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Pattern recognition and classification (MLR-PATT)

Partial Face Recognition: A Sparse Representation-based Approach


Partial face recognition is a problem that often arises in practical settings and applications. We propose a sparse representation-based algorithm for this problem. Our method firstly trains a dictionary and the classifier parameters in a supervised dictionary learning framework and then aligns the partially observed test image and seeks for the sparse representation with respect to the training data alternatively to obtain its label. We also analyze the performance limit of sparse representation-based classification algorithms on partial observations.

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Authors:
Luoluo Liu, Trac D. Tran, Sang "Peter" Chin
Submitted On:
25 March 2016 - 10:26pm
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poster_icassp16_luoluo_feb_version_2_33_56.pdf

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[1] Luoluo Liu, Trac D. Tran, Sang "Peter" Chin, "Partial Face Recognition: A Sparse Representation-based Approach", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1043. Accessed: Apr. 24, 2019.
@article{1043-16,
url = {http://sigport.org/1043},
author = {Luoluo Liu; Trac D. Tran; Sang "Peter" Chin },
publisher = {IEEE SigPort},
title = {Partial Face Recognition: A Sparse Representation-based Approach},
year = {2016} }
TY - EJOUR
T1 - Partial Face Recognition: A Sparse Representation-based Approach
AU - Luoluo Liu; Trac D. Tran; Sang "Peter" Chin
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1043
ER -
Luoluo Liu, Trac D. Tran, Sang "Peter" Chin. (2016). Partial Face Recognition: A Sparse Representation-based Approach. IEEE SigPort. http://sigport.org/1043
Luoluo Liu, Trac D. Tran, Sang "Peter" Chin, 2016. Partial Face Recognition: A Sparse Representation-based Approach. Available at: http://sigport.org/1043.
Luoluo Liu, Trac D. Tran, Sang "Peter" Chin. (2016). "Partial Face Recognition: A Sparse Representation-based Approach." Web.
1. Luoluo Liu, Trac D. Tran, Sang "Peter" Chin. Partial Face Recognition: A Sparse Representation-based Approach [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1043

Lecture ICASSP 2016 Pierre Laffitte


This presentation introduces a Deep Learning model that performs classification of the Audio Scene in the subway environment. The final goal is to detect Screams and Shouts for surveillance purposes. The model is a combination of Deep Belief Network and Deep Neural Network, (generatively pre-trained within the DBN framework and fine-tuned discriminatively within the DNN framework), and is trained on a novel database of pseudo-real signals collected in the Paris metro.

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Authors:
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu
Submitted On:
23 March 2016 - 10:01am
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ICASSP Lecture.pdf

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[1] Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu, "Lecture ICASSP 2016 Pierre Laffitte", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/991. Accessed: Apr. 24, 2019.
@article{991-16,
url = {http://sigport.org/991},
author = {Pierre Laffitte; David Sodoyer; Laurent Girin; Charles Tatkeu },
publisher = {IEEE SigPort},
title = {Lecture ICASSP 2016 Pierre Laffitte},
year = {2016} }
TY - EJOUR
T1 - Lecture ICASSP 2016 Pierre Laffitte
AU - Pierre Laffitte; David Sodoyer; Laurent Girin; Charles Tatkeu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/991
ER -
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu. (2016). Lecture ICASSP 2016 Pierre Laffitte. IEEE SigPort. http://sigport.org/991
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu, 2016. Lecture ICASSP 2016 Pierre Laffitte. Available at: http://sigport.org/991.
Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu. (2016). "Lecture ICASSP 2016 Pierre Laffitte." Web.
1. Pierre Laffitte, David Sodoyer, Laurent Girin, Charles Tatkeu. Lecture ICASSP 2016 Pierre Laffitte [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/991

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.

[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: Apr. 24, 2019.
@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

A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE


Recently, sparse representation based classification has been widely used in pattern recognition. Most of existing methods exploit the recovered representation coefficients to reconstruct the inputs, and the classwise reconstruction errors are used to identify the class of the sample based on the subspace assumption. Different from the reconstruction pipeline, an assignment framework is built on the representation coefficients in this paper.

