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Pattern recognition and classification (MLR-PATT)

A Deep Learning Network for Vision-based Vacant Parking Space Detection System


In the demonstration, we would show our live and real-time parking space detection system. The detection function is founded on a video surveillance system built in an outdoor parking lot. As we might know, it is challenging to implement a practical vision system in an outdoor environment owing to the dramatic lighting changes and uncontrollable variations from weather conditions.

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Authors:
Ching-Chun Huang
Submitted On:
8 September 2017 - 5:23am
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Poster_Hoang_v6.pdf

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[1] Ching-Chun Huang, "A Deep Learning Network for Vision-based Vacant Parking Space Detection System", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1875. Accessed: Jul. 22, 2019.
@article{1875-17,
url = {http://sigport.org/1875},
author = {Ching-Chun Huang },
publisher = {IEEE SigPort},
title = {A Deep Learning Network for Vision-based Vacant Parking Space Detection System},
year = {2017} }
TY - EJOUR
T1 - A Deep Learning Network for Vision-based Vacant Parking Space Detection System
AU - Ching-Chun Huang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1875
ER -
Ching-Chun Huang. (2017). A Deep Learning Network for Vision-based Vacant Parking Space Detection System. IEEE SigPort. http://sigport.org/1875
Ching-Chun Huang, 2017. A Deep Learning Network for Vision-based Vacant Parking Space Detection System. Available at: http://sigport.org/1875.
Ching-Chun Huang. (2017). "A Deep Learning Network for Vision-based Vacant Parking Space Detection System." Web.
1. Ching-Chun Huang. A Deep Learning Network for Vision-based Vacant Parking Space Detection System [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1875

Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform


Vacant parking space detection is a challenging vision task due to outdoor lighting variation and perspective distortion. Previous methods found on camera geometry and projection matrix to select space image region for status classification. By utilizing suitable hand-crafted features, outdoor lighting variation and perspective distortion could be well handled. However, if also considering parking displacement, non-unified car size, and inter-object occlusion, we find the problem becomes more troublesome.

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Authors:
Ching-Chun Huang
Submitted On:
8 September 2017 - 5:24am
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Parking-Space-Detection-Based-on-A-Multi-task-Deep-Convolutional-Network-with-Spatial-Transform.pdf

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[1] Ching-Chun Huang, "Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1874. Accessed: Jul. 22, 2019.
@article{1874-17,
url = {http://sigport.org/1874},
author = {Ching-Chun Huang },
publisher = {IEEE SigPort},
title = {Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform},
year = {2017} }
TY - EJOUR
T1 - Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform
AU - Ching-Chun Huang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1874
ER -
Ching-Chun Huang. (2017). Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform. IEEE SigPort. http://sigport.org/1874
Ching-Chun Huang, 2017. Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform. Available at: http://sigport.org/1874.
Ching-Chun Huang. (2017). "Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform." Web.
1. Ching-Chun Huang. Parking Space Detection Based on A Multi-task Deep Convolutional Network with Spatial Transform [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1874

Human-human interaction recognition based on spatial and motion trend feature

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Authors:
Bangli Liu, Haibin Cai, Xiaofei Ji, Honghai Liu
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3 September 2017 - 5:35am
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PaperID2688_ICIP2017

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[1] Bangli Liu, Haibin Cai, Xiaofei Ji, Honghai Liu, "Human-human interaction recognition based on spatial and motion trend feature ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1822. Accessed: Jul. 22, 2019.
@article{1822-17,
url = {http://sigport.org/1822},
author = {Bangli Liu; Haibin Cai; Xiaofei Ji; Honghai Liu },
publisher = {IEEE SigPort},
title = {Human-human interaction recognition based on spatial and motion trend feature },
year = {2017} }
TY - EJOUR
T1 - Human-human interaction recognition based on spatial and motion trend feature
AU - Bangli Liu; Haibin Cai; Xiaofei Ji; Honghai Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1822
ER -
Bangli Liu, Haibin Cai, Xiaofei Ji, Honghai Liu. (2017). Human-human interaction recognition based on spatial and motion trend feature . IEEE SigPort. http://sigport.org/1822
Bangli Liu, Haibin Cai, Xiaofei Ji, Honghai Liu, 2017. Human-human interaction recognition based on spatial and motion trend feature . Available at: http://sigport.org/1822.
Bangli Liu, Haibin Cai, Xiaofei Ji, Honghai Liu. (2017). "Human-human interaction recognition based on spatial and motion trend feature ." Web.
1. Bangli Liu, Haibin Cai, Xiaofei Ji, Honghai Liu. Human-human interaction recognition based on spatial and motion trend feature [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1822

NONLINEAR SUBSPACE CLUSTERING


This paper presents a nonlinear subspace clustering (NSC) method for image clustering. Unlike most existing subspace clustering methods which only exploit the linear relationship of samples to learn the affine matrix, our NSC reveals the multi-cluster nonlinear structure of samples via a nonlinear neural network. While kernel-based clustering methods can also address the nonlinear issue of samples, this type of methods suffers from the scalability issue.

