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Image, Video, and Multidimensional Signal Processing

Immersive Optical-See-Through Augmented Reality (Keynote Talk)


Immersive Optical-See-Through Augmented Reality. Augmented Reality has been getting ready for the last 20 years, and is finally becoming real, powered by progress in enabling technologies such as graphics, vision, sensors, and displays. In this talk I’ll provide a personal retrospective on my journey, working on all those enablers, getting ready for the coming AR revolution. At Meta, we are working on immersive optical-see-through AR headset, as well as the full software stack. We’ll discuss the differences of optical vs.

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Authors:
Kari Pulli
Submitted On:
22 December 2017 - 1:30pm
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ICIP_2017_Meta_AR_small.pdf

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[1] Kari Pulli, "Immersive Optical-See-Through Augmented Reality (Keynote Talk)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2261. Accessed: Nov. 20, 2018.
@article{2261-17,
url = {http://sigport.org/2261},
author = {Kari Pulli },
publisher = {IEEE SigPort},
title = {Immersive Optical-See-Through Augmented Reality (Keynote Talk)},
year = {2017} }
TY - EJOUR
T1 - Immersive Optical-See-Through Augmented Reality (Keynote Talk)
AU - Kari Pulli
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2261
ER -
Kari Pulli. (2017). Immersive Optical-See-Through Augmented Reality (Keynote Talk). IEEE SigPort. http://sigport.org/2261
Kari Pulli, 2017. Immersive Optical-See-Through Augmented Reality (Keynote Talk). Available at: http://sigport.org/2261.
Kari Pulli. (2017). "Immersive Optical-See-Through Augmented Reality (Keynote Talk)." Web.
1. Kari Pulli. Immersive Optical-See-Through Augmented Reality (Keynote Talk) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2261

LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR


Classification subnetwork and box regression subnetwork are
essential components in deep networks for object detection.
However, we observe a contradiction that before NMS, some
better localized detections do not correspond to higher classification confidences, and vice versa. This contradiction exists because classification confidences can not fully reflect the
localization-quality (loc-quality) of each detection. In this
work, we propose the Localization-quality Estimation embedded Detector abbreviated as LED, and a corresponding

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Authors:
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song
Submitted On:
9 October 2018 - 6:52am
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ICIP_oral_ShiquanZhang_LED

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[1] Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song, "LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3655. Accessed: Nov. 20, 2018.
@article{3655-18,
url = {http://sigport.org/3655},
author = {Shiquan Zhang;Xu Zhao;Liangji Fang;Haiping Fei;Haitao Song },
publisher = {IEEE SigPort},
title = {LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR},
year = {2018} }
TY - EJOUR
T1 - LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR
AU - Shiquan Zhang;Xu Zhao;Liangji Fang;Haiping Fei;Haitao Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3655
ER -
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song. (2018). LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE SigPort. http://sigport.org/3655
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song, 2018. LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. Available at: http://sigport.org/3655.
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song. (2018). "LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR." Web.
1. Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song. LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3655

Image Fusion and Reconstruction of Compressed Data: A Joint Approach


In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter.
In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources.

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Authors:
Laurent Condat, Florian Cotte, Mauro Dalla Mura
Submitted On:
8 October 2018 - 7:37pm
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Presentation_ICIP2018_v3.pdf

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[1] Laurent Condat, Florian Cotte, Mauro Dalla Mura, "Image Fusion and Reconstruction of Compressed Data: A Joint Approach", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3651. Accessed: Nov. 20, 2018.
@article{3651-18,
url = {http://sigport.org/3651},
author = {Laurent Condat; Florian Cotte; Mauro Dalla Mura },
publisher = {IEEE SigPort},
title = {Image Fusion and Reconstruction of Compressed Data: A Joint Approach},
year = {2018} }
TY - EJOUR
T1 - Image Fusion and Reconstruction of Compressed Data: A Joint Approach
AU - Laurent Condat; Florian Cotte; Mauro Dalla Mura
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3651
ER -
Laurent Condat, Florian Cotte, Mauro Dalla Mura. (2018). Image Fusion and Reconstruction of Compressed Data: A Joint Approach. IEEE SigPort. http://sigport.org/3651
Laurent Condat, Florian Cotte, Mauro Dalla Mura, 2018. Image Fusion and Reconstruction of Compressed Data: A Joint Approach. Available at: http://sigport.org/3651.
Laurent Condat, Florian Cotte, Mauro Dalla Mura. (2018). "Image Fusion and Reconstruction of Compressed Data: A Joint Approach." Web.
1. Laurent Condat, Florian Cotte, Mauro Dalla Mura. Image Fusion and Reconstruction of Compressed Data: A Joint Approach [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3651

Learning Semantics-Guided Visual Attention for Few-shot Image Classification


We propose a deep learning framework for few-shot image classification, which exploits information across label semantics and image domains, so that regions of interest can be properly attended for improved classification. The proposed semantics-guided attention module is able to focus on most relevant regions in an image, while the attended image samples allow data augmentation and alleviate possible overfitting during FSL training. Promising performances are presented in our experiments, in which we consider both closed and open-world settings.

