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Image/Video Processing

TCLBP: An LBP-based Color Descriptor for Face Recognition


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27 February 2017 - 7:59pm
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poster.pdf

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[1] , "TCLBP: An LBP-based Color Descriptor for Face Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1453. Accessed: Jul. 16, 2018.
@article{1453-17,
url = {http://sigport.org/1453},
author = { },
publisher = {IEEE SigPort},
title = {TCLBP: An LBP-based Color Descriptor for Face Recognition},
year = {2017} }
TY - EJOUR
T1 - TCLBP: An LBP-based Color Descriptor for Face Recognition
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1453
ER -
. (2017). TCLBP: An LBP-based Color Descriptor for Face Recognition. IEEE SigPort. http://sigport.org/1453
, 2017. TCLBP: An LBP-based Color Descriptor for Face Recognition. Available at: http://sigport.org/1453.
. (2017). "TCLBP: An LBP-based Color Descriptor for Face Recognition." Web.
1. . TCLBP: An LBP-based Color Descriptor for Face Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1453

ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT

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9 December 2016 - 2:05am
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blueFade_24x48.pdf

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[1] , "ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1430. Accessed: Jul. 16, 2018.
@article{1430-16,
url = {http://sigport.org/1430},
author = { },
publisher = {IEEE SigPort},
title = {ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT},
year = {2016} }
TY - EJOUR
T1 - ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1430
ER -
. (2016). ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT. IEEE SigPort. http://sigport.org/1430
, 2016. ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT. Available at: http://sigport.org/1430.
. (2016). "ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT." Web.
1. . ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1430

THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING


In this paper, we propose a new characteristic measure for relative people density and motion dynamics for the purpose of long-term crowd monitoring. While many related works focus on direct people counting and absolute density estimation, we will show that relative densities provide reliable information on crowd behaviour. Furthermore, we will discuss the derivation of a so-called Congestion Level of local areas in the crowd, which takes the current dynamics and density within a certain image region into account.

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7 December 2016 - 8:42am
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THE CROWD CONGESTION LEVEL_Bek_Monari_2016.pdf

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[1] , "THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1403. Accessed: Jul. 16, 2018.
@article{1403-16,
url = {http://sigport.org/1403},
author = { },
publisher = {IEEE SigPort},
title = {THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING},
year = {2016} }
TY - EJOUR
T1 - THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1403
ER -
. (2016). THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING. IEEE SigPort. http://sigport.org/1403
, 2016. THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING. Available at: http://sigport.org/1403.
. (2016). "THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING." Web.
1. . THE CROWD CONGESTION LEVEL - A NEW MEASURE FOR RISK ASSESSMENT IN VIDEO-BASED CROWD MONITORING [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1403

GlobalSIP_Recover from Tracking Failure_KEHE


Numerous trackers have been proposed in recent years with considerable success. But few trackers can cope with all scenarios without failures. It is very difficult to design a tracker robust enough to keep off tracking failure. As failure is inevitable, we propose a framework to correct tracker, verify failure, predict object position and re-detect object. The original model of the first frame is used to correct the tracker. Then, the confidence of tracking is used to verify tracking failure.

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Authors:
Ke He, Ningning Li, Borui Mo, Bo Yang, Aidong Men
Submitted On:
6 December 2016 - 9:56pm
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GlobalSIP_Recovery from Tracking Failure_KEHE.pdf

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[1] Ke He, Ningning Li, Borui Mo, Bo Yang, Aidong Men, "GlobalSIP_Recover from Tracking Failure_KEHE", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1388. Accessed: Jul. 16, 2018.
@article{1388-16,
url = {http://sigport.org/1388},
author = {Ke He; Ningning Li; Borui Mo; Bo Yang; Aidong Men },
publisher = {IEEE SigPort},
title = {GlobalSIP_Recover from Tracking Failure_KEHE},
year = {2016} }
TY - EJOUR
T1 - GlobalSIP_Recover from Tracking Failure_KEHE
AU - Ke He; Ningning Li; Borui Mo; Bo Yang; Aidong Men
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1388
ER -
Ke He, Ningning Li, Borui Mo, Bo Yang, Aidong Men. (2016). GlobalSIP_Recover from Tracking Failure_KEHE. IEEE SigPort. http://sigport.org/1388
Ke He, Ningning Li, Borui Mo, Bo Yang, Aidong Men, 2016. GlobalSIP_Recover from Tracking Failure_KEHE. Available at: http://sigport.org/1388.
Ke He, Ningning Li, Borui Mo, Bo Yang, Aidong Men. (2016). "GlobalSIP_Recover from Tracking Failure_KEHE." Web.
1. Ke He, Ningning Li, Borui Mo, Bo Yang, Aidong Men. GlobalSIP_Recover from Tracking Failure_KEHE [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1388

IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS


Measuring the Heart Rate (HR) plays an important role in the description of human physiological and psychological state, due to its relationship with cognitive/emotional factors such as attention effort, stress or arousal. For this reason, remote methodologies for HR measurements have recently been investigated to find a reliable and cost-effective methodology. Our work aims at the following:
• Development of a novel technique for remote HR estimation
• Comparison of the proposed method with the state of the art on a common dataset

