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ICIP 2018

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website.

A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS

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Authors:
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini
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9 October 2018 - 4:13pm
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ICIP_2018.pdf

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[1] Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini, "A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3642. Accessed: Dec. 10, 2018.
@article{3642-18,
url = {http://sigport.org/3642},
author = {Melpomeni Dimopoulou; Effrosyni Doutsi; Marc Antonini },
publisher = {IEEE SigPort},
title = {A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS},
year = {2018} }
TY - EJOUR
T1 - A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS
AU - Melpomeni Dimopoulou; Effrosyni Doutsi; Marc Antonini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3642
ER -
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. (2018). A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS. IEEE SigPort. http://sigport.org/3642
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini, 2018. A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS. Available at: http://sigport.org/3642.
Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. (2018). "A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS." Web.
1. Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. A RETINA-INSPIRED ENCODER: AN INNOVATIVE STEP ON IMAGE CODING USING LEAKY INTEGRATE-AND-FIRE NEURONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3642

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: Dec. 10, 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: Dec. 10, 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

R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION


Point cloud are precise digital record of an objects in space. It starts to getting more attention due to the additional information it provides compared to 2D images. In this paper, we propose a new deep learning architecture called R-CovNets, designed for 3D object recognition. Unlike to previous approaches that usually sample or convert point cloud into three-dimensional grids, R-CovNets does not reckon on any preprocessing. Our architecture is specially designed for cloud point, permutation invariant and can take as input, a data of any size.

R-COVNET.pdf

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Authors:
Danielle Tchuinkou Kwadj and Christophe Bobda
Submitted On:
8 October 2018 - 2:07pm
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R-COVNET.pdf

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[1] Danielle Tchuinkou Kwadj and Christophe Bobda, "R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3638. Accessed: Dec. 10, 2018.
@article{3638-18,
url = {http://sigport.org/3638},
author = {Danielle Tchuinkou Kwadj and Christophe Bobda },
publisher = {IEEE SigPort},
title = {R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION
AU - Danielle Tchuinkou Kwadj and Christophe Bobda
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3638
ER -
Danielle Tchuinkou Kwadj and Christophe Bobda. (2018). R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION. IEEE SigPort. http://sigport.org/3638
Danielle Tchuinkou Kwadj and Christophe Bobda, 2018. R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION. Available at: http://sigport.org/3638.
Danielle Tchuinkou Kwadj and Christophe Bobda. (2018). "R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION." Web.
1. Danielle Tchuinkou Kwadj and Christophe Bobda. R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3638

Deep-learning-based pipe leak detection using image-based leak features

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Authors:
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo
Submitted On:
8 October 2018 - 11:29am
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Poster_ICIP2018.pdf

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[1] Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo, "Deep-learning-based pipe leak detection using image-based leak features", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3637. Accessed: Dec. 10, 2018.
@article{3637-18,
url = {http://sigport.org/3637},
author = {Doo-Byung Yoon; Se Won OH; Gwan Joong Kim; Nae-Soo Kim; Cheol-Sig Pyo },
publisher = {IEEE SigPort},
title = {Deep-learning-based pipe leak detection using image-based leak features},
year = {2018} }
TY - EJOUR
T1 - Deep-learning-based pipe leak detection using image-based leak features
AU - Doo-Byung Yoon; Se Won OH; Gwan Joong Kim; Nae-Soo Kim; Cheol-Sig Pyo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3637
ER -
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo. (2018). Deep-learning-based pipe leak detection using image-based leak features. IEEE SigPort. http://sigport.org/3637
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo, 2018. Deep-learning-based pipe leak detection using image-based leak features. Available at: http://sigport.org/3637.
Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo. (2018). "Deep-learning-based pipe leak detection using image-based leak features." Web.
1. Doo-Byung Yoon, Se Won OH, Gwan Joong Kim, Nae-Soo Kim, Cheol-Sig Pyo. Deep-learning-based pipe leak detection using image-based leak features [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3637

Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information

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Authors:
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim
Submitted On:
8 October 2018 - 11:18am
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ICIP_DoyeobYeo.pdf

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[1] Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim, "Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3636. Accessed: Dec. 10, 2018.
@article{3636-18,
url = {http://sigport.org/3636},
author = {Ji-Hoon Bae; Junho Yim; Nae-Soo Kim; Cheol-Sig Pyo; Junmo Kim },
publisher = {IEEE SigPort},
title = {Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information},
year = {2018} }
TY - EJOUR
T1 - Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information
AU - Ji-Hoon Bae; Junho Yim; Nae-Soo Kim; Cheol-Sig Pyo; Junmo Kim
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3636
ER -
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim. (2018). Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information. IEEE SigPort. http://sigport.org/3636
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim, 2018. Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information. Available at: http://sigport.org/3636.
Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim. (2018). "Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information." Web.
1. Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, Junmo Kim. Sequential Knowledge Transfer in Teacher-Student Framework using Densely Distilled Flow-Base Information [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3636

