Accessed: Apr. 20, 2018.
@article{2086-17,
url = {http://sigport.org/2086},
author = {Huixu Dong; Dilip K. Prasad; I-Ming Chen },
publisher = {IEEE SigPort},
title = { ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION },
year = {2017} }
TY - EJOUR
T1 - ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION
AU - Huixu Dong; Dilip K. Prasad; I-Ming Chen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2086
ER -
Huixu Dong, Dilip K. Prasad, I-Ming Chen. (2017). ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION . IEEE SigPort. http://sigport.org/2086
Huixu Dong, Dilip K. Prasad, I-Ming Chen, 2017. ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION . Available at: http://sigport.org/2086.
Huixu Dong, Dilip K. Prasad, I-Ming Chen. (2017). " ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION ." Web.
1. Huixu Dong, Dilip K. Prasad, I-Ming Chen. ICIP 2017-ROBUST ELLIPSE DETECTION VIA ARC SEGMENTATION AND CLASSIFICATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2086

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