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Image/Video Processing

An Online Algorithm for Constrained Face Clustering in Videos


This is the poster for the ICIP 2018 paper titled "An Online Algorithm for Constrained Face Clustering in Videos".

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7 October 2018 - 12:38pm
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poster for the ICIP 2018 paper titled "An Online Algorithm for Constrained Face Clustering in Videos"

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[1] , "An Online Algorithm for Constrained Face Clustering in Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3594. Accessed: Apr. 24, 2019.
@article{3594-18,
url = {http://sigport.org/3594},
author = { },
publisher = {IEEE SigPort},
title = {An Online Algorithm for Constrained Face Clustering in Videos},
year = {2018} }
TY - EJOUR
T1 - An Online Algorithm for Constrained Face Clustering in Videos
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3594
ER -
. (2018). An Online Algorithm for Constrained Face Clustering in Videos. IEEE SigPort. http://sigport.org/3594
, 2018. An Online Algorithm for Constrained Face Clustering in Videos. Available at: http://sigport.org/3594.
. (2018). "An Online Algorithm for Constrained Face Clustering in Videos." Web.
1. . An Online Algorithm for Constrained Face Clustering in Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3594

THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA


We propose an automatic 3D segmentation algorithm for multiphoton microscopy images of microglia. Our method is capable of segmenting tubular and blob-like structures from noisy images. Current segmentation techniques and software fail to capture the fine processes and soma of the microglia cells, useful for the study of the microglia role in the brain during healthy and diseased states. Our coupled tubularity flow field (TuFF)-blob flow field (BFF) method evolves a level set towards the object boundary using the directional tubularity and blobness measure of 3D images.

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Authors:
Tiffany Ly, Jeremy Thompson, Tajie Harris, and Scott T. Acton
Submitted On:
7 October 2018 - 11:09am
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icip18.pdf

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[1] Tiffany Ly, Jeremy Thompson, Tajie Harris, and Scott T. Acton, "THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3592. Accessed: Apr. 24, 2019.
@article{3592-18,
url = {http://sigport.org/3592},
author = {Tiffany Ly; Jeremy Thompson; Tajie Harris; and Scott T. Acton },
publisher = {IEEE SigPort},
title = {THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA},
year = {2018} }
TY - EJOUR
T1 - THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA
AU - Tiffany Ly; Jeremy Thompson; Tajie Harris; and Scott T. Acton
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3592
ER -
Tiffany Ly, Jeremy Thompson, Tajie Harris, and Scott T. Acton. (2018). THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA. IEEE SigPort. http://sigport.org/3592
Tiffany Ly, Jeremy Thompson, Tajie Harris, and Scott T. Acton, 2018. THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA. Available at: http://sigport.org/3592.
Tiffany Ly, Jeremy Thompson, Tajie Harris, and Scott T. Acton. (2018). "THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA." Web.
1. Tiffany Ly, Jeremy Thompson, Tajie Harris, and Scott T. Acton. THE COUPLED TUFF-BFF ALGORITHM FOR AUTOMATIC 3D SEGMENTATION OF MICROGLIA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3592

SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING


Activities such as those involved in food preparation involve interactions between hands, tools and multiple manipulated objects that affect them in visually complex ways making recognition of their constituent actions challenging. We describe a system that classifies action classes in such a setting based on discriminative spatio-temporal superpixel groups. The entire system operates sequentially enabling online action recognition. We obtain state-of-the-art results whilst employing a compact, interpretable representation.

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Authors:
Stephen McKenna
Submitted On:
7 October 2018 - 9:23am
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HuangMcKennaICIP2018.pdf

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[1] Stephen McKenna, "SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3591. Accessed: Apr. 24, 2019.
@article{3591-18,
url = {http://sigport.org/3591},
author = {Stephen McKenna },
publisher = {IEEE SigPort},
title = {SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING},
year = {2018} }
TY - EJOUR
T1 - SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING
AU - Stephen McKenna
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3591
ER -
Stephen McKenna. (2018). SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING. IEEE SigPort. http://sigport.org/3591
Stephen McKenna, 2018. SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING. Available at: http://sigport.org/3591.
Stephen McKenna. (2018). "SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING." Web.
1. Stephen McKenna. SEQUENTIAL RECOGNITION OF MANIPULATION ACTIONS USING DISCRIMINATIVE SUPERPIXEL GROUP MINING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3591

SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT


Optical imaging delivers absolute, non-contact, and high-dynamic-range measurement of thermal expansion. However, to achieve high accuracy, various factors should be accounted within the image analysis, including: image spatial sampling, lens aberrations, brightness nonuniformity and object edge deformations. Approach based on the object contour reconstruction is presented. Measurement procedure consists of two stages. Firstly, object edge contours corresponding to different temperatures are estimated.

