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Image, Video, and Multidimensional Signal Processing

Block-coordinate proximal algorithms for scale-free texture segmentation


Texture segmentation still constitutes an on-going challenge, especially when processing large-size images.
Recently, procedures integrating a scale-free (or fractal)wavelet-leader model allowed the problem to be reformulated in a convex optimization framework by including a TV penalization. In this case, the TV penalty plays

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Authors:
Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet
Submitted On:
18 April 2018 - 12:00am
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icassp2018.pdf

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[1] Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet, "Block-coordinate proximal algorithms for scale-free texture segmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2716. Accessed: May. 26, 2018.
@article{2716-18,
url = {http://sigport.org/2716},
author = {Barbara Pascal; Nelly Pustelnik; Patrice Abry; Jean-Christophe Pesquet },
publisher = {IEEE SigPort},
title = {Block-coordinate proximal algorithms for scale-free texture segmentation},
year = {2018} }
TY - EJOUR
T1 - Block-coordinate proximal algorithms for scale-free texture segmentation
AU - Barbara Pascal; Nelly Pustelnik; Patrice Abry; Jean-Christophe Pesquet
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2716
ER -
Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet. (2018). Block-coordinate proximal algorithms for scale-free texture segmentation. IEEE SigPort. http://sigport.org/2716
Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet, 2018. Block-coordinate proximal algorithms for scale-free texture segmentation. Available at: http://sigport.org/2716.
Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet. (2018). "Block-coordinate proximal algorithms for scale-free texture segmentation." Web.
1. Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet. Block-coordinate proximal algorithms for scale-free texture segmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2716

Unsupervised Image Segmentation by Backpropagation


We investigate the use of convolutional neural networks (CNNs) for unsupervised image segmentation. As in the case of supervised image segmentation, the proposed CNN assigns labels to pixels that denote the cluster to which the pixel belongs. In the unsupervised scenario, however, no training images or ground truth labels of pixels are given beforehand. Therefore, once when a target image is input, we jointly optimize the pixel labels together with feature representations while their parameters are updated by gradient descent.

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13 April 2018 - 10:20am
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ICASSP2018_kanezaki_outline.pdf

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[1] , "Unsupervised Image Segmentation by Backpropagation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2710. Accessed: May. 26, 2018.
@article{2710-18,
url = {http://sigport.org/2710},
author = { },
publisher = {IEEE SigPort},
title = {Unsupervised Image Segmentation by Backpropagation},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Image Segmentation by Backpropagation
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2710
ER -
. (2018). Unsupervised Image Segmentation by Backpropagation. IEEE SigPort. http://sigport.org/2710
, 2018. Unsupervised Image Segmentation by Backpropagation. Available at: http://sigport.org/2710.
. (2018). "Unsupervised Image Segmentation by Backpropagation." Web.
1. . Unsupervised Image Segmentation by Backpropagation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2710

SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION


Unsupervised cross-database facial expression recognition(FER) is a challenging problem, in which the training and testing samples belong to different facial expression databases. For this reason, the training (source) and testing (target) facial expression samples would have different feature distributions and hence the performance of lots of existing FER methods may decrease.

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Authors:
Baofeng Zhang, Yuan Zong, Li Liu,Jie Chen, Guoying Zhao, Junchao Zhu
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13 April 2018 - 8:17am
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ICASSP2018_2838.pdf

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[1] Baofeng Zhang, Yuan Zong, Li Liu,Jie Chen, Guoying Zhao, Junchao Zhu, "SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2696. Accessed: May. 26, 2018.
@article{2696-18,
url = {http://sigport.org/2696},
author = {Baofeng Zhang; Yuan Zong; Li Liu;Jie Chen; Guoying Zhao; Junchao Zhu },
publisher = {IEEE SigPort},
title = {SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION
AU - Baofeng Zhang; Yuan Zong; Li Liu;Jie Chen; Guoying Zhao; Junchao Zhu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2696
ER -
Baofeng Zhang, Yuan Zong, Li Liu,Jie Chen, Guoying Zhao, Junchao Zhu. (2018). SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION. IEEE SigPort. http://sigport.org/2696
Baofeng Zhang, Yuan Zong, Li Liu,Jie Chen, Guoying Zhao, Junchao Zhu, 2018. SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION. Available at: http://sigport.org/2696.
Baofeng Zhang, Yuan Zong, Li Liu,Jie Chen, Guoying Zhao, Junchao Zhu. (2018). "SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION." Web.
1. Baofeng Zhang, Yuan Zong, Li Liu,Jie Chen, Guoying Zhao, Junchao Zhu. SUPER WIDE REGRESSION NETWORK FOR UNSUPERVISED CROSS-DATABASE FACIAL EXPRESSION RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2696

IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN


Matrix factorization (MF) and its extensions have been intensively studied in computer vision and machine learning. In this paper, unsupervised and supervised learning methods based on MF technique on complex domain are introduced. Projective complex matrix factorization (PCMF) and discriminant projective complex matrix factorization (DPCMF) present two frameworks of projecting complex data to a lower dimension space. The optimization problems are formulated as the minimization of the real-valued functions of complex variables.

