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

Image/Video Processing

ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING


By exploiting the geometry information, the estimation performance (efficiency, accuracy and robustness) can be improved, and the conventional searching procedure can also be avoided. The concept of the methods can be used in the parameters estimation field, especially .

Paper Details

Authors:
Xuepan Zhang, Xuejing Zhang, Bo Liu
Submitted On:
20 March 2016 - 8:56am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING.pdf

(167 downloads)

Subscribe

[1] Xuepan Zhang, Xuejing Zhang, Bo Liu, "ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/870. Accessed: Jul. 21, 2018.
@article{870-16,
url = {http://sigport.org/870},
author = {Xuepan Zhang; Xuejing Zhang; Bo Liu },
publisher = {IEEE SigPort},
title = {ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING},
year = {2016} }
TY - EJOUR
T1 - ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING
AU - Xuepan Zhang; Xuejing Zhang; Bo Liu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/870
ER -
Xuepan Zhang, Xuejing Zhang, Bo Liu. (2016). ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING. IEEE SigPort. http://sigport.org/870
Xuepan Zhang, Xuejing Zhang, Bo Liu, 2016. ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING. Available at: http://sigport.org/870.
Xuepan Zhang, Xuejing Zhang, Bo Liu. (2016). "ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING." Web.
1. Xuepan Zhang, Xuejing Zhang, Bo Liu. ESTIMATION EFFICIENCY, ACCURACY AND ROBUSTNESS IMPROVEMENT BY EXPLOITING THE GEOMETRY INFORMATION IN RADAR MOVING TARGETS DETECTION AND IMAGING [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/870

Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides


Visual tracking in unconstrained environments often involves following an object that exhibits a number of appearance changes from factors such as scale change, rotation, and illumination. Effective tracking requires adapting a tracker to the object’s changing appearance over time. When a target becomes occluded by other objects in the scene, a naive tracker may end up learning the appearance of the occluding object. Our work introduces a method of detecting occlusion by considering the color profile of the target to prevent inappropriate tracker updates while the target is occluded.

Paper Details

Authors:
B.V.K. Vijaya Kumar
Submitted On:
20 March 2016 - 5:11am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

icassp2016ssiena.pptx

(277 downloads)

Subscribe

[1] B.V.K. Vijaya Kumar, "Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/861. Accessed: Jul. 21, 2018.
@article{861-16,
url = {http://sigport.org/861},
author = {B.V.K. Vijaya Kumar },
publisher = {IEEE SigPort},
title = {Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides},
year = {2016} }
TY - EJOUR
T1 - Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides
AU - B.V.K. Vijaya Kumar
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/861
ER -
B.V.K. Vijaya Kumar. (2016). Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides. IEEE SigPort. http://sigport.org/861
B.V.K. Vijaya Kumar, 2016. Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides. Available at: http://sigport.org/861.
B.V.K. Vijaya Kumar. (2016). "Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides." Web.
1. B.V.K. Vijaya Kumar. Detecting Occlusion from Color Information to Improve Visual Tracking - Presentation Slides [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/861

Masked Correlation Filters for Partially Occluded Face Recognition

Paper Details

Authors:
Submitted On:
21 March 2016 - 10:10pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Eric He ICASSP Presentation.pdf

(214 downloads)

Subscribe

[1] , "Masked Correlation Filters for Partially Occluded Face Recognition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/851. Accessed: Jul. 21, 2018.
@article{851-16,
url = {http://sigport.org/851},
author = { },
publisher = {IEEE SigPort},
title = {Masked Correlation Filters for Partially Occluded Face Recognition},
year = {2016} }
TY - EJOUR
T1 - Masked Correlation Filters for Partially Occluded Face Recognition
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/851
ER -
. (2016). Masked Correlation Filters for Partially Occluded Face Recognition. IEEE SigPort. http://sigport.org/851
, 2016. Masked Correlation Filters for Partially Occluded Face Recognition. Available at: http://sigport.org/851.
. (2016). "Masked Correlation Filters for Partially Occluded Face Recognition." Web.
1. . Masked Correlation Filters for Partially Occluded Face Recognition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/851

A Largest Matching Area Approach to Image Denoising

Paper Details

Authors:
Ji Ming, Danny Crookes
Submitted On:
19 March 2016 - 4:21pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

A Largest Matching Area Approach To Image Denoising - presentation slides.pdf

(183 downloads)

Subscribe

[1] Ji Ming, Danny Crookes, "A Largest Matching Area Approach to Image Denoising", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/832. Accessed: Jul. 21, 2018.
@article{832-16,
url = {http://sigport.org/832},
author = {Ji Ming; Danny Crookes },
publisher = {IEEE SigPort},
title = {A Largest Matching Area Approach to Image Denoising},
year = {2016} }
TY - EJOUR
T1 - A Largest Matching Area Approach to Image Denoising
AU - Ji Ming; Danny Crookes
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/832
ER -
Ji Ming, Danny Crookes. (2016). A Largest Matching Area Approach to Image Denoising. IEEE SigPort. http://sigport.org/832
Ji Ming, Danny Crookes, 2016. A Largest Matching Area Approach to Image Denoising. Available at: http://sigport.org/832.
Ji Ming, Danny Crookes. (2016). "A Largest Matching Area Approach to Image Denoising." Web.
1. Ji Ming, Danny Crookes. A Largest Matching Area Approach to Image Denoising [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/832

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Jul. 21, 2018.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

Face Alignment by Deep Convolutional Network with Adaptive Learning Rate


Deep convolutional network has been widely used in face recognition while not often used in face alignment. One of the most important reasons of this is the lack of training images annotated with landmarks due to fussy and time-consuming annotation work. To overcome this problem, we propose a novel data augmentation strategy. And we design an innovative training algorithm with adaptive learning rate for two iterative procedures, which helps the network to search an optimal solution.