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Authors:
Wencheng Zhu, Jiwen Lu, Jie Zhou
Submitted On:
5 September 2017 - 7:59am
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NONLINEAR SUBSPACE CLUSTERING.pdf

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[1] Wencheng Zhu, Jiwen Lu, Jie Zhou, "NONLINEAR SUBSPACE CLUSTERING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1811. Accessed: Jul. 22, 2019.
@article{1811-17,
url = {http://sigport.org/1811},
author = {Wencheng Zhu; Jiwen Lu; Jie Zhou },
publisher = {IEEE SigPort},
title = {NONLINEAR SUBSPACE CLUSTERING},
year = {2017} }
TY - EJOUR
T1 - NONLINEAR SUBSPACE CLUSTERING
AU - Wencheng Zhu; Jiwen Lu; Jie Zhou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1811
ER -
Wencheng Zhu, Jiwen Lu, Jie Zhou. (2017). NONLINEAR SUBSPACE CLUSTERING. IEEE SigPort. http://sigport.org/1811
Wencheng Zhu, Jiwen Lu, Jie Zhou, 2017. NONLINEAR SUBSPACE CLUSTERING. Available at: http://sigport.org/1811.
Wencheng Zhu, Jiwen Lu, Jie Zhou. (2017). "NONLINEAR SUBSPACE CLUSTERING." Web.
1. Wencheng Zhu, Jiwen Lu, Jie Zhou. NONLINEAR SUBSPACE CLUSTERING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1811

HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION


Classification of plants based on a multi-organ approach is very challenging. Although additional data provides more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Existing approaches focus mainly on generic features for species classification, disregarding the features representing the organs. In fact, plants are complex entities sustained by a number of organ systems.

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Authors:
Yang Loong Chang, Chee Seng Chan, Paolo Remagnino
Submitted On:
23 August 2017 - 1:50am
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HGO-CNN.pdf

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[1] Yang Loong Chang, Chee Seng Chan, Paolo Remagnino, "HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1804. Accessed: Jul. 22, 2019.
@article{1804-17,
url = {http://sigport.org/1804},
author = {Yang Loong Chang; Chee Seng Chan; Paolo Remagnino },
publisher = {IEEE SigPort},
title = {HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION},
year = {2017} }
TY - EJOUR
T1 - HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION
AU - Yang Loong Chang; Chee Seng Chan; Paolo Remagnino
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1804
ER -
Yang Loong Chang, Chee Seng Chan, Paolo Remagnino. (2017). HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION. IEEE SigPort. http://sigport.org/1804
Yang Loong Chang, Chee Seng Chan, Paolo Remagnino, 2017. HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION. Available at: http://sigport.org/1804.
Yang Loong Chang, Chee Seng Chan, Paolo Remagnino. (2017). "HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION." Web.
1. Yang Loong Chang, Chee Seng Chan, Paolo Remagnino. HGO-CNN: HYBRID GENERIC-ORGAN CONVOLUTIONAL NEURAL NETWORK FOR MULTI-ORGAN PLANT CLASSIFICATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1804

RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM


This article proposes a performance analysis of kernel least squares support vector machines (LS-SVMs) based on a random matrix approach, in the regime where both the dimension of data p and their number n grow large at the same rate. Under a two-class Gaussian mixture model for the input data, we prove that the LS-SVM decision function is asymptotically normal with means and covariances shown to depend explicitly on the derivatives of the kernel function. This provides improved understanding along with new insights into the internal workings of SVM-type methods for large datasets.

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Authors:
Zhenyu Liao, Romain Couillet
Submitted On:
9 March 2017 - 6:36pm
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[1] Zhenyu Liao, Romain Couillet, "RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1731. Accessed: Jul. 22, 2019.
@article{1731-17,
url = {http://sigport.org/1731},
author = {Zhenyu Liao; Romain Couillet },
publisher = {IEEE SigPort},
title = {RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM},
year = {2017} }
TY - EJOUR
T1 - RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM
AU - Zhenyu Liao; Romain Couillet
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1731
ER -
Zhenyu Liao, Romain Couillet. (2017). RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM. IEEE SigPort. http://sigport.org/1731
Zhenyu Liao, Romain Couillet, 2017. RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM. Available at: http://sigport.org/1731.
Zhenyu Liao, Romain Couillet. (2017). "RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM." Web.
1. Zhenyu Liao, Romain Couillet. RANDOM MATRICES MEET MACHINE LEARNING: A LARGE DIMENSIONAL ANALYSIS OF LS-SVM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1731

End to end spoofing detection with raw wave CLDNNs

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6 March 2017 - 10:03am
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ICASSP_2017_E2ESpoof_Heinrich_Dinkel.pdf

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[1] , "End to end spoofing detection with raw wave CLDNNs", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1649. Accessed: Jul. 22, 2019.
@article{1649-17,
url = {http://sigport.org/1649},
author = { },
publisher = {IEEE SigPort},
title = {End to end spoofing detection with raw wave CLDNNs},
year = {2017} }
TY - EJOUR
T1 - End to end spoofing detection with raw wave CLDNNs
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1649
ER -
. (2017). End to end spoofing detection with raw wave CLDNNs. IEEE SigPort. http://sigport.org/1649
, 2017. End to end spoofing detection with raw wave CLDNNs. Available at: http://sigport.org/1649.
. (2017). "End to end spoofing detection with raw wave CLDNNs." Web.
1. . End to end spoofing detection with raw wave CLDNNs [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1649

Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network

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Authors:
Chao Wang, Jian Wang, Xudong Zhang
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3 March 2017 - 10:02am
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[1] Chao Wang, Jian Wang, Xudong Zhang, "Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1614. Accessed: Jul. 22, 2019.
@article{1614-17,
url = {http://sigport.org/1614},
author = {Chao Wang; Jian Wang; Xudong Zhang },
publisher = {IEEE SigPort},
title = {Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network
AU - Chao Wang; Jian Wang; Xudong Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1614
ER -
Chao Wang, Jian Wang, Xudong Zhang. (2017). Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network. IEEE SigPort. http://sigport.org/1614
Chao Wang, Jian Wang, Xudong Zhang, 2017. Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network. Available at: http://sigport.org/1614.
Chao Wang, Jian Wang, Xudong Zhang. (2017). "Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network." Web.
1. Chao Wang, Jian Wang, Xudong Zhang. Automatic Radar Waveform Recognition Based on Time-Frequency Analysis and Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1614

An Online Feature Selection Architecture For Human Activity Recognition


Human Activity Recognition (HAR) must currently face up to the challenge of rethinking analytics from the perspective of real-time operation, wherein biophysical sensing streams are efficiently intertwined at close vicinity to the point of sensing. As such, feature selection techniques, traditionally employed for off-line data processing, should be evaluated with respect to their ability to filter out redundant information in real-time.

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Authors:
Athanasia Panousopoulou, Panagiotis Tsakalides
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3 March 2017 - 9:28am
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[1] Athanasia Panousopoulou, Panagiotis Tsakalides, "An Online Feature Selection Architecture For Human Activity Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1613. Accessed: Jul. 22, 2019.
@article{1613-17,
url = {http://sigport.org/1613},
author = {Athanasia Panousopoulou; Panagiotis Tsakalides },
publisher = {IEEE SigPort},
title = {An Online Feature Selection Architecture For Human Activity Recognition},
year = {2017} }
TY - EJOUR
T1 - An Online Feature Selection Architecture For Human Activity Recognition
AU - Athanasia Panousopoulou; Panagiotis Tsakalides
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1613
ER -
Athanasia Panousopoulou, Panagiotis Tsakalides. (2017). An Online Feature Selection Architecture For Human Activity Recognition. IEEE SigPort. http://sigport.org/1613
Athanasia Panousopoulou, Panagiotis Tsakalides, 2017. An Online Feature Selection Architecture For Human Activity Recognition. Available at: http://sigport.org/1613.
Athanasia Panousopoulou, Panagiotis Tsakalides. (2017). "An Online Feature Selection Architecture For Human Activity Recognition." Web.
1. Athanasia Panousopoulou, Panagiotis Tsakalides. An Online Feature Selection Architecture For Human Activity Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1613

Part-Level Fully Convolutional Networks for Pedestrian Detection


Since pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, pedestrian detection is a challengeable task. In this paper, we propose part-level fully convolutional networks (FCN) for pedestrian detection. We adopt deep learning to deal with the proposal shifting problem in pedestrian detection. First, we combine convolutional neural networks (CNN) and FCN to align bounding boxes for pedestrians. Then, we perform part-level pedestrian detection based on CNN to recall the lost body parts.

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Authors:
Xinran Wang,Cheolkon Jung,Alfred O Hero
Submitted On:
8 March 2017 - 10:41pm
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[1] Xinran Wang,Cheolkon Jung,Alfred O Hero, "Part-Level Fully Convolutional Networks for Pedestrian Detection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1570. Accessed: Jul. 22, 2019.
@article{1570-17,
url = {http://sigport.org/1570},
author = {Xinran Wang;Cheolkon Jung;Alfred O Hero },
publisher = {IEEE SigPort},
title = {Part-Level Fully Convolutional Networks for Pedestrian Detection},
year = {2017} }
TY - EJOUR
T1 - Part-Level Fully Convolutional Networks for Pedestrian Detection
AU - Xinran Wang;Cheolkon Jung;Alfred O Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1570
ER -
Xinran Wang,Cheolkon Jung,Alfred O Hero. (2017). Part-Level Fully Convolutional Networks for Pedestrian Detection. IEEE SigPort. http://sigport.org/1570
Xinran Wang,Cheolkon Jung,Alfred O Hero, 2017. Part-Level Fully Convolutional Networks for Pedestrian Detection. Available at: http://sigport.org/1570.
Xinran Wang,Cheolkon Jung,Alfred O Hero. (2017). "Part-Level Fully Convolutional Networks for Pedestrian Detection." Web.
1. Xinran Wang,Cheolkon Jung,Alfred O Hero. Part-Level Fully Convolutional Networks for Pedestrian Detection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1570

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