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Authors:
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang
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13 April 2018 - 2:14am
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ICASSP2018_DPCMF_2.pdf

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[1] Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang, "IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2620. Accessed: May. 26, 2018.
@article{2620-18,
url = {http://sigport.org/2620},
author = {Manh-Quan Bui; Viet-Hang Duong; Yung-Hui Li; Tzu-Chiang Tai; Jia-Ching Wang },
publisher = {IEEE SigPort},
title = {IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN},
year = {2018} }
TY - EJOUR
T1 - IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN
AU - Manh-Quan Bui; Viet-Hang Duong; Yung-Hui Li; Tzu-Chiang Tai; Jia-Ching Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2620
ER -
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. (2018). IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN. IEEE SigPort. http://sigport.org/2620
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang, 2018. IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN. Available at: http://sigport.org/2620.
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. (2018). "IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN." Web.
1. Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2620

IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN


Matrix factorization (MF) and its extensions have been intensively studied in computer vision and machine learning. In this paper, unsupervised and supervised learning methods based on MF technique on complex domain are introduced. Projective complex matrix factorization (PCMF) and discriminant projective complex matrix factorization (DPCMF) present two frameworks of projecting complex data to a lower dimension space. The optimization problems are formulated as the minimization of the real-valued functions of complex variables.

Paper Details

Authors:
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang
Submitted On:
13 April 2018 - 2:14am
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ICASSP2018_DPCMF_2.pdf

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[1] Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang, "IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2619. Accessed: May. 26, 2018.
@article{2619-18,
url = {http://sigport.org/2619},
author = {Manh-Quan Bui; Viet-Hang Duong; Yung-Hui Li; Tzu-Chiang Tai; Jia-Ching Wang },
publisher = {IEEE SigPort},
title = {IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN},
year = {2018} }
TY - EJOUR
T1 - IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN
AU - Manh-Quan Bui; Viet-Hang Duong; Yung-Hui Li; Tzu-Chiang Tai; Jia-Ching Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2619
ER -
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. (2018). IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN. IEEE SigPort. http://sigport.org/2619
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang, 2018. IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN. Available at: http://sigport.org/2619.
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. (2018). "IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN." Web.
1. Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2619

IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN


Matrix factorization (MF) and its extensions have been intensively studied in computer vision and machine learning. In this paper, unsupervised and supervised learning methods based on MF technique on complex domain are introduced. Projective complex matrix factorization (PCMF) and discriminant projective complex matrix factorization (DPCMF) present two frameworks of projecting complex data to a lower dimension space. The optimization problems are formulated as the minimization of the real-valued functions of complex variables.

Paper Details

Authors:
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang
Submitted On:
13 April 2018 - 5:40am
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ICASSP2018_DPCMF_2.pdf

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ICASSP2018_DPCMF_Origin1.pdf

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[1] Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang, "IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2618. Accessed: May. 26, 2018.
@article{2618-18,
url = {http://sigport.org/2618},
author = {Manh-Quan Bui; Viet-Hang Duong; Yung-Hui Li; Tzu-Chiang Tai; Jia-Ching Wang },
publisher = {IEEE SigPort},
title = {IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN},
year = {2018} }
TY - EJOUR
T1 - IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN
AU - Manh-Quan Bui; Viet-Hang Duong; Yung-Hui Li; Tzu-Chiang Tai; Jia-Ching Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2618
ER -
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. (2018). IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN. IEEE SigPort. http://sigport.org/2618
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang, 2018. IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN. Available at: http://sigport.org/2618.
Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. (2018). "IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN." Web.
1. Manh-Quan Bui, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, Jia-Ching Wang. IMAGE REPRESENTATION USING SUPERVISED AND UNSUPERVISED LEARNING METHODS ON COMPLEX DOMAIN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2618

IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES


A robust multi-view disparity estimation algorithm for noisy images is presented. The proposed algorithm constructs 3D focus image stacks (3DFIS) by projecting and stacking multi-view images and estimates a disparity map based on the 3DFIS. To make the algorithm robust to noise and occlusion, a texture-based view selection and patch size variation scheme based on texture map is proposed.

