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Computer Vision

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. 17, 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

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.

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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. 17, 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

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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Authors:
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi
Submitted On:
16 July 2016 - 11:13pm
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DCA_ICASSP16_Poster.pdf

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[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Dec. 17, 2017.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828