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Image, Video, and Multidimensional Signal Processing

Multiple View Image Denoising Using 3D Focus Image Stacks


In this work, we propose an improved fast multiple-view image denoising algorithm using 3D focus image stacks. It showed improved computational efficiency and comparable denoising quality compared to conventional methods.

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Authors:
Yu Hen Hu, Hongrui Jiang
Submitted On:
23 February 2016 - 1:44pm
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Multiple View Image Denoising.pdf

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[1] Yu Hen Hu, Hongrui Jiang, "Multiple View Image Denoising Using 3D Focus Image Stacks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/286. Accessed: Oct. 24, 2017.
@article{286-15,
url = {http://sigport.org/286},
author = {Yu Hen Hu; Hongrui Jiang },
publisher = {IEEE SigPort},
title = {Multiple View Image Denoising Using 3D Focus Image Stacks},
year = {2015} }
TY - EJOUR
T1 - Multiple View Image Denoising Using 3D Focus Image Stacks
AU - Yu Hen Hu; Hongrui Jiang
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/286
ER -
Yu Hen Hu, Hongrui Jiang. (2015). Multiple View Image Denoising Using 3D Focus Image Stacks. IEEE SigPort. http://sigport.org/286
Yu Hen Hu, Hongrui Jiang, 2015. Multiple View Image Denoising Using 3D Focus Image Stacks. Available at: http://sigport.org/286.
Yu Hen Hu, Hongrui Jiang. (2015). "Multiple View Image Denoising Using 3D Focus Image Stacks." Web.
1. Yu Hen Hu, Hongrui Jiang. Multiple View Image Denoising Using 3D Focus Image Stacks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/286

Computer Vision and Image Processing for Automated Surveillance


Presentation slides covering:

- robust foreground detection / background subtraction via patch-based analysis
- person re-identification based on representations on Riemannian manifolds
- robust object tracking via Grassmann manifolds
- adapting the lessons from big data to computer vision
- future paradigm shifts: computer vision based on networks of neurosynaptic cores

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Submitted On:
23 February 2016 - 1:43pm
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Sanderson_Computer_Vision_and_Image_Processing_for_Automated_Surveillance.pdf

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[1] , "Computer Vision and Image Processing for Automated Surveillance", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/202. Accessed: Oct. 24, 2017.
@article{202-15,
url = {http://sigport.org/202},
author = { },
publisher = {IEEE SigPort},
title = {Computer Vision and Image Processing for Automated Surveillance},
year = {2015} }
TY - EJOUR
T1 - Computer Vision and Image Processing for Automated Surveillance
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/202
ER -
. (2015). Computer Vision and Image Processing for Automated Surveillance. IEEE SigPort. http://sigport.org/202
, 2015. Computer Vision and Image Processing for Automated Surveillance. Available at: http://sigport.org/202.
. (2015). "Computer Vision and Image Processing for Automated Surveillance." Web.
1. . Computer Vision and Image Processing for Automated Surveillance [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/202

Object Tracking and Person Re-Identification on Manifolds


person re-identification examples

Slides from the Tutorial on Riemannian Geometry in Computer Vision, presented at the Asian Conference on Computer Vision (ACCV), Singapore, 2014.

The slides show (1) how objects can be interpreted as points on Riemannian and Grassmann manifolds, and (2) various distance measures on manifolds. Demonstrates usefulness of manifold techniques in applications such as object tracking and person re-identification.

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23 February 2016 - 1:43pm
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sanderson_riemannian_geometry_tutorial_slides_accv_2014.pdf

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[1] , "Object Tracking and Person Re-Identification on Manifolds", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/198. Accessed: Oct. 24, 2017.
@article{198-15,
url = {http://sigport.org/198},
author = { },
publisher = {IEEE SigPort},
title = {Object Tracking and Person Re-Identification on Manifolds},
year = {2015} }
TY - EJOUR
T1 - Object Tracking and Person Re-Identification on Manifolds
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/198
ER -
. (2015). Object Tracking and Person Re-Identification on Manifolds. IEEE SigPort. http://sigport.org/198
, 2015. Object Tracking and Person Re-Identification on Manifolds. Available at: http://sigport.org/198.
. (2015). "Object Tracking and Person Re-Identification on Manifolds." Web.
1. . Object Tracking and Person Re-Identification on Manifolds [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/198

A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts


For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. Image sequences are analysed on a block-by-block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line.

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Authors:
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
Submitted On:
23 February 2016 - 1:43pm
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cluttered_background_estimation.pdf

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[1] Vikas Reddy, Conrad Sanderson, Brian C. Lovell, "A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/190. Accessed: Oct. 24, 2017.
@article{190-15,
url = {http://sigport.org/190},
author = {Vikas Reddy; Conrad Sanderson; Brian C. Lovell },
publisher = {IEEE SigPort},
title = {A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts},
year = {2015} }
TY - EJOUR
T1 - A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts
AU - Vikas Reddy; Conrad Sanderson; Brian C. Lovell
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/190
ER -
Vikas Reddy, Conrad Sanderson, Brian C. Lovell. (2015). A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts. IEEE SigPort. http://sigport.org/190
Vikas Reddy, Conrad Sanderson, Brian C. Lovell, 2015. A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts. Available at: http://sigport.org/190.
Vikas Reddy, Conrad Sanderson, Brian C. Lovell. (2015). "A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts." Web.
1. Vikas Reddy, Conrad Sanderson, Brian C. Lovell. A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/190

Article Summaries - SPM Special Issue on Signal Processing for Art Investigation


Expanded version of the Guest Editorial
for Special Issue on Signal Processing for Art Investigation
(IEEE Signal Processing Magazine, July 2015)

Include short summaries for each of the 11 articles in the special issue.

