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Bioimaging and microscopy

WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING


Brain tumor can be a fatal disease in the world. With the aim of improving survival rates, many computerized algorithms have been proposed to assist the pathologists to make a diagnosis, using Whole Slide Pathology Images (WSI). Most methods focus on performing patch-level classification and aggregating the patch-level results to obtain the image classification. Since not all patches carry diagnostic information, it is thus important for our algorithm to recognize discriminative and non-discriminative patches.

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Authors:
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai
Submitted On:
9 October 2018 - 8:39am
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poster

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[1] Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai, "WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3654. Accessed: Nov. 21, 2018.
@article{3654-18,
url = {http://sigport.org/3654},
author = {Chaoyi Zhang; Yang Song; Donghao Zhang; Sidong Liu; Mei Chen; Weidong Cai },
publisher = {IEEE SigPort},
title = {WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING},
year = {2018} }
TY - EJOUR
T1 - WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING
AU - Chaoyi Zhang; Yang Song; Donghao Zhang; Sidong Liu; Mei Chen; Weidong Cai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3654
ER -
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. (2018). WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING. IEEE SigPort. http://sigport.org/3654
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai, 2018. WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING. Available at: http://sigport.org/3654.
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. (2018). "WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING." Web.
1. Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3654

SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK


We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches.

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Authors:
Michael Liebling
Submitted On:
6 October 2018 - 8:16pm
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AS_ICIP2018_Poster_horiz.pdf

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[1] Michael Liebling, "SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3580. Accessed: Nov. 21, 2018.
@article{3580-18,
url = {http://sigport.org/3580},
author = {Michael Liebling },
publisher = {IEEE SigPort},
title = {SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK
AU - Michael Liebling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3580
ER -
Michael Liebling. (2018). SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK. IEEE SigPort. http://sigport.org/3580
Michael Liebling, 2018. SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK. Available at: http://sigport.org/3580.
Michael Liebling. (2018). "SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK." Web.
1. Michael Liebling. SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3580

Radial function based ab-initio tomographic reconstruction for cryo electron microscopy

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Authors:
Yves Michels, Etienne Baudrier, Loic Mazo
Submitted On:
5 October 2018 - 5:32am
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Poster2018.pdf

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[1] Yves Michels, Etienne Baudrier, Loic Mazo, "Radial function based ab-initio tomographic reconstruction for cryo electron microscopy", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3527. Accessed: Nov. 21, 2018.
@article{3527-18,
url = {http://sigport.org/3527},
author = {Yves Michels; Etienne Baudrier; Loic Mazo },
publisher = {IEEE SigPort},
title = {Radial function based ab-initio tomographic reconstruction for cryo electron microscopy},
year = {2018} }
TY - EJOUR
T1 - Radial function based ab-initio tomographic reconstruction for cryo electron microscopy
AU - Yves Michels; Etienne Baudrier; Loic Mazo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3527
ER -
Yves Michels, Etienne Baudrier, Loic Mazo. (2018). Radial function based ab-initio tomographic reconstruction for cryo electron microscopy. IEEE SigPort. http://sigport.org/3527
Yves Michels, Etienne Baudrier, Loic Mazo, 2018. Radial function based ab-initio tomographic reconstruction for cryo electron microscopy. Available at: http://sigport.org/3527.
Yves Michels, Etienne Baudrier, Loic Mazo. (2018). "Radial function based ab-initio tomographic reconstruction for cryo electron microscopy." Web.
1. Yves Michels, Etienne Baudrier, Loic Mazo. Radial function based ab-initio tomographic reconstruction for cryo electron microscopy [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3527

CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING


We present a region based method for segmenting and splitting
images of cells in an automatic and unsupervised manner.
The detection of cell nuclei is based on the Bradley’s method.
False positives are automatically identified and rejected based
on shape and intensity features. Additionally, the proposed
method is able to automatically detect and split touching cells.
To do so, we employ a variant of a region based multi-ellipse
fitting method (DEFA) that makes use of constraints on the

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Authors:
Costas Panagiotakis, Antonis A. Argyros
Submitted On:
5 October 2018 - 3:50am
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CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING

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[1] Costas Panagiotakis, Antonis A. Argyros, "CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3514. Accessed: Nov. 21, 2018.
@article{3514-18,
url = {http://sigport.org/3514},
author = {Costas Panagiotakis; Antonis A. Argyros },
publisher = {IEEE SigPort},
title = {CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING},
year = {2018} }
TY - EJOUR
T1 - CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING
AU - Costas Panagiotakis; Antonis A. Argyros
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3514
ER -
Costas Panagiotakis, Antonis A. Argyros. (2018). CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING. IEEE SigPort. http://sigport.org/3514
Costas Panagiotakis, Antonis A. Argyros, 2018. CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING. Available at: http://sigport.org/3514.
Costas Panagiotakis, Antonis A. Argyros. (2018). "CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING." Web.
1. Costas Panagiotakis, Antonis A. Argyros. CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3514

OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD


This work proposes a volumetric data restoration method, especially for data acquired through an optical coherence tomography (OCT) device. OCT is a technique for acquiring a tomographic image of a specimen object in a few $\mu$m scale by using a near infrared laser. The authors have been trying dynamic observation of epithelium in cochlear of the inner ear. Currently, there is a problem to remove the influence of the measurement process as well as noise due to image sensor sensitivity.

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19 April 2018 - 1:07pm
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oct-volumetric-data.pdf

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[1] , "OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2983. Accessed: Nov. 21, 2018.
@article{2983-18,
url = {http://sigport.org/2983},
author = { },
publisher = {IEEE SigPort},
title = {OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD},
year = {2018} }
TY - EJOUR
T1 - OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2983
ER -
. (2018). OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD. IEEE SigPort. http://sigport.org/2983
, 2018. OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD. Available at: http://sigport.org/2983.
. (2018). "OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD." Web.
1. . OCT VOLUMETRIC DATA RESTORATION VIA PRIMAL-DUAL PLUG-AND-PLAY METHOD [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2983

BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS


In microscopy, new super-resolution methods are emerging that produce three-dimensional images at resolutions ten times smaller than that provided by traditional light microscopy. Such technology is enabling the exploration of structure and function in living tissues such as bacterial biofilms that have mysterious interconnections and organization. Unfortunately, the standard tools used in the image analysis community to perform segmentation and other higher-level analyses cannot be applied naively to these data.

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Authors:
R. Sarkar, A. Aziz, A. Vaccari, A. Gahlmann and S. T. Acton
Submitted On:
16 September 2017 - 9:24pm
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ICIP 2017 BACT-3D

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[1] R. Sarkar, A. Aziz, A. Vaccari, A. Gahlmann and S. T. Acton, "BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2202. Accessed: Nov. 21, 2018.
@article{2202-17,
url = {http://sigport.org/2202},
author = {R. Sarkar; A. Aziz; A. Vaccari; A. Gahlmann and S. T. Acton },
publisher = {IEEE SigPort},
title = {BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS},
year = {2017} }
TY - EJOUR
T1 - BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS
AU - R. Sarkar; A. Aziz; A. Vaccari; A. Gahlmann and S. T. Acton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2202
ER -
R. Sarkar, A. Aziz, A. Vaccari, A. Gahlmann and S. T. Acton. (2017). BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS. IEEE SigPort. http://sigport.org/2202
R. Sarkar, A. Aziz, A. Vaccari, A. Gahlmann and S. T. Acton, 2017. BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS. Available at: http://sigport.org/2202.
R. Sarkar, A. Aziz, A. Vaccari, A. Gahlmann and S. T. Acton. (2017). "BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS." Web.
1. R. Sarkar, A. Aziz, A. Vaccari, A. Gahlmann and S. T. Acton. BACT-3D: A LEVEL SET SEGMENTATION APPROACH FOR DENSE MULTI-LAYERED 3D BACTERIAL BIOFILMS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2202

Blur Estimation for Photon-Limited Images


Blur estimation is critical to blind image deconvolution. In this work, by taking Gaussian kernel as an example, we propose an approach to estimate the blur size for photon-limited images. This estimation is based on the minimization of a novel criterion, blur-PURE (Poisson unbiased risk estimate), which makes use of the Poisson noise statistics of the measurement. Experimental results demonstrate the effectiveness of the proposed method in various scenarios.

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Authors:
Jizhou Li, Feng Xue, and Thierry Blu
Submitted On:
14 September 2017 - 5:00am
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Slides_blurPURE_ICIP17.pdf

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[1] Jizhou Li, Feng Xue, and Thierry Blu, "Blur Estimation for Photon-Limited Images", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2001. Accessed: Nov. 21, 2018.
@article{2001-17,
url = {http://sigport.org/2001},
author = {Jizhou Li; Feng Xue; and Thierry Blu },
publisher = {IEEE SigPort},
title = {Blur Estimation for Photon-Limited Images},
year = {2017} }
TY - EJOUR
T1 - Blur Estimation for Photon-Limited Images
AU - Jizhou Li; Feng Xue; and Thierry Blu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2001
ER -
Jizhou Li, Feng Xue, and Thierry Blu. (2017). Blur Estimation for Photon-Limited Images. IEEE SigPort. http://sigport.org/2001
Jizhou Li, Feng Xue, and Thierry Blu, 2017. Blur Estimation for Photon-Limited Images. Available at: http://sigport.org/2001.
Jizhou Li, Feng Xue, and Thierry Blu. (2017). "Blur Estimation for Photon-Limited Images." Web.
1. Jizhou Li, Feng Xue, and Thierry Blu. Blur Estimation for Photon-Limited Images [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2001

