Sorry, you need to enable JavaScript to visit this website.

Image/Video Processing

Key frames hysteresis-seeking based on motion change points for RGB-D video


Key-frame extraction has been a focused problem of human action recognition due to its effectiveness, efficiency and importance in action understanding. According to the characteristics of human motion perception, this paper proposes a new key-frame extraction framework based on motion change points. And in order to detect motion change points robustly, a hysteresis extrema seeking algorithm has been developed. Experimental results have demonstrated the good performance of the proposed methods.

slides.pdf

PDF icon slides.pdf (384 downloads)

Paper Details

Authors:
Yong Nie, Peng Zhang, Bo Hu
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

slides.pdf

(384 downloads)

Keywords

Subscribe

[1] Yong Nie, Peng Zhang, Bo Hu, "Key frames hysteresis-seeking based on motion change points for RGB-D video", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/392. Accessed: Dec. 15, 2017.
@article{392-15,
url = {http://sigport.org/392},
author = {Yong Nie; Peng Zhang; Bo Hu },
publisher = {IEEE SigPort},
title = {Key frames hysteresis-seeking based on motion change points for RGB-D video},
year = {2015} }
TY - EJOUR
T1 - Key frames hysteresis-seeking based on motion change points for RGB-D video
AU - Yong Nie; Peng Zhang; Bo Hu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/392
ER -
Yong Nie, Peng Zhang, Bo Hu. (2015). Key frames hysteresis-seeking based on motion change points for RGB-D video. IEEE SigPort. http://sigport.org/392
Yong Nie, Peng Zhang, Bo Hu, 2015. Key frames hysteresis-seeking based on motion change points for RGB-D video. Available at: http://sigport.org/392.
Yong Nie, Peng Zhang, Bo Hu. (2015). "Key frames hysteresis-seeking based on motion change points for RGB-D video." Web.
1. Yong Nie, Peng Zhang, Bo Hu. Key frames hysteresis-seeking based on motion change points for RGB-D video [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/392

Guided Signal Reconstruction with Application to Image Magnification


Reconstruction Set

We propose signal reconstruction algorithms which utilize a guiding subspace that represents desired properties of reconstructed signals. Optimal reconstructed signals are shown to belong to a convex bounded set, called the ``reconstruction'' set. Iterative reconstruction algorithms, based on conjugate gradient methods, are developed to approximate optimal reconstructions with low memory and computational costs. Effectiveness of the proposed method is demonstrated with an application to image magnification.

Paper Details

Authors:
Akshay Gadde, Hassan Mansour, Dong Tian
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

globalsip-15-slides-v2.pdf

(312 downloads)

Keywords

Subscribe

[1] Akshay Gadde, Hassan Mansour, Dong Tian, "Guided Signal Reconstruction with Application to Image Magnification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/384. Accessed: Dec. 15, 2017.
@article{384-15,
url = {http://sigport.org/384},
author = {Akshay Gadde; Hassan Mansour; Dong Tian },
publisher = {IEEE SigPort},
title = {Guided Signal Reconstruction with Application to Image Magnification},
year = {2015} }
TY - EJOUR
T1 - Guided Signal Reconstruction with Application to Image Magnification
AU - Akshay Gadde; Hassan Mansour; Dong Tian
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/384
ER -
Akshay Gadde, Hassan Mansour, Dong Tian. (2015). Guided Signal Reconstruction with Application to Image Magnification. IEEE SigPort. http://sigport.org/384
Akshay Gadde, Hassan Mansour, Dong Tian, 2015. Guided Signal Reconstruction with Application to Image Magnification. Available at: http://sigport.org/384.
Akshay Gadde, Hassan Mansour, Dong Tian. (2015). "Guided Signal Reconstruction with Application to Image Magnification." Web.
1. Akshay Gadde, Hassan Mansour, Dong Tian. Guided Signal Reconstruction with Application to Image Magnification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/384

Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction

Paper Details

Authors:
Robert Bregovic, Atanas Gotchev
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSIP2015_Presentation.pdf

(259 downloads)

Keywords

Subscribe

[1] Robert Bregovic, Atanas Gotchev, "Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/379. Accessed: Dec. 15, 2017.
@article{379-15,
url = {http://sigport.org/379},
author = {Robert Bregovic; Atanas Gotchev },
publisher = {IEEE SigPort},
title = {Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction},
year = {2015} }
TY - EJOUR
T1 - Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction
AU - Robert Bregovic; Atanas Gotchev
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/379
ER -
Robert Bregovic, Atanas Gotchev. (2015). Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction. IEEE SigPort. http://sigport.org/379
Robert Bregovic, Atanas Gotchev, 2015. Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction. Available at: http://sigport.org/379.
Robert Bregovic, Atanas Gotchev. (2015). "Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction." Web.
1. Robert Bregovic, Atanas Gotchev. Tree-Structured Algorithm For Efficient Shearlet-domain Light Field Reconstruction [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/379