Paper Details

Authors:
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma
Submitted On:
19 March 2016 - 6:38am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

zhiwen_icassp_slides_final.pdf

(283 downloads)

Subscribe

[1] Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma, "Face Alignment by Deep Convolutional Network with Adaptive Learning Rate", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/799. Accessed: Jul. 21, 2018.
@article{799-16,
url = {http://sigport.org/799},
author = {Zhiwen Shao; Shouhong Ding; Hengliang Zhu; Chengjie Wang; Lizhuang Ma },
publisher = {IEEE SigPort},
title = {Face Alignment by Deep Convolutional Network with Adaptive Learning Rate},
year = {2016} }
TY - EJOUR
T1 - Face Alignment by Deep Convolutional Network with Adaptive Learning Rate
AU - Zhiwen Shao; Shouhong Ding; Hengliang Zhu; Chengjie Wang; Lizhuang Ma
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/799
ER -
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma. (2016). Face Alignment by Deep Convolutional Network with Adaptive Learning Rate. IEEE SigPort. http://sigport.org/799
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma, 2016. Face Alignment by Deep Convolutional Network with Adaptive Learning Rate. Available at: http://sigport.org/799.
Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma. (2016). "Face Alignment by Deep Convolutional Network with Adaptive Learning Rate." Web.
1. Zhiwen Shao, Shouhong Ding, Hengliang Zhu, Chengjie Wang, Lizhuang Ma. Face Alignment by Deep Convolutional Network with Adaptive Learning Rate [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/799

Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification

Paper Details

Authors:
Submitted On:
19 March 2016 - 3:18am
Short Link:
Type:

Document Files

ICASSP2016_IVMSP-L7.2-Li.pdf

(231 downloads)

Subscribe

[1] , "Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/783. Accessed: Jul. 21, 2018.
@article{783-16,
url = {http://sigport.org/783},
author = { },
publisher = {IEEE SigPort},
title = {Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification},
year = {2016} }
TY - EJOUR
T1 - Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/783
ER -
. (2016). Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification. IEEE SigPort. http://sigport.org/783
, 2016. Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification. Available at: http://sigport.org/783.
. (2016). "Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification." Web.
1. . Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/783

CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION


Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to fisheries survey, such as fish size measurement, abundance prediction, catch estimation and compliance, species recognition and population counting. In this work, we present an innovative and effective method for measuring the chute-based fish length based on the morphological midline of the fish. The midline is generated through recursive morphological operations on the segmented fish mask.

Paper Details

Authors:
Jenq-Neng Hwang, Craig S. Rose
Submitted On:
17 March 2016 - 3:48pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

twhuang_ICASSP_2016_poster.pdf

(257 downloads)

Subscribe

[1] Jenq-Neng Hwang, Craig S. Rose, "CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/752. Accessed: Jul. 21, 2018.
@article{752-16,
url = {http://sigport.org/752},
author = {Jenq-Neng Hwang; Craig S. Rose },
publisher = {IEEE SigPort},
title = {CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION},
year = {2016} }
TY - EJOUR
T1 - CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION
AU - Jenq-Neng Hwang; Craig S. Rose
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/752
ER -
Jenq-Neng Hwang, Craig S. Rose. (2016). CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION. IEEE SigPort. http://sigport.org/752
Jenq-Neng Hwang, Craig S. Rose, 2016. CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION. Available at: http://sigport.org/752.
Jenq-Neng Hwang, Craig S. Rose. (2016). "CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION." Web.
1. Jenq-Neng Hwang, Craig S. Rose. CHUTE BASED AUTOMATED FISH LENGTH MEASUREMENT AND WATER DROP DETECTION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/752

Multi-focus image fusion via coupled dictionary training


ICASSP 2016 presentation, Session: IVMP-P8 - Interpolation and Super-Resolution, Tursday, March 24, 13:30-15:30

Paper Details

Authors:
Rui Gao, Sergiy A. Vorobyov, Hong Zhao
Submitted On:
16 March 2016 - 10:44am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

MultifocusPoster.pdf

(298 downloads)

Subscribe

[1] Rui Gao, Sergiy A. Vorobyov, Hong Zhao, "Multi-focus image fusion via coupled dictionary training", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/718. Accessed: Jul. 21, 2018.
@article{718-16,
url = {http://sigport.org/718},
author = {Rui Gao; Sergiy A. Vorobyov; Hong Zhao },
publisher = {IEEE SigPort},
title = {Multi-focus image fusion via coupled dictionary training},
year = {2016} }
TY - EJOUR
T1 - Multi-focus image fusion via coupled dictionary training
AU - Rui Gao; Sergiy A. Vorobyov; Hong Zhao
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/718
ER -
Rui Gao, Sergiy A. Vorobyov, Hong Zhao. (2016). Multi-focus image fusion via coupled dictionary training. IEEE SigPort. http://sigport.org/718
Rui Gao, Sergiy A. Vorobyov, Hong Zhao, 2016. Multi-focus image fusion via coupled dictionary training. Available at: http://sigport.org/718.
Rui Gao, Sergiy A. Vorobyov, Hong Zhao. (2016). "Multi-focus image fusion via coupled dictionary training." Web.
1. Rui Gao, Sergiy A. Vorobyov, Hong Zhao. Multi-focus image fusion via coupled dictionary training [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/718

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
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

posterICASSP2016fisheyedataset.pdf

(288 downloads)

Subscribe

[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. 21, 2018.
@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

Pages