Paper Details

Authors:
Ding Ni, Hongbing Ma
Submitted On:
12 March 2016 - 9:09pm
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ICASSP2016_Ding Ni_poster.pdf

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[1] Ding Ni, Hongbing Ma, "A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/648. Accessed: Apr. 24, 2019.
@article{648-16,
url = {http://sigport.org/648},
author = {Ding Ni; Hongbing Ma },
publisher = {IEEE SigPort},
title = {A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE},
year = {2016} }
TY - EJOUR
T1 - A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE
AU - Ding Ni; Hongbing Ma
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/648
ER -
Ding Ni, Hongbing Ma. (2016). A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE. IEEE SigPort. http://sigport.org/648
Ding Ni, Hongbing Ma, 2016. A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE. Available at: http://sigport.org/648.
Ding Ni, Hongbing Ma. (2016). "A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE." Web.
1. Ding Ni, Hongbing Ma. A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/648

EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS

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Authors:
Alexander R. Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover
Submitted On:
12 March 2016 - 8:31am
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ICASSP2016Poster_CNN.pdf

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[1] Alexander R. Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover, "EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/639. Accessed: Apr. 24, 2019.
@article{639-16,
url = {http://sigport.org/639},
author = {Alexander R. Johansen; Jing Jin; Tomasz Maszczyk; Justin Dauwels; Sydney S. Cash; M. Brandon Westover },
publisher = {IEEE SigPort},
title = {EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS},
year = {2016} }
TY - EJOUR
T1 - EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS
AU - Alexander R. Johansen; Jing Jin; Tomasz Maszczyk; Justin Dauwels; Sydney S. Cash; M. Brandon Westover
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/639
ER -
Alexander R. Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover. (2016). EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/639
Alexander R. Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover, 2016. EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/639.
Alexander R. Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover. (2016). "EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Alexander R. Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover. EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/639

Characterization and Classification of Sonar Targets Using Ellipsoid Features


We present a geometry-inspired characterization of
target response for active sonar that exploits similarity between
intra-class features to distinguish between different targets
against environmental objects such as a rock. Key innovation is to
represent feature manifolds as a set of ellipsoids, each of which
geometrically encompasses a unique physical characteristic of
the target’s response. We have demonstrated over experimental
field data that for a given target class, these feature ellipsoids

Paper Details

Authors:
Ananya Sen Gupta, Ivars Kirsteins
Submitted On:
23 February 2016 - 1:44pm
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globalSIP_slides_final3.pdf

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[1] Ananya Sen Gupta, Ivars Kirsteins, "Characterization and Classification of Sonar Targets Using Ellipsoid Features", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/507. Accessed: Apr. 24, 2019.
@article{507-15,
url = {http://sigport.org/507},
author = {Ananya Sen Gupta; Ivars Kirsteins },
publisher = {IEEE SigPort},
title = {Characterization and Classification of Sonar Targets Using Ellipsoid Features},
year = {2015} }
TY - EJOUR
T1 - Characterization and Classification of Sonar Targets Using Ellipsoid Features
AU - Ananya Sen Gupta; Ivars Kirsteins
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/507
ER -
Ananya Sen Gupta, Ivars Kirsteins. (2015). Characterization and Classification of Sonar Targets Using Ellipsoid Features. IEEE SigPort. http://sigport.org/507
Ananya Sen Gupta, Ivars Kirsteins, 2015. Characterization and Classification of Sonar Targets Using Ellipsoid Features. Available at: http://sigport.org/507.
Ananya Sen Gupta, Ivars Kirsteins. (2015). "Characterization and Classification of Sonar Targets Using Ellipsoid Features." Web.
1. Ananya Sen Gupta, Ivars Kirsteins. Characterization and Classification of Sonar Targets Using Ellipsoid Features [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/507

Kernel-based low-rank feature extraction on a budget for Big data streams

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Authors:
Dimitrios Berberidis, Georgios B. Giannakis
Submitted On:
23 February 2016 - 1:44pm
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Globalsip2015.pdf

