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Bio Imaging and Signal Processing

Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data

Aref Miri Rekavandi

Adaptive subspace detectors (ASD) generalize matched subspace detectors (MSD) by accounting for possible correlation. Both ASD and MSD are derived using the generalized likelihood ratio test (GLRT). While MSD assumes there is no correlation between observations, ASD estimates a sample covariance matrix of possibly correlated samples using signal-free observations. In this paper, we address the performance of the ASD when the number of secondary data is insufficient and the observed signal lies in higher dimensional space.

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Authors:
Aref Miri Rekavandi
Aref Miri Rekavandi, Abd-Krim Seghouane, Robin J. Evans
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10 May 2019 - 2:26am
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[1] Aref Miri Rekavandi, Abd-Krim Seghouane, Robin J. Evans, "Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4274. Accessed: May. 26, 2019.
@article{4274-19,
url = {http://sigport.org/4274},
author = {Aref Miri Rekavandi; Abd-Krim Seghouane; Robin J. Evans },
publisher = {IEEE SigPort},
title = {Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data},
year = {2019} }
TY - EJOUR
T1 - Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data
AU - Aref Miri Rekavandi; Abd-Krim Seghouane; Robin J. Evans
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4274
ER -
Aref Miri Rekavandi, Abd-Krim Seghouane, Robin J. Evans. (2019). Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data. IEEE SigPort. http://sigport.org/4274
Aref Miri Rekavandi, Abd-Krim Seghouane, Robin J. Evans, 2019. Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data. Available at: http://sigport.org/4274.
Aref Miri Rekavandi, Abd-Krim Seghouane, Robin J. Evans. (2019). "Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data." Web.
1. Aref Miri Rekavandi, Abd-Krim Seghouane, Robin J. Evans. Adaptive Subspace Detector in High Dimensional Space with Insufficient Training Data [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4274

Adaptive Multi-Trace Carving Based on Dynamic Programming

Qiang ZHU
Chau-Wai Wong
Min Wu

In this work, we study the problem of tracking multiple frequency components in a noisy signal using a spectrogram-based method. Previous approaches such as image processing based or hidden Markov model-based methods may not be capable of tracking multiple frequency components, may require extensive training, and may be time-consuming. To address these issues, we propose an accurate and efficient method named Adaptive Multi-Trace Carving (AMTC) for tracking multiple frequency traces by iterative forward and backward dynamic programming and adaptive trace compensation.

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Authors:
Qiang ZHU
Chau-Wai Wong
Min Wu
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu
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7 March 2019 - 10:50am
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[PDF] AMTC Asilomar 2018 poster

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[1] Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu, "Adaptive Multi-Trace Carving Based on Dynamic Programming", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3845. Accessed: May. 26, 2019.
@article{3845-19,
url = {http://sigport.org/3845},
author = {Qiang Zhu; Mingliang Chen; Chau-Wai Wong; Min Wu },
publisher = {IEEE SigPort},
title = {Adaptive Multi-Trace Carving Based on Dynamic Programming},
year = {2019} }
TY - EJOUR
T1 - Adaptive Multi-Trace Carving Based on Dynamic Programming
AU - Qiang Zhu; Mingliang Chen; Chau-Wai Wong; Min Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3845
ER -
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu. (2019). Adaptive Multi-Trace Carving Based on Dynamic Programming. IEEE SigPort. http://sigport.org/3845
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu, 2019. Adaptive Multi-Trace Carving Based on Dynamic Programming. Available at: http://sigport.org/3845.
Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu. (2019). "Adaptive Multi-Trace Carving Based on Dynamic Programming." Web.
1. Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu. Adaptive Multi-Trace Carving Based on Dynamic Programming [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3845

NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER

Fangyu Li

Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. A bed-mounted seismometer system is proposed to monitor the heart and respiratory rates, and body movement and posture, during the sleep. To effectively monitor sleep status, an innovative local maxima statistics based approach and an instantaneous property based method are developed to estimate heart and respiratory rates, respectively. These methods are more robust and stable compared to previous works.

