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Machine Learning for Signal Processing

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.

Paper Details

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: Jul. 27, 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

DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING


Imaging techniques involve counting of photons striking a detector.
Due to fluctuations in the counting process, the measured
photon counts are known to be corrupted by Poisson
noise. In this paper, we propose a blind dictionary learning
framework for the reconstruction of photographic image data
from Poisson corrupted measurements acquired by a compressive
camera. We exploit the inherent non-negativity of the
data by modeling the dictionary as well as the sparse dictionary
coefficients as non-negative entities, and infer these directly

Paper Details

Authors:
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade
Submitted On:
19 March 2016 - 4:43am
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ICASSP_poster.pdf

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[1] Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade, "DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/791. Accessed: Jul. 27, 2017.
@article{791-16,
url = {http://sigport.org/791},
author = {Sukanya Patil; Rajbabu Velmurugan; Ajit Rajwade },
publisher = {IEEE SigPort},
title = {DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING},
year = {2016} }
TY - EJOUR
T1 - DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING
AU - Sukanya Patil; Rajbabu Velmurugan; Ajit Rajwade
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/791
ER -
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade. (2016). DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING. IEEE SigPort. http://sigport.org/791
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade, 2016. DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING. Available at: http://sigport.org/791.
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade. (2016). "DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING." Web.
1. Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade. DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/791

Stable and Symmetric Filter Convolutional Neural Network

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Authors:
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do
Submitted On:
18 March 2016 - 10:27pm
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icassp2016.pdf

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[1] Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do, "Stable and Symmetric Filter Convolutional Neural Network", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/781. Accessed: Jul. 27, 2017.
@article{781-16,
url = {http://sigport.org/781},
author = {Raymond Yeh; Mark Hasegawa-Johnson; Minh N. Do },
publisher = {IEEE SigPort},
title = {Stable and Symmetric Filter Convolutional Neural Network},
year = {2016} }
TY - EJOUR
T1 - Stable and Symmetric Filter Convolutional Neural Network
AU - Raymond Yeh; Mark Hasegawa-Johnson; Minh N. Do
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/781
ER -
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do. (2016). Stable and Symmetric Filter Convolutional Neural Network. IEEE SigPort. http://sigport.org/781
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do, 2016. Stable and Symmetric Filter Convolutional Neural Network. Available at: http://sigport.org/781.
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do. (2016). "Stable and Symmetric Filter Convolutional Neural Network." Web.
1. Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do. Stable and Symmetric Filter Convolutional Neural Network [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/781

Deep Learning: Propelling Recent Rapid Advances in AI

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23 February 2016 - 1:44pm
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GlobalSIP-Plenary-LiDeng-Dec16-2015 - reduced.pptx

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[1] , "Deep Learning: Propelling Recent Rapid Advances in AI", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/600. Accessed: Jul. 27, 2017.
@article{600-16,
url = {http://sigport.org/600},
author = { },
publisher = {IEEE SigPort},
title = {Deep Learning: Propelling Recent Rapid Advances in AI},
year = {2016} }
TY - EJOUR
T1 - Deep Learning: Propelling Recent Rapid Advances in AI
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/600
ER -
. (2016). Deep Learning: Propelling Recent Rapid Advances in AI. IEEE SigPort. http://sigport.org/600
, 2016. Deep Learning: Propelling Recent Rapid Advances in AI. Available at: http://sigport.org/600.
. (2016). "Deep Learning: Propelling Recent Rapid Advances in AI." Web.
1. . Deep Learning: Propelling Recent Rapid Advances in AI [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/600

A feasibility study of automated plug-load identification from high-frequency measurements


Plug-meters benefit many grid and building-level energy management applications like automated load control and load scheduling. However, installing and maintaining large and/orlong term deployments of such meters requires assignment and updating of the identity (labels) of electrical loads connected to them. Although the literature on electricity disaggregation and appliance identification is extensive, there is no consensus on the generalizability of the proposed solutions, especially with respect to the features that are extracted from voltage and current measurements.