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Authors:
Alain Malacarne, Mattia Bonomi, Cecilia Pasquini, Giulia Boato
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6 December 2016 - 9:34pm
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Improved Remote Estimation of Heart Rate in Face Videos

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[1] Alain Malacarne, Mattia Bonomi, Cecilia Pasquini, Giulia Boato, " IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1387. Accessed: Jul. 16, 2018.
@article{1387-16,
url = {http://sigport.org/1387},
author = {Alain Malacarne; Mattia Bonomi; Cecilia Pasquini; Giulia Boato },
publisher = {IEEE SigPort},
title = { IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS},
year = {2016} }
TY - EJOUR
T1 - IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS
AU - Alain Malacarne; Mattia Bonomi; Cecilia Pasquini; Giulia Boato
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1387
ER -
Alain Malacarne, Mattia Bonomi, Cecilia Pasquini, Giulia Boato. (2016). IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS. IEEE SigPort. http://sigport.org/1387
Alain Malacarne, Mattia Bonomi, Cecilia Pasquini, Giulia Boato, 2016. IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS. Available at: http://sigport.org/1387.
Alain Malacarne, Mattia Bonomi, Cecilia Pasquini, Giulia Boato. (2016). " IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS." Web.
1. Alain Malacarne, Mattia Bonomi, Cecilia Pasquini, Giulia Boato. IMPROVED REMOTE ESTIMATION OF HEART RATE IN FACE VIDEOS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1387

Emplying Vector Quantization on Detected Facial Parts for Face Recognition


Facial Parts Detection (FPD) approach in conjunction with Vector Quantization (VQ) algorithm are proposed for face recognition. Detecting facial parts, which are nose, both eyes, and mouth, and choosing appropriate dimensions for each part, are done in the preprocessing phase. In the feature extraction phase, four groups for each person, one group for each detected part, are constructed for dimensionality reduction and feature discrimination by considering all parts of all training poses. For further data compression, VQ algorithm is applied to each of the four groups.

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Authors:
Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael
Submitted On:
5 December 2016 - 2:09pm
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Global_SIP_2016.pdf

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[1] Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael, "Emplying Vector Quantization on Detected Facial Parts for Face Recognition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1348. Accessed: Jul. 16, 2018.
@article{1348-16,
url = {http://sigport.org/1348},
author = {Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael },
publisher = {IEEE SigPort},
title = {Emplying Vector Quantization on Detected Facial Parts for Face Recognition},
year = {2016} }
TY - EJOUR
T1 - Emplying Vector Quantization on Detected Facial Parts for Face Recognition
AU - Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1348
ER -
Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael. (2016). Emplying Vector Quantization on Detected Facial Parts for Face Recognition. IEEE SigPort. http://sigport.org/1348
Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael, 2016. Emplying Vector Quantization on Detected Facial Parts for Face Recognition. Available at: http://sigport.org/1348.
Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael. (2016). "Emplying Vector Quantization on Detected Facial Parts for Face Recognition." Web.
1. Ahmed Aldhahab; Taif Alobaidi; Wasfy B. Mikhael. Emplying Vector Quantization on Detected Facial Parts for Face Recognition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1348

Face spoofing attack detection based on the behavior of noises


Authentication by facial recognition is actually one of the solutions to reinforce the security level
of information systems. However, face recognition systems are proven to be vulnerable to spoofing
attack. In fact, an attacker can bypass the authentification process easily by presenting in front of the
camera a copy version of a legitimate user’s face.
To make your face as your password, it is of vital importance to identify and reject the falsified faces

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Authors:
Florent Retraint, Frédéric Morain-Nicolier, Agnès Delahaies
Submitted On:
2 December 2016 - 11:13am
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GlobalSIP HOAI.pdf

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[1] Florent Retraint, Frédéric Morain-Nicolier, Agnès Delahaies, "Face spoofing attack detection based on the behavior of noises", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1332. Accessed: Jul. 16, 2018.
@article{1332-16,
url = {http://sigport.org/1332},
author = {Florent Retraint; Frédéric Morain-Nicolier; Agnès Delahaies },
publisher = {IEEE SigPort},
title = {Face spoofing attack detection based on the behavior of noises},
year = {2016} }
TY - EJOUR
T1 - Face spoofing attack detection based on the behavior of noises
AU - Florent Retraint; Frédéric Morain-Nicolier; Agnès Delahaies
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1332
ER -
Florent Retraint, Frédéric Morain-Nicolier, Agnès Delahaies. (2016). Face spoofing attack detection based on the behavior of noises. IEEE SigPort. http://sigport.org/1332
Florent Retraint, Frédéric Morain-Nicolier, Agnès Delahaies, 2016. Face spoofing attack detection based on the behavior of noises. Available at: http://sigport.org/1332.
Florent Retraint, Frédéric Morain-Nicolier, Agnès Delahaies. (2016). "Face spoofing attack detection based on the behavior of noises." Web.
1. Florent Retraint, Frédéric Morain-Nicolier, Agnès Delahaies. Face spoofing attack detection based on the behavior of noises [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1332