Dense Invariant Feature Based Support Vector Ranking for Person Re-identification

Paper Details

Authors:
Feng Zheng, Ling Shao
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Support Vector Ranking on Fused Density Features_presentation.pdf

(262 downloads)

Keywords

Subscribe

[1] Feng Zheng, Ling Shao, "Dense Invariant Feature Based Support Vector Ranking for Person Re-identification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/375. Accessed: Dec. 15, 2017.
@article{375-15,
url = {http://sigport.org/375},
author = {Feng Zheng; Ling Shao },
publisher = {IEEE SigPort},
title = {Dense Invariant Feature Based Support Vector Ranking for Person Re-identification},
year = {2015} }
TY - EJOUR
T1 - Dense Invariant Feature Based Support Vector Ranking for Person Re-identification
AU - Feng Zheng; Ling Shao
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/375
ER -
Feng Zheng, Ling Shao. (2015). Dense Invariant Feature Based Support Vector Ranking for Person Re-identification. IEEE SigPort. http://sigport.org/375
Feng Zheng, Ling Shao, 2015. Dense Invariant Feature Based Support Vector Ranking for Person Re-identification. Available at: http://sigport.org/375.
Feng Zheng, Ling Shao. (2015). "Dense Invariant Feature Based Support Vector Ranking for Person Re-identification." Web.
1. Feng Zheng, Ling Shao. Dense Invariant Feature Based Support Vector Ranking for Person Re-identification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/375

Dense Invariant Feature Based Support Vector Ranking for Person Re-identification

Paper Details

Authors:
Feng Zheng, Ling Shao
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Support Vector Ranking on Fused Density Features_presentation.pdf

(262 downloads)

Keywords

Subscribe

[1] Feng Zheng, Ling Shao, "Dense Invariant Feature Based Support Vector Ranking for Person Re-identification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/374. Accessed: Dec. 15, 2017.
@article{374-15,
url = {http://sigport.org/374},
author = {Feng Zheng; Ling Shao },
publisher = {IEEE SigPort},
title = {Dense Invariant Feature Based Support Vector Ranking for Person Re-identification},
year = {2015} }
TY - EJOUR
T1 - Dense Invariant Feature Based Support Vector Ranking for Person Re-identification
AU - Feng Zheng; Ling Shao
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/374
ER -
Feng Zheng, Ling Shao. (2015). Dense Invariant Feature Based Support Vector Ranking for Person Re-identification. IEEE SigPort. http://sigport.org/374
Feng Zheng, Ling Shao, 2015. Dense Invariant Feature Based Support Vector Ranking for Person Re-identification. Available at: http://sigport.org/374.
Feng Zheng, Ling Shao. (2015). "Dense Invariant Feature Based Support Vector Ranking for Person Re-identification." Web.
1. Feng Zheng, Ling Shao. Dense Invariant Feature Based Support Vector Ranking for Person Re-identification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/374

IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS

Paper Details

Authors:
Yehoshua Y. Zeevi
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

PaperPresentation.pdf

(315 downloads)

Keywords

Subscribe

[1] Yehoshua Y. Zeevi, "IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/353. Accessed: Dec. 15, 2017.
@article{353-15,
url = {http://sigport.org/353},
author = {Yehoshua Y. Zeevi },
publisher = {IEEE SigPort},
title = {IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS},
year = {2015} }
TY - EJOUR
T1 - IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS
AU - Yehoshua Y. Zeevi
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/353
ER -
Yehoshua Y. Zeevi. (2015). IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS. IEEE SigPort. http://sigport.org/353
Yehoshua Y. Zeevi, 2015. IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS. Available at: http://sigport.org/353.
Yehoshua Y. Zeevi. (2015). "IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS." Web.
1. Yehoshua Y. Zeevi. IMAGE UNMIXING SUCCESS ESTIMATION IN SPATIALLY VARYING SYSTEMS [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/353

A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption

Paper Details

Authors:
Edmar S. da Silva, Ricardo M. Campello de Souza
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

presentation.pdf

(423 downloads)

Keywords

Subscribe

[1] Edmar S. da Silva, Ricardo M. Campello de Souza, "A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/337. Accessed: Dec. 15, 2017.
@article{337-15,
url = {http://sigport.org/337},
author = {Edmar S. da Silva; Ricardo M. Campello de Souza },
publisher = {IEEE SigPort},
title = {A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption},
year = {2015} }
TY - EJOUR
T1 - A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption
AU - Edmar S. da Silva; Ricardo M. Campello de Souza
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/337
ER -
Edmar S. da Silva, Ricardo M. Campello de Souza. (2015). A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption. IEEE SigPort. http://sigport.org/337
Edmar S. da Silva, Ricardo M. Campello de Souza, 2015. A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption. Available at: http://sigport.org/337.
Edmar S. da Silva, Ricardo M. Campello de Souza. (2015). "A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption." Web.
1. Edmar S. da Silva, Ricardo M. Campello de Souza. A Finite Field Cosine Transform-Based Image Processing Scheme for Color Image Encryption [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/337

