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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.

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: Oct. 19, 2018.
@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

IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER


Using detector arrays can speed up lidar systems by parallelizing acquisition.
However, current SPAD arrays have time bins longer than
typical laser pulse durations, resulting in measurement errors dominated
by quantization. We propose an optical time-of-flight system
that uses subtractive dither to improve image depth resolution.
Modeling the measurement noise with a generalized Gaussian distribution
further improves estimation error in simulations, although
model mismatch prevents the same advantage for our experimental

Paper Details

Authors:
Joshua Rapp, Robin M. A. Dawson, Vivek K Goyal
Submitted On:
7 October 2018 - 11:39am
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Poster PDF

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[1] Joshua Rapp, Robin M. A. Dawson, Vivek K Goyal, "IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3593. Accessed: Oct. 19, 2018.
@article{3593-18,
url = {http://sigport.org/3593},
author = {Joshua Rapp; Robin M. A. Dawson; Vivek K Goyal },
publisher = {IEEE SigPort},
title = {IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER},
year = {2018} }
TY - EJOUR
T1 - IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER
AU - Joshua Rapp; Robin M. A. Dawson; Vivek K Goyal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3593
ER -
Joshua Rapp, Robin M. A. Dawson, Vivek K Goyal. (2018). IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER. IEEE SigPort. http://sigport.org/3593
Joshua Rapp, Robin M. A. Dawson, Vivek K Goyal, 2018. IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER. Available at: http://sigport.org/3593.
Joshua Rapp, Robin M. A. Dawson, Vivek K Goyal. (2018). "IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER." Web.
1. Joshua Rapp, Robin M. A. Dawson, Vivek K Goyal. IMPROVING LIDAR DEPTH RESOLUTION WITH DITHER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3593

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.

icip18.pdf

PDF icon icip18.pdf (6 downloads)

Paper Details

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: Oct. 19, 2018.
@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.

Paper Details

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: Oct. 19, 2018.
@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

IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX


This paper presents a simple yet effective method to improve the visual quality of Generative Adversarial Network (GAN) generated images. In typical GAN architectures, the discriminator block is designed mainly to capture the class-specific content from images without explicitly imposing constraints on the visual quality of the generated images. A key insight from the image quality assessment literature is that natural scenes possess a very unique local structural and (hence) statistical signature, and that distortions affect this signature.

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Submitted On:
7 October 2018 - 6:01am
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ICIP_2018_BEGAN.pdf

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[1] , "IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3590. Accessed: Oct. 19, 2018.
@article{3590-18,
url = {http://sigport.org/3590},
author = { },
publisher = {IEEE SigPort},
title = {IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX},
year = {2018} }
TY - EJOUR
T1 - IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3590
ER -
. (2018). IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX. IEEE SigPort. http://sigport.org/3590
, 2018. IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX. Available at: http://sigport.org/3590.
. (2018). "IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX." Web.
1. . IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3590

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.

poster.pdf

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Paper Details

Authors:
S. K. Kruglov, I. G. Bronshtein, T. A. Kompan, S. V. Kondratiev, A. S. Korenev, N. F. Puhov
Submitted On:
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: Oct. 19, 2018.
@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

IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING


Fake news and deep fakes have been making social and mainstream media headlines. At the same time, engaged scientists strive for find- ing ways to detect forgeries and suspicious manipulations using even the subtlest clues. In this vein, this work proposes a new method for detecting photographic splicing by bringing together the high repre- sentation power of Illuminant Maps and Convolutional Neural Net- works as a way of learning directly from available training data the most important hints of a forgery.

Paper Details

Authors:
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho
Submitted On:
7 October 2018 - 5:48am
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ICIP-Poster-V2.pdf

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[1] Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho, "IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3588. Accessed: Oct. 19, 2018.
@article{3588-18,
url = {http://sigport.org/3588},
author = {Thales Pomari; Guillherme Ruppert; Edmar Rezende; Anderson Rocha; Tiago Carvalho },
publisher = {IEEE SigPort},
title = {IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING},
year = {2018} }
TY - EJOUR
T1 - IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING
AU - Thales Pomari; Guillherme Ruppert; Edmar Rezende; Anderson Rocha; Tiago Carvalho
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3588
ER -
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho. (2018). IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING. IEEE SigPort. http://sigport.org/3588
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho, 2018. IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING. Available at: http://sigport.org/3588.
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho. (2018). "IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING." Web.
1. Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho. IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3588

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: Oct. 19, 2018.
@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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

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Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

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Keywords

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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: Oct. 19, 2018.
@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

(10 downloads)

Keywords

Additional Categories

Subscribe

[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3584. Accessed: Oct. 19, 2018.
@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

Pages