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Authors:
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang
Submitted On:
12 April 2018 - 12:44pm
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ICASSP_poster.pdf

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[1] Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang, "IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2442. Accessed: May. 26, 2018.
@article{2442-18,
url = {http://sigport.org/2442},
author = {Shiwei Zhou; Zhengyang Lou; Yu Hen Hu; Hongrui Jiang },
publisher = {IEEE SigPort},
title = {IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES },
year = {2018} }
TY - EJOUR
T1 - IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES
AU - Shiwei Zhou; Zhengyang Lou; Yu Hen Hu; Hongrui Jiang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2442
ER -
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang. (2018). IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES . IEEE SigPort. http://sigport.org/2442
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang, 2018. IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES . Available at: http://sigport.org/2442.
Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang. (2018). "IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES ." Web.
1. Shiwei Zhou, Zhengyang Lou, Yu Hen Hu, Hongrui Jiang. IMPROVING DISPARITY MAP ESTIMATION FOR MULTI-VIEW NOISY IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2442

ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER


We propose an adaptive visual target tracking algorithm based on Label-Consistent K-Singular Value Decomposition (LC-KSVD) dictionary learning. To construct target templates, local patch features are sampled from foreground and background of the target. LC-KSVD then is applied to these local patches to simultaneously estimate a set of low-dimension dictionary and classification parameters (CP). To track the target over time, a kernel particle filter (KPF) is proposed that integrates both local and global motion information of the target.

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Authors:
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu
Submitted On:
12 April 2018 - 12:25pm
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Poster-2119.pdf

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[1] Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu, "ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2432. Accessed: May. 26, 2018.
@article{2432-18,
url = {http://sigport.org/2432},
author = {Jinlong Yang; Xiaoping Chen; Yu Hen Hu Jianjun Liu },
publisher = {IEEE SigPort},
title = {ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER
AU - Jinlong Yang; Xiaoping Chen; Yu Hen Hu Jianjun Liu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2432
ER -
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu. (2018). ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER. IEEE SigPort. http://sigport.org/2432
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu, 2018. ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER. Available at: http://sigport.org/2432.
Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu. (2018). "ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER." Web.
1. Jinlong Yang, Xiaoping Chen, Yu Hen Hu Jianjun Liu. ADAPTIVE VISUAL TARGET TRACKING BASED ON LABEL CONSISTENT K-SVD SPARSE CODING AND KERNEL PARTICLE FILTER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2432

AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS


To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an automatic image-based aircraft defect detection using Deep Neural Networks (DNNs). To the best of our knowledge, this is the first work for aircraft defect detection using DNNs. We perform a comprehensive evaluation of state-of-the-art feature descriptors and show that the best performance is achieved by vgg-f DNN as feature extractor with a linear SVM classifier.

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Authors:
TOUBA MALEKZADEH, MILAD ABDOLLAHZADEH HOSSEIN NEJATI, NGAI-MAN CHEUNG
Submitted On:
13 November 2017 - 8:57am
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AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS__v2.pdf

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[1] TOUBA MALEKZADEH, MILAD ABDOLLAHZADEH HOSSEIN NEJATI, NGAI-MAN CHEUNG, "AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2333. Accessed: May. 26, 2018.
@article{2333-17,
url = {http://sigport.org/2333},
author = {TOUBA MALEKZADEH; MILAD ABDOLLAHZADEH HOSSEIN NEJATI; NGAI-MAN CHEUNG },
publisher = {IEEE SigPort},
title = {AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS},
year = {2017} }
TY - EJOUR
T1 - AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS
AU - TOUBA MALEKZADEH; MILAD ABDOLLAHZADEH HOSSEIN NEJATI; NGAI-MAN CHEUNG
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2333
ER -
TOUBA MALEKZADEH, MILAD ABDOLLAHZADEH HOSSEIN NEJATI, NGAI-MAN CHEUNG. (2017). AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS. IEEE SigPort. http://sigport.org/2333
TOUBA MALEKZADEH, MILAD ABDOLLAHZADEH HOSSEIN NEJATI, NGAI-MAN CHEUNG, 2017. AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS. Available at: http://sigport.org/2333.
TOUBA MALEKZADEH, MILAD ABDOLLAHZADEH HOSSEIN NEJATI, NGAI-MAN CHEUNG. (2017). "AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS." Web.
1. TOUBA MALEKZADEH, MILAD ABDOLLAHZADEH HOSSEIN NEJATI, NGAI-MAN CHEUNG. AIRCRAFT FUSELAGE DEFECT DETECTION USING DEEP NEURAL NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2333

Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation

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12 November 2017 - 7:58pm
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[1] , "Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2323. Accessed: May. 26, 2018.
@article{2323-17,
url = {http://sigport.org/2323},
author = { },
publisher = {IEEE SigPort},
title = {Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation},
year = {2017} }
TY - EJOUR
T1 - Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2323
ER -
. (2017). Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation. IEEE SigPort. http://sigport.org/2323
, 2017. Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation. Available at: http://sigport.org/2323.
. (2017). "Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation." Web.
1. . Hierarchical multinomial latent model with G0 distribution for remote sensing image semantic segmentation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2323

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