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Image, Video, and Multidimensional Signal Processing

Immersive Optical-See-Through Augmented Reality (Keynote Talk)


Immersive Optical-See-Through Augmented Reality

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Authors:
Kari Pulli
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11 October 2017 - 2:41pm
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ICIP_2017_Meta_AR_small.pdf

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[1] Kari Pulli, "Immersive Optical-See-Through Augmented Reality (Keynote Talk)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2261. Accessed: Nov. 20, 2017.
@article{2261-17,
url = {http://sigport.org/2261},
author = {Kari Pulli },
publisher = {IEEE SigPort},
title = {Immersive Optical-See-Through Augmented Reality (Keynote Talk)},
year = {2017} }
TY - EJOUR
T1 - Immersive Optical-See-Through Augmented Reality (Keynote Talk)
AU - Kari Pulli
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2261
ER -
Kari Pulli. (2017). Immersive Optical-See-Through Augmented Reality (Keynote Talk). IEEE SigPort. http://sigport.org/2261
Kari Pulli, 2017. Immersive Optical-See-Through Augmented Reality (Keynote Talk). Available at: http://sigport.org/2261.
Kari Pulli. (2017). "Immersive Optical-See-Through Augmented Reality (Keynote Talk)." Web.
1. Kari Pulli. Immersive Optical-See-Through Augmented Reality (Keynote Talk) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2261

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
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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: Nov. 20, 2017.
@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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ID1185-Yiping Duan-tsinghua.pdf

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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: Nov. 20, 2017.
@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

GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS

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Authors:
Ji Bao, Hong Bu
Submitted On:
3 October 2017 - 4:28am
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ICIP_poster3433.pdf

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[1] Ji Bao, Hong Bu, "GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2253. Accessed: Nov. 20, 2017.
@article{2253-17,
url = {http://sigport.org/2253},
author = {Ji Bao; Hong Bu },
publisher = {IEEE SigPort},
title = {GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS},
year = {2017} }
TY - EJOUR
T1 - GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS
AU - Ji Bao; Hong Bu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2253
ER -
Ji Bao, Hong Bu. (2017). GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS. IEEE SigPort. http://sigport.org/2253
Ji Bao, Hong Bu, 2017. GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS. Available at: http://sigport.org/2253.
Ji Bao, Hong Bu. (2017). "GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS." Web.
1. Ji Bao, Hong Bu. GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2253

Probabilistic Approach to People-Centric Photo Selection and Sequencing


We present a crowdsourcing (CS) study to examine how specific attributes probabilistically affect the selection and sequencing of images from personal photo collections. 13 image attributes are explored, including 7 people-centric properties. We first propose a novel dataset shaping technique based on Mixed Integer Linear Programming (MILP) to identify a subset of photos in which the attributes of interest are uniformly distributed and minimally correlated.

poster.pdf

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Authors:
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler
Submitted On:
27 September 2017 - 11:08pm
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poster.pdf

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[1] Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler, "Probabilistic Approach to People-Centric Photo Selection and Sequencing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2250. Accessed: Nov. 20, 2017.
@article{2250-17,
url = {http://sigport.org/2250},
author = {Vassilios Vonikakis; Ramanathan Subramanian; Jonas Arnfred; Stefan Winkler },
publisher = {IEEE SigPort},
title = {Probabilistic Approach to People-Centric Photo Selection and Sequencing},
year = {2017} }
TY - EJOUR
T1 - Probabilistic Approach to People-Centric Photo Selection and Sequencing
AU - Vassilios Vonikakis; Ramanathan Subramanian; Jonas Arnfred; Stefan Winkler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2250
ER -
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler. (2017). Probabilistic Approach to People-Centric Photo Selection and Sequencing. IEEE SigPort. http://sigport.org/2250
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler, 2017. Probabilistic Approach to People-Centric Photo Selection and Sequencing. Available at: http://sigport.org/2250.
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler. (2017). "Probabilistic Approach to People-Centric Photo Selection and Sequencing." Web.
1. Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler. Probabilistic Approach to People-Centric Photo Selection and Sequencing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2250

BAFT: Binary Affine Feature Transform


We introduce BAFT, a fast binary and quasi affine invariant local image feature. It combines the affine invariance of Harris Affine feature descriptors with the speed of binary descriptors such as BRISK and ORB. BAFT derives its speed and precision from sampling local image patches in a pattern that depends on the second moment matrix of the same image patch. This approach results in a fast but discriminative descriptor, especially for image pairs with large perspective changes.

poster.pdf

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Authors:
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler
Submitted On:
27 September 2017 - 11:05pm
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poster.pdf

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[1] Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler, "BAFT: Binary Affine Feature Transform", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2249. Accessed: Nov. 20, 2017.
@article{2249-17,
url = {http://sigport.org/2249},
author = {Jonas T. Arnfred; Viet Dung Nguyen; Stefan Winkler },
publisher = {IEEE SigPort},
title = {BAFT: Binary Affine Feature Transform},
year = {2017} }
TY - EJOUR
T1 - BAFT: Binary Affine Feature Transform
AU - Jonas T. Arnfred; Viet Dung Nguyen; Stefan Winkler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2249
ER -
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler. (2017). BAFT: Binary Affine Feature Transform. IEEE SigPort. http://sigport.org/2249
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler, 2017. BAFT: Binary Affine Feature Transform. Available at: http://sigport.org/2249.
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler. (2017). "BAFT: Binary Affine Feature Transform." Web.
1. Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler. BAFT: Binary Affine Feature Transform [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2249

CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?