A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising


A Wiener filtering scheme in graph Fourier domain is proposed for
improving image denoising performance achieved by various spectral
graph based denoising methods. The proposed Wiener filter is
estimated by using graph Fourier coefficients of the noisy image after
they are processed for denoising, to further improve the already
achieved denoising accuracy as a post-processing step. It can be estimated
from and applied to the entire image, or can be used patchwise
in a locally adaptive manner. Our results indicate that the proposed

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Authors:
Ali Can Yagan, Mehmet Tankut Ozgen
Submitted On:
5 November 2016 - 8:54am
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Poster

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[1] Ali Can Yagan, Mehmet Tankut Ozgen, "A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1262. Accessed: Jul. 16, 2018.
@article{1262-16,
url = {http://sigport.org/1262},
author = {Ali Can Yagan; Mehmet Tankut Ozgen },
publisher = {IEEE SigPort},
title = {A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising},
year = {2016} }
TY - EJOUR
T1 - A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising
AU - Ali Can Yagan; Mehmet Tankut Ozgen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1262
ER -
Ali Can Yagan, Mehmet Tankut Ozgen. (2016). A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising. IEEE SigPort. http://sigport.org/1262
Ali Can Yagan, Mehmet Tankut Ozgen, 2016. A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising. Available at: http://sigport.org/1262.
Ali Can Yagan, Mehmet Tankut Ozgen. (2016). "A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising." Web.
1. Ali Can Yagan, Mehmet Tankut Ozgen. A Spectral Graph Wiener Filter in Graph Fourier Domain for Improved Image Denoising [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1262

Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes

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Authors:
Frank Ong, Sameer Pawar, Kannan Ramchandran
Submitted On:
30 March 2016 - 11:40pm
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2dffast_icassp.pdf

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[1] Frank Ong, Sameer Pawar, Kannan Ramchandran, "Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1072. Accessed: Jul. 16, 2018.
@article{1072-16,
url = {http://sigport.org/1072},
author = {Frank Ong; Sameer Pawar; Kannan Ramchandran },
publisher = {IEEE SigPort},
title = {Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes},
year = {2016} }
TY - EJOUR
T1 - Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes
AU - Frank Ong; Sameer Pawar; Kannan Ramchandran
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1072
ER -
Frank Ong, Sameer Pawar, Kannan Ramchandran. (2016). Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes. IEEE SigPort. http://sigport.org/1072
Frank Ong, Sameer Pawar, Kannan Ramchandran, 2016. Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes. Available at: http://sigport.org/1072.
Frank Ong, Sameer Pawar, Kannan Ramchandran. (2016). "Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes." Web.
1. Frank Ong, Sameer Pawar, Kannan Ramchandran. Slides for Fast sparse 2-D DFT Computation Using Sparse-Graph Alias Codes [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1072

Predicting Visual Attention Using Gamma Kernels


Saliency measures are a popular way to predict visual attention. However, saliency is normally tested on sets of single resolution images that are unlike what the human vision system sees. We propose a new saliency measure based on convolving images with 2D gamma kernels which function as a comparison between a center and a surrounding neighborhood. The two parameters in the gamma kernel provide an ideal way to change the size of both the center and the surrounding neighborhood, which makes finding saliency at different scales simple and fast.

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Authors:
Ryan Burt, Eder Santana, Jose Principe, Nina Thigpen, Andreas Keil
Submitted On:
24 March 2016 - 1:39pm
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Gamma_poster.pdf

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[1] Ryan Burt, Eder Santana, Jose Principe, Nina Thigpen, Andreas Keil, "Predicting Visual Attention Using Gamma Kernels", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1029. Accessed: Jul. 16, 2018.
@article{1029-16,
url = {http://sigport.org/1029},
author = {Ryan Burt; Eder Santana; Jose Principe; Nina Thigpen; Andreas Keil },
publisher = {IEEE SigPort},
title = {Predicting Visual Attention Using Gamma Kernels},
year = {2016} }
TY - EJOUR
T1 - Predicting Visual Attention Using Gamma Kernels
AU - Ryan Burt; Eder Santana; Jose Principe; Nina Thigpen; Andreas Keil
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1029
ER -
Ryan Burt, Eder Santana, Jose Principe, Nina Thigpen, Andreas Keil. (2016). Predicting Visual Attention Using Gamma Kernels. IEEE SigPort. http://sigport.org/1029
Ryan Burt, Eder Santana, Jose Principe, Nina Thigpen, Andreas Keil, 2016. Predicting Visual Attention Using Gamma Kernels. Available at: http://sigport.org/1029.
Ryan Burt, Eder Santana, Jose Principe, Nina Thigpen, Andreas Keil. (2016). "Predicting Visual Attention Using Gamma Kernels." Web.
1. Ryan Burt, Eder Santana, Jose Principe, Nina Thigpen, Andreas Keil. Predicting Visual Attention Using Gamma Kernels [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1029

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