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Authors:
Wen-Hsuan Chu, Yu-Chiang Frank Wang
Submitted On:
8 October 2018 - 2:56pm
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ICIP18_POSTER_FSL.pdf

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[1] Wen-Hsuan Chu, Yu-Chiang Frank Wang, "Learning Semantics-Guided Visual Attention for Few-shot Image Classification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3640. Accessed: Nov. 20, 2018.
@article{3640-18,
url = {http://sigport.org/3640},
author = {Wen-Hsuan Chu; Yu-Chiang Frank Wang },
publisher = {IEEE SigPort},
title = {Learning Semantics-Guided Visual Attention for Few-shot Image Classification},
year = {2018} }
TY - EJOUR
T1 - Learning Semantics-Guided Visual Attention for Few-shot Image Classification
AU - Wen-Hsuan Chu; Yu-Chiang Frank Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3640
ER -
Wen-Hsuan Chu, Yu-Chiang Frank Wang. (2018). Learning Semantics-Guided Visual Attention for Few-shot Image Classification. IEEE SigPort. http://sigport.org/3640
Wen-Hsuan Chu, Yu-Chiang Frank Wang, 2018. Learning Semantics-Guided Visual Attention for Few-shot Image Classification. Available at: http://sigport.org/3640.
Wen-Hsuan Chu, Yu-Chiang Frank Wang. (2018). "Learning Semantics-Guided Visual Attention for Few-shot Image Classification." Web.
1. Wen-Hsuan Chu, Yu-Chiang Frank Wang. Learning Semantics-Guided Visual Attention for Few-shot Image Classification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3640

A Pipeline for Lenslet Light Field Quality Enhancement


In recent years, light fields have become a major research topic and their applications span across the entire spectrum of classical image processing. Among the different methods used to capture a light field are the lenslet cameras, such as those developed by Lytro. While these cameras give a lot of freedom to the user, they also create light field views that suffer from a number of artefacts. As a result, it is common to ignore a significant subset of these views when doing high-level light field processing.

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Authors:
Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic
Submitted On:
8 October 2018 - 2:38pm
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matysiak_lenslet_pipeline.pdf

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[1] Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic, "A Pipeline for Lenslet Light Field Quality Enhancement", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3639. Accessed: Nov. 20, 2018.
@article{3639-18,
url = {http://sigport.org/3639},
author = {Pierre Matysiak; Mairéad Grogan; Mikaël Le Pendu; Martin Alain; Aljosa Smolic },
publisher = {IEEE SigPort},
title = {A Pipeline for Lenslet Light Field Quality Enhancement},
year = {2018} }
TY - EJOUR
T1 - A Pipeline for Lenslet Light Field Quality Enhancement
AU - Pierre Matysiak; Mairéad Grogan; Mikaël Le Pendu; Martin Alain; Aljosa Smolic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3639
ER -
Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic. (2018). A Pipeline for Lenslet Light Field Quality Enhancement. IEEE SigPort. http://sigport.org/3639
Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic, 2018. A Pipeline for Lenslet Light Field Quality Enhancement. Available at: http://sigport.org/3639.
Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic. (2018). "A Pipeline for Lenslet Light Field Quality Enhancement." Web.
1. Pierre Matysiak, Mairéad Grogan, Mikaël Le Pendu, Martin Alain, Aljosa Smolic. A Pipeline for Lenslet Light Field Quality Enhancement [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3639

Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences

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Authors:
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni
Submitted On:
8 October 2018 - 7:48am
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ICIP2018_Presentation.pdf

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[1] Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni, "Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3627. Accessed: Nov. 20, 2018.
@article{3627-18,
url = {http://sigport.org/3627},
author = {Damian Campo; Mohamad Baydoun; Lucio Marcenaro; Andrea Cavallaro; Carlo Regazzoni },
publisher = {IEEE SigPort},
title = {Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences
AU - Damian Campo; Mohamad Baydoun; Lucio Marcenaro; Andrea Cavallaro; Carlo Regazzoni
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3627
ER -
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni. (2018). Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences. IEEE SigPort. http://sigport.org/3627
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni, 2018. Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences. Available at: http://sigport.org/3627.
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni. (2018). "Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences." Web.
1. Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni. Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3627

Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences

Paper Details

Authors:
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni
Submitted On:
8 October 2018 - 7:48am
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ICIP2018_Presentation.pdf

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[1] Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni, "Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3626. Accessed: Nov. 20, 2018.
@article{3626-18,
url = {http://sigport.org/3626},
author = {Damian Campo; Mohamad Baydoun; Lucio Marcenaro; Andrea Cavallaro; Carlo Regazzoni },
publisher = {IEEE SigPort},
title = {Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences
AU - Damian Campo; Mohamad Baydoun; Lucio Marcenaro; Andrea Cavallaro; Carlo Regazzoni
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3626
ER -
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni. (2018). Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences. IEEE SigPort. http://sigport.org/3626
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni, 2018. Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences. Available at: http://sigport.org/3626.
Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni. (2018). "Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences." Web.
1. Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Andrea Cavallaro, Carlo Regazzoni. Unsupervised Trajectory Modeling based on Discrete Descriptors for classifying Moving Objects in video Sequences [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3626

TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA


Light field acquisition technologies capture spatial and angular information of the scene. The angular information paves
the way for various post-processing applications, e.g. scene reconstruction, refocusing, synthetic aperture. The light field
is usually captured by a single plenoptic camera or by multiple traditional cameras. The former captures dense light

Paper Details

Authors:
Roger Olsson, Mårten Sjöström
Submitted On:
8 October 2018 - 12:39am
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AhmadWaqas_ICIP2018.pptx

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[1] Roger Olsson, Mårten Sjöström, "TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3606. Accessed: Nov. 20, 2018.
@article{3606-18,
url = {http://sigport.org/3606},
author = {Roger Olsson; Mårten Sjöström },
publisher = {IEEE SigPort},
title = {TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA},
year = {2018} }
TY - EJOUR
T1 - TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA
AU - Roger Olsson; Mårten Sjöström
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3606
ER -
Roger Olsson, Mårten Sjöström. (2018). TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA. IEEE SigPort. http://sigport.org/3606
Roger Olsson, Mårten Sjöström, 2018. TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA. Available at: http://sigport.org/3606.
Roger Olsson, Mårten Sjöström. (2018). "TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA." Web.
1. Roger Olsson, Mårten Sjöström. TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3606

An Interior Point Method for Nonnegative Sparse Signal Reconstruction


We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.

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Authors:
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld
Submitted On:
7 October 2018 - 5:05pm
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2018_Huang_IPAlgorithm_ICIP_Poster.pdf

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[1] Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, "An Interior Point Method for Nonnegative Sparse Signal Reconstruction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3603. Accessed: Nov. 20, 2018.
@article{3603-18,
url = {http://sigport.org/3603},
author = {Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld },
publisher = {IEEE SigPort},
title = {An Interior Point Method for Nonnegative Sparse Signal Reconstruction},
year = {2018} }
TY - EJOUR
T1 - An Interior Point Method for Nonnegative Sparse Signal Reconstruction
AU - Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3603
ER -
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). An Interior Point Method for Nonnegative Sparse Signal Reconstruction. IEEE SigPort. http://sigport.org/3603
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, 2018. An Interior Point Method for Nonnegative Sparse Signal Reconstruction. Available at: http://sigport.org/3603.
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). "An Interior Point Method for Nonnegative Sparse Signal Reconstruction." Web.
1. Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. An Interior Point Method for Nonnegative Sparse Signal Reconstruction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3603

A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY


Model based methods have gained popularity in the past few decades in reconstruction problems particularly when the measurement data is sparse. In model based inference, apart from a model for the measurements, there exists a model for the unknown signal to be reconstructed, called the prior model. Model based methods tend to do very well when the prior model is accurate and representative of real world behavior of the unknown signal.

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Authors:
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman
Submitted On:
6 October 2018 - 5:02pm
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ICIP_2018_Final_Poster_v2.pdf

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[1] Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman, "A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3578. Accessed: Nov. 20, 2018.
@article{3578-18,
url = {http://sigport.org/3578},
author = {Zeeshan Nadir; Kristin M. Rice; Michael S. Brown; Charles A. Bouman },
publisher = {IEEE SigPort},
title = {A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY},
year = {2018} }
TY - EJOUR
T1 - A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY
AU - Zeeshan Nadir; Kristin M. Rice; Michael S. Brown; Charles A. Bouman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3578
ER -
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman. (2018). A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY. IEEE SigPort. http://sigport.org/3578
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman, 2018. A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY. Available at: http://sigport.org/3578.
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman. (2018). "A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY." Web.
1. Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman. A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3578

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