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Image/Video Processing

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
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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: Jul. 20, 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.

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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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[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: Jul. 20, 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.

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Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
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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: Jul. 20, 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.

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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: Jul. 20, 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:
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Cite

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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/3584. Accessed: Jul. 20, 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:
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Cite

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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/3583. Accessed: Jul. 20, 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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[1] , "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3581. Accessed: Jul. 20, 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
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6 October 2018 - 5:15am
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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: Jul. 20, 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

Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos


We focus on first-person action recognition from egocentric videos. Unlike third person domain, researchers have divided first-person actions into two categories: involving hand-object interactions and the ones without, and developed separate techniques for the two action categories. Further, it has been argued that traditional cues used for third person action recognition do not suffice, and egocentric specific features, such as head motion and handled objects have been used for such actions.

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Authors:
Pravin Nagar, Divam Gupta, Chetan Arora
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6 October 2018 - 4:37am
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Unified approach of recognizing multiple first-person action categories.

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[1] Pravin Nagar, Divam Gupta, Chetan Arora, "Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3569. Accessed: Jul. 20, 2019.
@article{3569-18,
url = {http://sigport.org/3569},
author = {Pravin Nagar; Divam Gupta; Chetan Arora },
publisher = {IEEE SigPort},
title = {Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos},
year = {2018} }
TY - EJOUR
T1 - Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos
AU - Pravin Nagar; Divam Gupta; Chetan Arora
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3569
ER -
Pravin Nagar, Divam Gupta, Chetan Arora. (2018). Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos. IEEE SigPort. http://sigport.org/3569
Pravin Nagar, Divam Gupta, Chetan Arora, 2018. Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos. Available at: http://sigport.org/3569.
Pravin Nagar, Divam Gupta, Chetan Arora. (2018). "Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos." Web.
1. Pravin Nagar, Divam Gupta, Chetan Arora. Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3569

CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION


Computer-aided diagnosis (CAD) has great potential in providing real benefits to doctors and patients. Recent studies have, however, found lack of trust in CAD by radiologists in clinical diagnostic decision making. One of the main reasons is the lack of an appropriate confidence measure. This paper presents the first-ever study of classification confidence in the context of breast mass classification.

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Authors:
Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, Reyer Zwiggelaar
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6 October 2018 - 3:12am
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Paper 1991: Confidence Analysis for Breast Mass Image Classification

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[1] Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, Reyer Zwiggelaar, "CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3566. Accessed: Jul. 20, 2019.
@article{3566-18,
url = {http://sigport.org/3566},
author = {Andrik Rampun; Hui Wang; Bryan Scotney; Philip Morrow; Reyer Zwiggelaar },
publisher = {IEEE SigPort},
title = {CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION},
year = {2018} }
TY - EJOUR
T1 - CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION
AU - Andrik Rampun; Hui Wang; Bryan Scotney; Philip Morrow; Reyer Zwiggelaar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3566
ER -
Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, Reyer Zwiggelaar. (2018). CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION. IEEE SigPort. http://sigport.org/3566
Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, Reyer Zwiggelaar, 2018. CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION. Available at: http://sigport.org/3566.
Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, Reyer Zwiggelaar. (2018). "CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION." Web.
1. Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, Reyer Zwiggelaar. CONFIDENCE ANALYSIS FOR BREAST MASS IMAGE CLASSIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3566

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