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21 September 2017 - 10:18am
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Landscape_Poster_ICIP_Xinwei LIU.pdf

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[1] , "CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2243. Accessed: Nov. 20, 2017.
@article{2243-17,
url = {http://sigport.org/2243},
author = { },
publisher = {IEEE SigPort},
title = {CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?},
year = {2017} }
TY - EJOUR
T1 - CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2243
ER -
. (2017). CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?. IEEE SigPort. http://sigport.org/2243
, 2017. CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?. Available at: http://sigport.org/2243.
. (2017). "CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY?." Web.
1. . CAN NO-REFERENCE IMAGE QUALITY METRICS ASSESS VISIBLE WAVELENGTH IRIS SAMPLE QUALITY? [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2243

Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox


Although many visual attention models have been proposed, very few saliency models investigated the impact of audio information. To develop audio-visual attention models, researchers need to have a ground truth of eye movements recorded while exploring complex natural scenes in different audio conditions. They also need tools to compare eye movements and gaze patterns between these different audio conditions.

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Authors:
Marighetto P., Coutrot A., Riche N., Guyader N., Mancas M., Gosselin B.
Submitted On:
20 September 2017 - 1:23am
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audiovisualSaliency.pdf

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[1] Marighetto P., Coutrot A., Riche N., Guyader N., Mancas M., Gosselin B., "Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2238. Accessed: Nov. 20, 2017.
@article{2238-17,
url = {http://sigport.org/2238},
author = {Marighetto P.; Coutrot A.; Riche N.; Guyader N.; Mancas M.; Gosselin B. },
publisher = {IEEE SigPort},
title = {Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox},
year = {2017} }
TY - EJOUR
T1 - Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox
AU - Marighetto P.; Coutrot A.; Riche N.; Guyader N.; Mancas M.; Gosselin B.
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2238
ER -
Marighetto P., Coutrot A., Riche N., Guyader N., Mancas M., Gosselin B.. (2017). Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox. IEEE SigPort. http://sigport.org/2238
Marighetto P., Coutrot A., Riche N., Guyader N., Mancas M., Gosselin B., 2017. Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox. Available at: http://sigport.org/2238.
Marighetto P., Coutrot A., Riche N., Guyader N., Mancas M., Gosselin B.. (2017). "Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox." Web.
1. Marighetto P., Coutrot A., Riche N., Guyader N., Mancas M., Gosselin B.. Audio-Visual Attention: Eye-Tracking Dataset and Analysis ToolBox [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2238

TA-L7.5. Efficient estimation of target detection quality


The capability of determining the quality of target detections is important for applications using smart cameras, such as autonomous robotics and surveillance. We propose to estimate the quality of target detections by integrating the target location uncertainty over polygonal domains, which represent the fields of view of the cameras. We define a framework based on numerical integration that easily accommodates multiple models for uncertainty and fields of view.

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Authors:
Andrea Cavallaro
Submitted On:
19 September 2017 - 12:19am
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2017.09.19--EFFICIENT ESTIMATION OF TARGET DETECTION QUALITY_v2.pdf

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[1] Andrea Cavallaro, "TA-L7.5. Efficient estimation of target detection quality", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2232. Accessed: Nov. 20, 2017.
@article{2232-17,
url = {http://sigport.org/2232},
author = {Andrea Cavallaro },
publisher = {IEEE SigPort},
title = {TA-L7.5. Efficient estimation of target detection quality},
year = {2017} }
TY - EJOUR
T1 - TA-L7.5. Efficient estimation of target detection quality
AU - Andrea Cavallaro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2232
ER -
Andrea Cavallaro. (2017). TA-L7.5. Efficient estimation of target detection quality. IEEE SigPort. http://sigport.org/2232
Andrea Cavallaro, 2017. TA-L7.5. Efficient estimation of target detection quality. Available at: http://sigport.org/2232.
Andrea Cavallaro. (2017). "TA-L7.5. Efficient estimation of target detection quality." Web.
1. Andrea Cavallaro. TA-L7.5. Efficient estimation of target detection quality [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2232

PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION


In the recent years, we experienced the proliferation of sensors for retrieving depth information on a scene, such as LIDAR or RGBD sensors (Kinect). However, it is still a challenge to identify the meaning of a specific point cloud to recognize the underlying object. Here, we wonder if it is possible to define a global feature for an object that is robust to noise, sampling and occlusion. We propose a local measure based on curvature. We called it Principal Curvatures because rather than using the Gaussian curvature we keep the

SPC.pdf

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Authors:
Justin Lev , Joo-Hwee Lim , Nizar Ouarti
Submitted On:
18 September 2017 - 2:39am
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SPC.pdf

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[1] Justin Lev , Joo-Hwee Lim , Nizar Ouarti, "PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2221. Accessed: Nov. 20, 2017.
@article{2221-17,
url = {http://sigport.org/2221},
author = {Justin Lev ; Joo-Hwee Lim ; Nizar Ouarti },
publisher = {IEEE SigPort},
title = {PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION
AU - Justin Lev ; Joo-Hwee Lim ; Nizar Ouarti
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2221
ER -
Justin Lev , Joo-Hwee Lim , Nizar Ouarti. (2017). PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION. IEEE SigPort. http://sigport.org/2221
Justin Lev , Joo-Hwee Lim , Nizar Ouarti, 2017. PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION. Available at: http://sigport.org/2221.
Justin Lev , Joo-Hwee Lim , Nizar Ouarti. (2017). "PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION." Web.
1. Justin Lev , Joo-Hwee Lim , Nizar Ouarti. PRINCIPAL CURVATURE OF POINT CLOUD FOR 3D SHAPE RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2221

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