A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images


Significant cardiac and respiratory motion of the living subject, occasional spells of defocus, drifts in the field of view,
and long image sequences make the registration of in-vivo microscopy image sequences used in atherosclerosis study an
onerous task. In this study we developed and implemented a novel Minimum Spanning Tree (MST)-based clustering
method for image sequence registration that first constructs a minimum spanning tree for the input image sequence. The

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Authors:
Nilanjan Ray, Sara McArdle, and Klaus Ley
Submitted On:
11 September 2017 - 3:35pm
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ICIP2017_Registration.pdf

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[1] Nilanjan Ray, Sara McArdle, and Klaus Ley, "A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1916. Accessed: Nov. 21, 2018.
@article{1916-17,
url = {http://sigport.org/1916},
author = {Nilanjan Ray; Sara McArdle; and Klaus Ley },
publisher = {IEEE SigPort},
title = {A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images},
year = {2017} }
TY - EJOUR
T1 - A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images
AU - Nilanjan Ray; Sara McArdle; and Klaus Ley
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1916
ER -
Nilanjan Ray, Sara McArdle, and Klaus Ley. (2017). A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images. IEEE SigPort. http://sigport.org/1916
Nilanjan Ray, Sara McArdle, and Klaus Ley, 2017. A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images. Available at: http://sigport.org/1916.
Nilanjan Ray, Sara McArdle, and Klaus Ley. (2017). "A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images." Web.
1. Nilanjan Ray, Sara McArdle, and Klaus Ley. A Two-Stage Minimum Spanning Tree (MST) based Clustering Algorithm for 2D Deformable Registration of Time Sequenced Images [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1916

IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS


Neurons depend critically on active transport of cargoes throughout their complex neurite networks for their survival and function. Defects in this process have been strongly associated with many human neurodevelopmental and neurodegenerative diseases. To understand related neuronal physiology and disease mechanisms, it is essential to measure the traffic flow within the neurite networks. Currently, however, image analysis methods required for this measurement are lacking.

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Authors:
Douglas Qian, Qinle Ba, Angran Li, Jessica Zhang, Ge Yang
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8 September 2017 - 4:17pm
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ICIP_poster_XiaoqiChai.pdf

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[1] Douglas Qian, Qinle Ba, Angran Li, Jessica Zhang, Ge Yang, "IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1878. Accessed: Nov. 21, 2018.
@article{1878-17,
url = {http://sigport.org/1878},
author = {Douglas Qian; Qinle Ba; Angran Li; Jessica Zhang; Ge Yang },
publisher = {IEEE SigPort},
title = {IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS},
year = {2017} }
TY - EJOUR
T1 - IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS
AU - Douglas Qian; Qinle Ba; Angran Li; Jessica Zhang; Ge Yang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1878
ER -
Douglas Qian, Qinle Ba, Angran Li, Jessica Zhang, Ge Yang. (2017). IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS. IEEE SigPort. http://sigport.org/1878
Douglas Qian, Qinle Ba, Angran Li, Jessica Zhang, Ge Yang, 2017. IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS. Available at: http://sigport.org/1878.
Douglas Qian, Qinle Ba, Angran Li, Jessica Zhang, Ge Yang. (2017). "IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS." Web.
1. Douglas Qian, Qinle Ba, Angran Li, Jessica Zhang, Ge Yang. IMAGE-BASED MEASUREMENT OF CARGO TRAFFIC FLOW IN COMPLEX NEURITE NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1878

A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction

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7 September 2017 - 9:06am
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Bourdonp_ICIP2017_2017_08_28.pdf

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[1] , "A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1853. Accessed: Nov. 21, 2018.
@article{1853-17,
url = {http://sigport.org/1853},
author = { },
publisher = {IEEE SigPort},
title = {A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction},
year = {2017} }
TY - EJOUR
T1 - A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1853
ER -
. (2017). A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction. IEEE SigPort. http://sigport.org/1853
, 2017. A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction. Available at: http://sigport.org/1853.
. (2017). "A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction." Web.
1. . A data-driven approach to feature space selection for robust micro-endoscopic image reconstruction [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1853

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