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Authors:
Emre Can Kara, Suman Giri, Mario Berges
Submitted On:
23 February 2016 - 1:44pm
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A feasibility study of automated plug-load identification from high-frequency measurements.pdf

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[1] Emre Can Kara, Suman Giri, Mario Berges, "A feasibility study of automated plug-load identification from high-frequency measurements", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/334. Accessed: Jul. 27, 2017.
@article{334-15,
url = {http://sigport.org/334},
author = {Emre Can Kara; Suman Giri; Mario Berges },
publisher = {IEEE SigPort},
title = {A feasibility study of automated plug-load identification from high-frequency measurements},
year = {2015} }
TY - EJOUR
T1 - A feasibility study of automated plug-load identification from high-frequency measurements
AU - Emre Can Kara; Suman Giri; Mario Berges
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/334
ER -
Emre Can Kara, Suman Giri, Mario Berges. (2015). A feasibility study of automated plug-load identification from high-frequency measurements. IEEE SigPort. http://sigport.org/334
Emre Can Kara, Suman Giri, Mario Berges, 2015. A feasibility study of automated plug-load identification from high-frequency measurements. Available at: http://sigport.org/334.
Emre Can Kara, Suman Giri, Mario Berges. (2015). "A feasibility study of automated plug-load identification from high-frequency measurements." Web.
1. Emre Can Kara, Suman Giri, Mario Berges. A feasibility study of automated plug-load identification from high-frequency measurements [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/334

Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation


Greedy pursuit is one of the standard approaches for sparse approximation. Since the derivation of the conventional greedy pursuit schemes, including matching pursuit (MP) and orthogonal MP (OMP), is based on the inner product space, they may not work properly in the presence of impulsive noise. In this work, we devise a new definition of correlation in lp-space with p>0, called lp-correlation, and introduce the concept of orthogonality in lp-space.

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

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[1] , "Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/236. Accessed: Jul. 27, 2017.
@article{236-15,
url = {http://sigport.org/236},
author = { },
publisher = {IEEE SigPort},
title = {Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation},
year = {2015} }
TY - EJOUR
T1 - Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/236
ER -
. (2015). Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation. IEEE SigPort. http://sigport.org/236
, 2015. Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation. Available at: http://sigport.org/236.
. (2015). "Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation." Web.
1. . Outlier-Robust Greedy Pursuit Algorithms in lp-Space for Sparse Approximation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/236

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

Authors:
Submitted On:
23 February 2016 - 1:43pm
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Type:

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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: Jul. 27, 2017.
@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

Object Tracking and Person Re-Identification on Manifolds


person re-identification examples

Slides from the Tutorial on Riemannian Geometry in Computer Vision, presented at the Asian Conference on Computer Vision (ACCV), Singapore, 2014.

The slides show (1) how objects can be interpreted as points on Riemannian and Grassmann manifolds, and (2) various distance measures on manifolds. Demonstrates usefulness of manifold techniques in applications such as object tracking and person re-identification.

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Submitted On:
23 February 2016 - 1:43pm
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sanderson_riemannian_geometry_tutorial_slides_accv_2014.pdf

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[1] , "Object Tracking and Person Re-Identification on Manifolds", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/198. Accessed: Jul. 27, 2017.
@article{198-15,
url = {http://sigport.org/198},
author = { },
publisher = {IEEE SigPort},
title = {Object Tracking and Person Re-Identification on Manifolds},
year = {2015} }
TY - EJOUR
T1 - Object Tracking and Person Re-Identification on Manifolds
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/198
ER -
. (2015). Object Tracking and Person Re-Identification on Manifolds. IEEE SigPort. http://sigport.org/198
, 2015. Object Tracking and Person Re-Identification on Manifolds. Available at: http://sigport.org/198.
. (2015). "Object Tracking and Person Re-Identification on Manifolds." Web.
1. . Object Tracking and Person Re-Identification on Manifolds [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/198

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