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Image/Video Processing

A Data Set Providing Synthetic and Real-World Fisheye Video Sequences


Synthetic fisheye image

In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The sequences are freely available via www.lms.lnt.de/fisheyedataset/.

Paper Details

Authors:
Andrea Eichenseer, André Kaup
Submitted On:
16 March 2016 - 5:36am
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posterICASSP2016fisheyedataset.pdf

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[1] Andrea Eichenseer, André Kaup, "A Data Set Providing Synthetic and Real-World Fisheye Video Sequences", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/709. Accessed: Jul. 20, 2019.
@article{709-16,
url = {http://sigport.org/709},
author = {Andrea Eichenseer; André Kaup },
publisher = {IEEE SigPort},
title = {A Data Set Providing Synthetic and Real-World Fisheye Video Sequences},
year = {2016} }
TY - EJOUR
T1 - A Data Set Providing Synthetic and Real-World Fisheye Video Sequences
AU - Andrea Eichenseer; André Kaup
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/709
ER -
Andrea Eichenseer, André Kaup. (2016). A Data Set Providing Synthetic and Real-World Fisheye Video Sequences. IEEE SigPort. http://sigport.org/709
Andrea Eichenseer, André Kaup, 2016. A Data Set Providing Synthetic and Real-World Fisheye Video Sequences. Available at: http://sigport.org/709.
Andrea Eichenseer, André Kaup. (2016). "A Data Set Providing Synthetic and Real-World Fisheye Video Sequences." Web.
1. Andrea Eichenseer, André Kaup. A Data Set Providing Synthetic and Real-World Fisheye Video Sequences [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/709

Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'


In a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation.

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Authors:
Jenq-Neng Hwang,Yen-Shuo Lin,Jen-Hui Chuang
Submitted On:
17 March 2016 - 3:46pm
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mast.pdf

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[1] Jenq-Neng Hwang,Yen-Shuo Lin,Jen-Hui Chuang, "Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/698. Accessed: Jul. 20, 2019.
@article{698-16,
url = {http://sigport.org/698},
author = {Jenq-Neng Hwang;Yen-Shuo Lin;Jen-Hui Chuang },
publisher = {IEEE SigPort},
title = {Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'},
year = {2016} }
TY - EJOUR
T1 - Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'
AU - Jenq-Neng Hwang;Yen-Shuo Lin;Jen-Hui Chuang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/698
ER -
Jenq-Neng Hwang,Yen-Shuo Lin,Jen-Hui Chuang. (2016). Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'. IEEE SigPort. http://sigport.org/698
Jenq-Neng Hwang,Yen-Shuo Lin,Jen-Hui Chuang, 2016. Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'. Available at: http://sigport.org/698.
Jenq-Neng Hwang,Yen-Shuo Lin,Jen-Hui Chuang. (2016). "Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking'." Web.
1. Jenq-Neng Hwang,Yen-Shuo Lin,Jen-Hui Chuang. Presentation Slides for 'Multiple-kernel Adaptive Segmentation and Tracking (MAST) for Robust Object Tracking' [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/698

REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE

Paper Details

Authors:
Yang Yang, Yun-Xia Liu
Submitted On:
16 March 2016 - 9:55pm
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海报.pdf

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[1] Yang Yang, Yun-Xia Liu, "REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/686. Accessed: Jul. 20, 2019.
@article{686-16,
url = {http://sigport.org/686},
author = {Yang Yang; Yun-Xia Liu },
publisher = {IEEE SigPort},
title = {REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE},
year = {2016} }
TY - EJOUR
T1 - REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE
AU - Yang Yang; Yun-Xia Liu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/686
ER -
Yang Yang, Yun-Xia Liu. (2016). REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE. IEEE SigPort. http://sigport.org/686
Yang Yang, Yun-Xia Liu, 2016. REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE. Available at: http://sigport.org/686.
Yang Yang, Yun-Xia Liu. (2016). "REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE." Web.
1. Yang Yang, Yun-Xia Liu. REAL-TIME MULTI-CANDIDATES FUSION BASED HEAD TRACKING ON KINECT DEPTH SEQUENCE [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/686

Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning

Paper Details

Authors:
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun
Submitted On:
14 March 2016 - 1:33am
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ICASSP-poster-Final.pdf

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[1] Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun, "Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/661. Accessed: Jul. 20, 2019.
@article{661-16,
url = {http://sigport.org/661},
author = {Guozheng Yang; Jing Yu; Chuangbai Xiao; Weidong Sun },
publisher = {IEEE SigPort},
title = {Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning},
year = {2016} }
TY - EJOUR
T1 - Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning
AU - Guozheng Yang; Jing Yu; Chuangbai Xiao; Weidong Sun
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/661
ER -
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun. (2016). Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning. IEEE SigPort. http://sigport.org/661
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun, 2016. Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning. Available at: http://sigport.org/661.
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun. (2016). "Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning." Web.
1. Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun. Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/661

Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning

Paper Details

Authors:
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun
Submitted On:
14 March 2016 - 1:33am
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ICASSP-poster-Final.pdf

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[1] Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun, "Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/659. Accessed: Jul. 20, 2019.
@article{659-16,
url = {http://sigport.org/659},
author = {Guozheng Yang; Jing Yu; Chuangbai Xiao; Weidong Sun },
publisher = {IEEE SigPort},
title = {Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning},
year = {2016} }
TY - EJOUR
T1 - Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning
AU - Guozheng Yang; Jing Yu; Chuangbai Xiao; Weidong Sun
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/659
ER -
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun. (2016). Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning. IEEE SigPort. http://sigport.org/659
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun, 2016. Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning. Available at: http://sigport.org/659.
Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun. (2016). "Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning." Web.
1. Guozheng Yang, Jing Yu, Chuangbai Xiao, Weidong Sun. Ship wake detection for sar images with complex backgrounds based on morphological dictionary learning [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/659

Bernstein Filter: a new solver for mean curvature regularized models


The mean curvature has been shown a proper regularization in various ill-posed inverse problems in signal processing. Traditional solvers are based on either gradient descent methods or Euler Lagrange Equation. However, it is not clear if this mean curvature regularization term itself is convex or not. In this paper, we first prove that the mean curvature regularization is convex if the dimension of imaging domain is not

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Submitted On:
11 April 2016 - 3:46am
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BernsteinFilter_poster.pdf

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ICASSP.pdf

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[1] , "Bernstein Filter: a new solver for mean curvature regularized models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/640. Accessed: Jul. 20, 2019.
@article{640-16,
url = {http://sigport.org/640},
author = { },
publisher = {IEEE SigPort},
title = {Bernstein Filter: a new solver for mean curvature regularized models},
year = {2016} }
TY - EJOUR
T1 - Bernstein Filter: a new solver for mean curvature regularized models
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/640
ER -
. (2016). Bernstein Filter: a new solver for mean curvature regularized models. IEEE SigPort. http://sigport.org/640
, 2016. Bernstein Filter: a new solver for mean curvature regularized models. Available at: http://sigport.org/640.
. (2016). "Bernstein Filter: a new solver for mean curvature regularized models." Web.
1. . Bernstein Filter: a new solver for mean curvature regularized models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/640

Supervised-learning based face hallucination for enhancing face recognition


This paper presents a two-step supervised face hallucination framework based on class-specific dictionary learning. Since the performance of learning-based face hallucination relies on its training set, an inappropriate training set (e.g., an input face image is very different from the training set) can reduce the visual quality of reconstructed high-resolution (HR) face significantly.