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[1] Dimitrios Berberidis, Georgios B. Giannakis, "Kernel-based low-rank feature extraction on a budget for Big data streams", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/434. Accessed: Apr. 24, 2019.
@article{434-15,
url = {http://sigport.org/434},
author = {Dimitrios Berberidis; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Kernel-based low-rank feature extraction on a budget for Big data streams},
year = {2015} }
TY - EJOUR
T1 - Kernel-based low-rank feature extraction on a budget for Big data streams
AU - Dimitrios Berberidis; Georgios B. Giannakis
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/434
ER -
Dimitrios Berberidis, Georgios B. Giannakis. (2015). Kernel-based low-rank feature extraction on a budget for Big data streams. IEEE SigPort. http://sigport.org/434
Dimitrios Berberidis, Georgios B. Giannakis, 2015. Kernel-based low-rank feature extraction on a budget for Big data streams. Available at: http://sigport.org/434.
Dimitrios Berberidis, Georgios B. Giannakis. (2015). "Kernel-based low-rank feature extraction on a budget for Big data streams." Web.
1. Dimitrios Berberidis, Georgios B. Giannakis. Kernel-based low-rank feature extraction on a budget for Big data streams [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/434

3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning

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Authors:
Faisal R. Al-Osaimi, Nizar Bouguila, Ji-Xiang Du
Submitted On:
23 February 2016 - 1:44pm
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[1] Faisal R. Al-Osaimi, Nizar Bouguila, Ji-Xiang Du, "3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/260. Accessed: Apr. 24, 2019.
@article{260-15,
url = {http://sigport.org/260},
author = {Faisal R. Al-Osaimi; Nizar Bouguila; Ji-Xiang Du },
publisher = {IEEE SigPort},
title = {3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning},
year = {2015} }
TY - EJOUR
T1 - 3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning
AU - Faisal R. Al-Osaimi; Nizar Bouguila; Ji-Xiang Du
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/260
ER -
Faisal R. Al-Osaimi, Nizar Bouguila, Ji-Xiang Du. (2015). 3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning. IEEE SigPort. http://sigport.org/260
Faisal R. Al-Osaimi, Nizar Bouguila, Ji-Xiang Du, 2015. 3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning. Available at: http://sigport.org/260.
Faisal R. Al-Osaimi, Nizar Bouguila, Ji-Xiang Du. (2015). "3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning." Web.
1. Faisal R. Al-Osaimi, Nizar Bouguila, Ji-Xiang Du. 3D Object Modeling and Recognition via Online Hierarchical Pitman-Yor Process Mixture Learning [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/260

CRH: A Simple Benchmark Approach to Continuous Hashing

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Submitted On:
23 February 2016 - 1:43pm
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gsip_mc.pdf

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[1] , "CRH: A Simple Benchmark Approach to Continuous Hashing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/248. Accessed: Apr. 24, 2019.
@article{248-15,
url = {http://sigport.org/248},
author = { },
publisher = {IEEE SigPort},
title = {CRH: A Simple Benchmark Approach to Continuous Hashing},
year = {2015} }
TY - EJOUR
T1 - CRH: A Simple Benchmark Approach to Continuous Hashing
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/248
ER -
. (2015). CRH: A Simple Benchmark Approach to Continuous Hashing. IEEE SigPort. http://sigport.org/248
, 2015. CRH: A Simple Benchmark Approach to Continuous Hashing. Available at: http://sigport.org/248.
. (2015). "CRH: A Simple Benchmark Approach to Continuous Hashing." Web.
1. . CRH: A Simple Benchmark Approach to Continuous Hashing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/248

Computer Vision and Image Processing for Automated Surveillance


Presentation slides covering:

- robust foreground detection / background subtraction via patch-based analysis
- person re-identification based on representations on Riemannian manifolds
- robust object tracking via Grassmann manifolds
- adapting the lessons from big data to computer vision
- future paradigm shifts: computer vision based on networks of neurosynaptic cores

Paper Details

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Submitted On:
23 February 2016 - 1:43pm
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Sanderson_Computer_Vision_and_Image_Processing_for_Automated_Surveillance.pdf

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[1] , "Computer Vision and Image Processing for Automated Surveillance", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/202. Accessed: Apr. 24, 2019.
@article{202-15,
url = {http://sigport.org/202},
author = { },
publisher = {IEEE SigPort},
title = {Computer Vision and Image Processing for Automated Surveillance},
year = {2015} }
TY - EJOUR
T1 - Computer Vision and Image Processing for Automated Surveillance
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/202
ER -
. (2015). Computer Vision and Image Processing for Automated Surveillance. IEEE SigPort. http://sigport.org/202
, 2015. Computer Vision and Image Processing for Automated Surveillance. Available at: http://sigport.org/202.
. (2015). "Computer Vision and Image Processing for Automated Surveillance." Web.
1. . Computer Vision and Image Processing for Automated Surveillance [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/202

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