Paper Details

Authors:
Patrice Abry, Andrew. G. Klein, William A. Sethares, C. Richard Johnson, Jr.
Submitted On:
23 February 2016 - 1:43pm
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Type:

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ArtForensics_Editorial1507Long.pdf

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[1] Patrice Abry, Andrew. G. Klein, William A. Sethares, C. Richard Johnson, Jr., "Article Summaries - SPM Special Issue on Signal Processing for Art Investigation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/189. Accessed: Oct. 24, 2017.
@article{189-15,
url = {http://sigport.org/189},
author = {Patrice Abry; Andrew. G. Klein; William A. Sethares; C. Richard Johnson; Jr. },
publisher = {IEEE SigPort},
title = {Article Summaries - SPM Special Issue on Signal Processing for Art Investigation},
year = {2015} }
TY - EJOUR
T1 - Article Summaries - SPM Special Issue on Signal Processing for Art Investigation
AU - Patrice Abry; Andrew. G. Klein; William A. Sethares; C. Richard Johnson; Jr.
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/189
ER -
Patrice Abry, Andrew. G. Klein, William A. Sethares, C. Richard Johnson, Jr.. (2015). Article Summaries - SPM Special Issue on Signal Processing for Art Investigation. IEEE SigPort. http://sigport.org/189
Patrice Abry, Andrew. G. Klein, William A. Sethares, C. Richard Johnson, Jr., 2015. Article Summaries - SPM Special Issue on Signal Processing for Art Investigation. Available at: http://sigport.org/189.
Patrice Abry, Andrew. G. Klein, William A. Sethares, C. Richard Johnson, Jr.. (2015). "Article Summaries - SPM Special Issue on Signal Processing for Art Investigation." Web.
1. Patrice Abry, Andrew. G. Klein, William A. Sethares, C. Richard Johnson, Jr.. Article Summaries - SPM Special Issue on Signal Processing for Art Investigation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/189

Bags of Affine Subspaces for Robust Object Tracking


object tracking results

We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces.

report.pdf

PDF icon report.pdf (593 downloads)

Paper Details

Authors:
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi
Submitted On:
23 February 2016 - 1:43pm
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report.pdf

(593 downloads)

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[1] Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi, "Bags of Affine Subspaces for Robust Object Tracking", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/185. Accessed: Oct. 24, 2017.
@article{185-15,
url = {http://sigport.org/185},
author = {Sareh Shirazi; Conrad Sanderson; Chris McCool; Mehrtash Harandi },
publisher = {IEEE SigPort},
title = {Bags of Affine Subspaces for Robust Object Tracking},
year = {2015} }
TY - EJOUR
T1 - Bags of Affine Subspaces for Robust Object Tracking
AU - Sareh Shirazi; Conrad Sanderson; Chris McCool; Mehrtash Harandi
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/185
ER -
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. (2015). Bags of Affine Subspaces for Robust Object Tracking. IEEE SigPort. http://sigport.org/185
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi, 2015. Bags of Affine Subspaces for Robust Object Tracking. Available at: http://sigport.org/185.
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. (2015). "Bags of Affine Subspaces for Robust Object Tracking." Web.
1. Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. Bags of Affine Subspaces for Robust Object Tracking [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/185

Reduced-Reference Structural Quality Assessment for Retargeted Images


Recent years have witnessed tremendous growth in the generation and consumption of digital images. Monitoring and evaluating image quality is an important issue for online and mobile media applications. Conventional quality assessment work mostly focus on intensity level distortion caused by operations that do not change image aspect ratio/size, such as distortion caused by compression, noise, and blurring. Here, we study the problem of quality assessment for images undergone content-adaptive resizing, also known as retargeting operations.

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23 February 2016 - 1:43pm
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WongLuWu2014_TIP_Quality.pdf

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[1] , "Reduced-Reference Structural Quality Assessment for Retargeted Images", IEEE SigPort, 2014. [Online]. Available: http://sigport.org/22. Accessed: Oct. 24, 2017.
@article{22-14,
url = {http://sigport.org/22},
author = { },
publisher = {IEEE SigPort},
title = {Reduced-Reference Structural Quality Assessment for Retargeted Images},
year = {2014} }
TY - EJOUR
T1 - Reduced-Reference Structural Quality Assessment for Retargeted Images
AU -
PY - 2014
PB - IEEE SigPort
UR - http://sigport.org/22
ER -
. (2014). Reduced-Reference Structural Quality Assessment for Retargeted Images. IEEE SigPort. http://sigport.org/22
, 2014. Reduced-Reference Structural Quality Assessment for Retargeted Images. Available at: http://sigport.org/22.
. (2014). "Reduced-Reference Structural Quality Assessment for Retargeted Images." Web.
1. . Reduced-Reference Structural Quality Assessment for Retargeted Images [Internet]. IEEE SigPort; 2014. Available from : http://sigport.org/22

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