Paper Details

Authors:
Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin
Submitted On:
11 March 2016 - 10:48pm
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icassp-2016-poster.pdf

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[1] Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin, "Supervised-learning based face hallucination for enhancing face recognition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/623. Accessed: Jul. 20, 2019.
@article{623-16,
url = {http://sigport.org/623},
author = {Chih-Chung Hsu; Chia-Wen Lin; Weiyao Lin },
publisher = {IEEE SigPort},
title = {Supervised-learning based face hallucination for enhancing face recognition},
year = {2016} }
TY - EJOUR
T1 - Supervised-learning based face hallucination for enhancing face recognition
AU - Chih-Chung Hsu; Chia-Wen Lin; Weiyao Lin
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/623
ER -
Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin. (2016). Supervised-learning based face hallucination for enhancing face recognition. IEEE SigPort. http://sigport.org/623
Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin, 2016. Supervised-learning based face hallucination for enhancing face recognition. Available at: http://sigport.org/623.
Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin. (2016). "Supervised-learning based face hallucination for enhancing face recognition." Web.
1. Chih-Chung Hsu, Chia-Wen Lin, Weiyao Lin. Supervised-learning based face hallucination for enhancing face recognition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/623

Models for Spectral Clustering and Their Applications


Microarray Spectral Clustering.

Ph.D. Thesis by Donald McCuan (advisor Andrew Knyazev), Department of Mathematical and Statistical Sciences, University of Colorado Denver, 2012, originally posted at http://math.ucdenver.edu/theses/McCuan_PhdThesis.pdf (872)

Paper Details

Authors:
Donald Donald
Submitted On:
23 February 2016 - 1:44pm
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McCuan_PhdThesis.pdf

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[1] Donald Donald, "Models for Spectral Clustering and Their Applications", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/564. Accessed: Jul. 20, 2019.
@article{564-15,
url = {http://sigport.org/564},
author = {Donald Donald },
publisher = {IEEE SigPort},
title = {Models for Spectral Clustering and Their Applications},
year = {2015} }
TY - EJOUR
T1 - Models for Spectral Clustering and Their Applications
AU - Donald Donald
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/564
ER -
Donald Donald. (2015). Models for Spectral Clustering and Their Applications. IEEE SigPort. http://sigport.org/564
Donald Donald, 2015. Models for Spectral Clustering and Their Applications. Available at: http://sigport.org/564.
Donald Donald. (2015). "Models for Spectral Clustering and Their Applications." Web.
1. Donald Donald. Models for Spectral Clustering and Their Applications [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/564

Multigrid Eigensolvers for Image Segmentation


Spectral Image Segmentation

Presentation at LANL and UC Davis, 2009. Originally posted at http://math.ucdenver.edu/~aknyazev/research/conf/

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23 February 2016 - 1:44pm
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LANL09.ppt

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[1] , "Multigrid Eigensolvers for Image Segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/562. Accessed: Jul. 20, 2019.
@article{562-15,
url = {http://sigport.org/562},
author = { },
publisher = {IEEE SigPort},
title = {Multigrid Eigensolvers for Image Segmentation},
year = {2015} }
TY - EJOUR
T1 - Multigrid Eigensolvers for Image Segmentation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/562
ER -
. (2015). Multigrid Eigensolvers for Image Segmentation. IEEE SigPort. http://sigport.org/562
, 2015. Multigrid Eigensolvers for Image Segmentation. Available at: http://sigport.org/562.
. (2015). "Multigrid Eigensolvers for Image Segmentation." Web.
1. . Multigrid Eigensolvers for Image Segmentation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/562

Novel data clustering for microarrays and image segmentation


Spectral Clustering Eigenvalue Problem

We develop novel algorithms and software on parallel computers for data clustering of large datasets. We are interested in applying our approach, e.g., for analysis of large datasets of microarrays or tiling arrays in molecular biology and for segmentation of high resolution images.

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23 February 2016 - 1:44pm
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camready-1031.ppt

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[1] , "Novel data clustering for microarrays and image segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/561. Accessed: Jul. 20, 2019.
@article{561-15,
url = {http://sigport.org/561},
author = { },
publisher = {IEEE SigPort},
title = {Novel data clustering for microarrays and image segmentation},
year = {2015} }
TY - EJOUR
T1 - Novel data clustering for microarrays and image segmentation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/561
ER -
. (2015). Novel data clustering for microarrays and image segmentation. IEEE SigPort. http://sigport.org/561
, 2015. Novel data clustering for microarrays and image segmentation. Available at: http://sigport.org/561.
. (2015). "Novel data clustering for microarrays and image segmentation." Web.
1. . Novel data clustering for microarrays and image segmentation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/561

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