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


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 D. Balomenos, Victoria Stefanou, Elias S. Manolakos
Submitted On:
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: Dec. 10, 2018.
@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

Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons


This paper presents the software architecture of a robotic museum guide application called CUMA.
It is intended to run upon the Pepper robotic platform and has the objective of guiding visitors of a museum accompanying them in the tour, explaining museum works, and interacting with them in order to gather feedback.

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Authors:
Francesco Alessandro, Dario Allegra, Filippo Stanco, Corrado Santoro
Submitted On:
8 October 2018 - 9:22am
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2184_presentation_c.pptx

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[1] Francesco Alessandro, Dario Allegra, Filippo Stanco, Corrado Santoro, "Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3633. Accessed: Dec. 10, 2018.
@article{3633-18,
url = {http://sigport.org/3633},
author = {Francesco Alessandro; Dario Allegra; Filippo Stanco; Corrado Santoro },
publisher = {IEEE SigPort},
title = {Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons},
year = {2018} }
TY - EJOUR
T1 - Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons
AU - Francesco Alessandro; Dario Allegra; Filippo Stanco; Corrado Santoro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3633
ER -
Francesco Alessandro, Dario Allegra, Filippo Stanco, Corrado Santoro. (2018). Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons. IEEE SigPort. http://sigport.org/3633
Francesco Alessandro, Dario Allegra, Filippo Stanco, Corrado Santoro, 2018. Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons. Available at: http://sigport.org/3633.
Francesco Alessandro, Dario Allegra, Filippo Stanco, Corrado Santoro. (2018). "Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons." Web.
1. Francesco Alessandro, Dario Allegra, Filippo Stanco, Corrado Santoro. Experiences in using the Pepper Robotic Platform for Museum Assistance Applicatons [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3633

Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition


Fine-grained recognition focuses on the challenging task of automatically identifying the subtle differences between similar categories. Current state-of-the-art approaches require elaborated feature learning procedures, involving tuning several hyper-parameters, or rely on expensive human annotations such as objects or parts location. In this paper we propose a simple method for fine-grained recognition that exploits a nearly cost-free attention-based focus operation to construct an ensemble of increasingly specialized Convolutional Neural Networks.

simonelli.pdf

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Authors:
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'
Submitted On:
8 October 2018 - 9:25am
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[1] Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo', "Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3632. Accessed: Dec. 10, 2018.
@article{3632-18,
url = {http://sigport.org/3632},
author = {Andrea Simonelli; Stefano Messelodi; Francesco De Natale; Samuel Rota Bulo' },
publisher = {IEEE SigPort},
title = {Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition},
year = {2018} }
TY - EJOUR
T1 - Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition
AU - Andrea Simonelli; Stefano Messelodi; Francesco De Natale; Samuel Rota Bulo'
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3632
ER -
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. (2018). Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition. IEEE SigPort. http://sigport.org/3632
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo', 2018. Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition. Available at: http://sigport.org/3632.
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. (2018). "Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition." Web.
1. Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3632

NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM

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Authors:
Thibaud Ehret, Jean-Michel Morel, Pablo Arias
Submitted On:
8 October 2018 - 8:28am
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[1] Thibaud Ehret, Jean-Michel Morel, Pablo Arias, "NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3630. Accessed: Dec. 10, 2018.
@article{3630-18,
url = {http://sigport.org/3630},
author = {Thibaud Ehret; Jean-Michel Morel; Pablo Arias },
publisher = {IEEE SigPort},
title = {NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM},
year = {2018} }
TY - EJOUR
T1 - NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM
AU - Thibaud Ehret; Jean-Michel Morel; Pablo Arias
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3630
ER -
Thibaud Ehret, Jean-Michel Morel, Pablo Arias. (2018). NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM. IEEE SigPort. http://sigport.org/3630
Thibaud Ehret, Jean-Michel Morel, Pablo Arias, 2018. NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM. Available at: http://sigport.org/3630.
Thibaud Ehret, Jean-Michel Morel, Pablo Arias. (2018). "NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM." Web.
1. Thibaud Ehret, Jean-Michel Morel, Pablo Arias. NON-LOCAL KALMAN: A RECURSIVE VIDEO DENOISING ALGORITHM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3630

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