Piecewise Planar Super-Resolution for 3D Scene

Paper Details

Authors:
Yu Hen Hu, Hongrui Jiang
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

GlobalSiP_WangGaoAng.pdf

(219 downloads)

Keywords

Subscribe

[1] Yu Hen Hu, Hongrui Jiang, "Piecewise Planar Super-Resolution for 3D Scene", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/324. Accessed: Dec. 15, 2017.
@article{324-15,
url = {http://sigport.org/324},
author = {Yu Hen Hu; Hongrui Jiang },
publisher = {IEEE SigPort},
title = {Piecewise Planar Super-Resolution for 3D Scene},
year = {2015} }
TY - EJOUR
T1 - Piecewise Planar Super-Resolution for 3D Scene
AU - Yu Hen Hu; Hongrui Jiang
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/324
ER -
Yu Hen Hu, Hongrui Jiang. (2015). Piecewise Planar Super-Resolution for 3D Scene. IEEE SigPort. http://sigport.org/324
Yu Hen Hu, Hongrui Jiang, 2015. Piecewise Planar Super-Resolution for 3D Scene. Available at: http://sigport.org/324.
Yu Hen Hu, Hongrui Jiang. (2015). "Piecewise Planar Super-Resolution for 3D Scene." Web.
1. Yu Hen Hu, Hongrui Jiang. Piecewise Planar Super-Resolution for 3D Scene [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/324

Model-based Color Natural Stochastic Textures Processing and Classification

Paper Details

Authors:
Zeevi, Yehoshua Y.
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

gs15_pres.pdf

(239 downloads)

Keywords

Subscribe

[1] Zeevi, Yehoshua Y., "Model-based Color Natural Stochastic Textures Processing and Classification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/313. Accessed: Dec. 15, 2017.
@article{313-15,
url = {http://sigport.org/313},
author = {Zeevi; Yehoshua Y. },
publisher = {IEEE SigPort},
title = {Model-based Color Natural Stochastic Textures Processing and Classification},
year = {2015} }
TY - EJOUR
T1 - Model-based Color Natural Stochastic Textures Processing and Classification
AU - Zeevi; Yehoshua Y.
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/313
ER -
Zeevi, Yehoshua Y.. (2015). Model-based Color Natural Stochastic Textures Processing and Classification. IEEE SigPort. http://sigport.org/313
Zeevi, Yehoshua Y., 2015. Model-based Color Natural Stochastic Textures Processing and Classification. Available at: http://sigport.org/313.
Zeevi, Yehoshua Y.. (2015). "Model-based Color Natural Stochastic Textures Processing and Classification." Web.
1. Zeevi, Yehoshua Y.. Model-based Color Natural Stochastic Textures Processing and Classification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/313

Unsupervised Image Segmentation using Comparative Reasoning and Random Walks

Paper Details

Authors:
Filipe Condessa, Jelena Kovacevic
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Presentation-USCRRW v7 red.pdf

(246 downloads)

Keywords

Subscribe

[1] Filipe Condessa, Jelena Kovacevic, "Unsupervised Image Segmentation using Comparative Reasoning and Random Walks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/298. Accessed: Dec. 15, 2017.
@article{298-15,
url = {http://sigport.org/298},
author = {Filipe Condessa; Jelena Kovacevic },
publisher = {IEEE SigPort},
title = {Unsupervised Image Segmentation using Comparative Reasoning and Random Walks},
year = {2015} }
TY - EJOUR
T1 - Unsupervised Image Segmentation using Comparative Reasoning and Random Walks
AU - Filipe Condessa; Jelena Kovacevic
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/298
ER -
Filipe Condessa, Jelena Kovacevic. (2015). Unsupervised Image Segmentation using Comparative Reasoning and Random Walks. IEEE SigPort. http://sigport.org/298
Filipe Condessa, Jelena Kovacevic, 2015. Unsupervised Image Segmentation using Comparative Reasoning and Random Walks. Available at: http://sigport.org/298.
Filipe Condessa, Jelena Kovacevic. (2015). "Unsupervised Image Segmentation using Comparative Reasoning and Random Walks." Web.
1. Filipe Condessa, Jelena Kovacevic. Unsupervised Image Segmentation using Comparative Reasoning and Random Walks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/298

Pages