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Authors:
Fangyu Li
Fangyu Li, Jose Clemente, WenZhan Song
Submitted On:
18 November 2018 - 4:25pm
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[1] Fangyu Li, Jose Clemente, WenZhan Song, "NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3681. Accessed: May. 26, 2019.
@article{3681-18,
url = {http://sigport.org/3681},
author = {Fangyu Li; Jose Clemente; WenZhan Song },
publisher = {IEEE SigPort},
title = {NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER},
year = {2018} }
TY - EJOUR
T1 - NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER
AU - Fangyu Li; Jose Clemente; WenZhan Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3681
ER -
Fangyu Li, Jose Clemente, WenZhan Song. (2018). NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER. IEEE SigPort. http://sigport.org/3681
Fangyu Li, Jose Clemente, WenZhan Song, 2018. NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER. Available at: http://sigport.org/3681.
Fangyu Li, Jose Clemente, WenZhan Song. (2018). "NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER." Web.
1. Fangyu Li, Jose Clemente, WenZhan Song. NON-INTRUSIVE AND NON-CONTACT SLEEP MONITORING WITH SEISMOMETER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3681

BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES

Athanasios Balomenos

Time-lapse microscopy provides 4D imaging data for monitoring and studying down to single-cell, the stochastic processes involved as bacterial colonies grow and interact under different stress conditions. Two main factors prevent high throughput analysis: a) cell segmentation and tracking are very time-consuming and error-prone and b) analytics tools are lacking to interpret the plethora of features extracted from a complex “cell-movie.” To address both limitations, we have recently developed a multi-resolution Bio-image Analysis & Single-Cell Analytics framework, called BaSCA.

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Authors:
Athanasios Balomenos
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos
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8 October 2018 - 9:46am
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BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES POSTER

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[1] Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos, "BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3634. Accessed: May. 26, 2019.
@article{3634-18,
url = {http://sigport.org/3634},
author = {Athanasios D. Balomenos; Victoria Stefanou; Elias S. Manolakos },
publisher = {IEEE SigPort},
title = {BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES},
year = {2018} }
TY - EJOUR
T1 - BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES
AU - Athanasios D. Balomenos; Victoria Stefanou; Elias S. Manolakos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3634
ER -
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos. (2018). BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES. IEEE SigPort. http://sigport.org/3634
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos, 2018. BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES. Available at: http://sigport.org/3634.
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos. (2018). "BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES." Web.
1. Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos. BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3634

ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES

Saul Calderon-Ramirez

In this work we analyze the impact of denoising, contrast and edge enhancement using the Deceived Non Local Means (DNLM) filter in a Convolutional Neural Network (CNN) based approach for age estimation using digital X-ray images from hands. The DNLM filter contains two parameters which control edge enhancement and denoising. Increasing levels were tested to assess the impact of both contrast enhancement and denoising in the CNN based model regression accuracy.

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Authors:
Saul Calderon-Ramirez
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+
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6 October 2018 - 8:15am
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[1] S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+, "ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3573. Accessed: May. 26, 2019.
@article{3573-18,
url = {http://sigport.org/3573},
author = {S. Calderon?; F. Fallas†; M. Zumbado‡; P. N. Tyrrell±; H. Stark; Z. Emersic§; B. Medeno; M. Solis+ },
publisher = {IEEE SigPort},
title = {ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES},
year = {2018} }
TY - EJOUR
T1 - ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES
AU - S. Calderon?; F. Fallas†; M. Zumbado‡; P. N. Tyrrell±; H. Stark; Z. Emersic§; B. Medeno; M. Solis+
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3573
ER -
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+. (2018). ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES. IEEE SigPort. http://sigport.org/3573
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+, 2018. ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES. Available at: http://sigport.org/3573.
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+. (2018). "ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES." Web.
1. S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+. ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3573

MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES

Prabhjot Kaur

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Authors:
Prabhjot Kaur
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar
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5 October 2018 - 12:09pm
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[1] Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar, "MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3545. Accessed: May. 26, 2019.
@article{3545-18,
url = {http://sigport.org/3545},
author = {Prabhjot Kaur; Aditya Sharma; Aditya Nigam; Arnav Bhavsar },
publisher = {IEEE SigPort},
title = {MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES},
year = {2018} }
TY - EJOUR
T1 - MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES
AU - Prabhjot Kaur; Aditya Sharma; Aditya Nigam; Arnav Bhavsar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3545
ER -
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar. (2018). MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES. IEEE SigPort. http://sigport.org/3545
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar, 2018. MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES. Available at: http://sigport.org/3545.
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar. (2018). "MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES." Web.
1. Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar. MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3545

SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES

Panagiotis Dimitrakpoulos

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Authors:
Panagiotis Dimitrakpoulos
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti
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5 October 2018 - 4:35am
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[1] Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti , "SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3520. Accessed: May. 26, 2019.
@article{3520-18,
url = {http://sigport.org/3520},
author = {Marina E. Plissiti; P. Dimitrakopoulos; G. Sfikas; Christophoros Nikou; O. Krikoni; A. Charchanti },
publisher = {IEEE SigPort},
title = {SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES},
year = {2018} }
TY - EJOUR
T1 - SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES
AU - Marina E. Plissiti; P. Dimitrakopoulos; G. Sfikas; Christophoros Nikou; O. Krikoni; A. Charchanti
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3520
ER -
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti . (2018). SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES. IEEE SigPort. http://sigport.org/3520
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti , 2018. SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES. Available at: http://sigport.org/3520.
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti . (2018). "SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES." Web.
1. Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti . SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3520

INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS

Hossein Rabbani Khorasgani

Optical coherence tomography (OCT) is a powerful method for imaging the retinal layers. In this paper, we develop a novel 3D fully convolutional deep architecture for automated segmentation of retinal layers in OCT scans. This model extracts features from both the spatial and the inter-frame dimensions by performing 3D convolutions, thereby capturing the information encoded in multiple adjacent frames.

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Hossein Rabbani Khorasgani
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5 October 2018 - 3:33am
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[1] , "INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3511. Accessed: May. 26, 2019.
@article{3511-18,
url = {http://sigport.org/3511},
author = { },
publisher = {IEEE SigPort},
title = {INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3511
ER -
. (2018). INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS. IEEE SigPort. http://sigport.org/3511
, 2018. INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS. Available at: http://sigport.org/3511.
. (2018). "INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS." Web.
1. . INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3511

Deep Tree Models for ‘Big’ Biological Data

Ioannis Kontoyiannis

The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems across the spectrum of biological studies. Most of the early statistical approaches were ineffective in practice, because the amount of data required for reliable modelling grew exponentially with memory length.

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Authors:
Ioannis Kontoyiannis
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou
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24 June 2018 - 9:36am
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[1] Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou, "Deep Tree Models for ‘Big’ Biological Data", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3320. Accessed: May. 26, 2019.
@article{3320-18,
url = {http://sigport.org/3320},
author = {Lambros Mertzanis; Athina Panotopoulou; Maria Skoularidou },
publisher = {IEEE SigPort},
title = {Deep Tree Models for ‘Big’ Biological Data},
year = {2018} }
TY - EJOUR
T1 - Deep Tree Models for ‘Big’ Biological Data
AU - Lambros Mertzanis; Athina Panotopoulou; Maria Skoularidou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3320
ER -
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. (2018). Deep Tree Models for ‘Big’ Biological Data. IEEE SigPort. http://sigport.org/3320
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou, 2018. Deep Tree Models for ‘Big’ Biological Data. Available at: http://sigport.org/3320.
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. (2018). "Deep Tree Models for ‘Big’ Biological Data." Web.
1. Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. Deep Tree Models for ‘Big’ Biological Data [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3320

Invisible Geo-Location Signature in a Single Image

Chau-Wai Wong
Adi Hajj-Ahmad
Min Wu

Geo-tagging images of interest is increasingly important to law enforcement, national security, and journalism. Many images today do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an invisible signature from the power grid, the Electric Network Frequency (ENF) signal, which can be inherently recorded in a sensing stream at the time of capturing and carries useful location information.

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Authors:
Chau-Wai Wong
Adi Hajj-Ahmad
Min Wu
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1 March 2019 - 1:27pm
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[1] , "Invisible Geo-Location Signature in a Single Image", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3186. Accessed: May. 26, 2019.
@article{3186-18,
url = {http://sigport.org/3186},
author = { },
publisher = {IEEE SigPort},
title = {Invisible Geo-Location Signature in a Single Image},
year = {2018} }
TY - EJOUR
T1 - Invisible Geo-Location Signature in a Single Image
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3186
ER -
. (2018). Invisible Geo-Location Signature in a Single Image. IEEE SigPort. http://sigport.org/3186
, 2018. Invisible Geo-Location Signature in a Single Image. Available at: http://sigport.org/3186.
. (2018). "Invisible Geo-Location Signature in a Single Image." Web.
1. . Invisible Geo-Location Signature in a Single Image [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3186

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