Paper Details

Authors:
S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov
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7 October 2018 - 5:52am
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poster.pdf

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[1] S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov, "SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3589. Accessed: Apr. 24, 2019.
@article{3589-18,
url = {http://sigport.org/3589},
author = {S. K. Kruglov; I. G. Bronshtein; T. A. Kompan; S. V. Kondratiev; A. S. Korenev; N. F. Puhov },
publisher = {IEEE SigPort},
title = {SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT},
year = {2018} }
TY - EJOUR
T1 - SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT
AU - S. K. Kruglov; I. G. Bronshtein; T. A. Kompan; S. V. Kondratiev; A. S. Korenev; N. F. Puhov
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3589
ER -
S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov. (2018). SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT. IEEE SigPort. http://sigport.org/3589
S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov, 2018. SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT. Available at: http://sigport.org/3589.
S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov. (2018). "SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT." Web.
1. S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov. SUPERRESOLUTION CONTOUR RECONSTRUCTION APPROACH TO A LINEAR THERMAL EXPANSION MEASUREMENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3589

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

Paper Details

Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
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Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3587. Accessed: Apr. 24, 2019.
@article{3587-18,
url = {http://sigport.org/3587},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3587
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3587
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3587.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3587

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

Paper Details

Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
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Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3585. Accessed: Apr. 24, 2019.
@article{3585-18,
url = {http://sigport.org/3585},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3585
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3585
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3585.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3585

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

Paper Details

Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3584. Accessed: Apr. 24, 2019.
@article{3584-18,
url = {http://sigport.org/3584},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3584
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3584
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3584.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3584

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

Paper Details

Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3583. Accessed: Apr. 24, 2019.
@article{3583-18,
url = {http://sigport.org/3583},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3583
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3583
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3583.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3583

DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT


Visual tracking frameworks employing Convolutional Neural Networks (CNNs) have shown state-of-the-art performance due to their hierarchical feature representation. While classification and update based deep neural net tracking have shown good performance in terms of accuracy, they have poor tracking speed. On the other hand, recent matching based techniques using CNNs show higher than real-time speed in tracking but this speed is achieved at a considerably lower accuracy.

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Submitted On:
6 October 2018 - 9:51pm
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poster_ICIP2.pdf

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[1] , "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3581. Accessed: Apr. 24, 2019.
@article{3581-18,
url = {http://sigport.org/3581},
author = { },
publisher = {IEEE SigPort},
title = {DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT},
year = {2018} }
TY - EJOUR
T1 - DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3581
ER -
. (2018). DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT. IEEE SigPort. http://sigport.org/3581
, 2018. DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT. Available at: http://sigport.org/3581.
. (2018). "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT." Web.
1. . DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3581

Parallel Mean Shift Accuracy and Performance Trade-Offs

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Authors:
Kirsty Duncan, Robert Stewart, Greg Michaelson
Submitted On:
6 October 2018 - 5:15am
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Parallel_Mean_Shift_Accuracy_and_Performance_Trade-Offs.pdf

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[1] Kirsty Duncan, Robert Stewart, Greg Michaelson, "Parallel Mean Shift Accuracy and Performance Trade-Offs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3570. Accessed: Apr. 24, 2019.
@article{3570-18,
url = {http://sigport.org/3570},
author = {Kirsty Duncan; Robert Stewart; Greg Michaelson },
publisher = {IEEE SigPort},
title = {Parallel Mean Shift Accuracy and Performance Trade-Offs},
year = {2018} }
TY - EJOUR
T1 - Parallel Mean Shift Accuracy and Performance Trade-Offs
AU - Kirsty Duncan; Robert Stewart; Greg Michaelson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3570
ER -
Kirsty Duncan, Robert Stewart, Greg Michaelson. (2018). Parallel Mean Shift Accuracy and Performance Trade-Offs. IEEE SigPort. http://sigport.org/3570
Kirsty Duncan, Robert Stewart, Greg Michaelson, 2018. Parallel Mean Shift Accuracy and Performance Trade-Offs. Available at: http://sigport.org/3570.
Kirsty Duncan, Robert Stewart, Greg Michaelson. (2018). "Parallel Mean Shift Accuracy and Performance Trade-Offs." Web.
1. Kirsty Duncan, Robert Stewart, Greg Michaelson. Parallel Mean Shift Accuracy and Performance Trade-Offs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3570

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