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Image, Video, and Multidimensional Signal Processing

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: May. 26, 2018.
@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: May. 26, 2018.
@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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[1] Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler, "BAFT: Binary Affine Feature Transform", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2249. Accessed: May. 26, 2018.
@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: May. 26, 2018.
@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: May. 26, 2018.
@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: May. 26, 2018.
@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: May. 26, 2018.
@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

REFLECTANCE-BASED SURFACE SALIENCY


In this paper, we propose an original methodology allowing the computation of the saliency maps for high dimensional RTI data (Reflectance Transformation Imaging). Unlike most of the classical methods, our approach aims at devising an intrinsic visual saliency of the surface, independent of the sensor (image) and the geometry of the scene (light-object-camera).

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Authors:
Gilles Pitard, Gaetan Le Goic, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George, Jon Yngve Hardeberg
Submitted On:
17 September 2017 - 10:21am
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ICIP_PITARD_Beijing

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[1] Gilles Pitard, Gaetan Le Goic, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George, Jon Yngve Hardeberg, "REFLECTANCE-BASED SURFACE SALIENCY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2213. Accessed: May. 26, 2018.
@article{2213-17,
url = {http://sigport.org/2213},
author = {Gilles Pitard; Gaetan Le Goic; Alamin Mansouri; Hugues Favrelière; Maurice Pillet; Sony George; Jon Yngve Hardeberg },
publisher = {IEEE SigPort},
title = {REFLECTANCE-BASED SURFACE SALIENCY},
year = {2017} }
TY - EJOUR
T1 - REFLECTANCE-BASED SURFACE SALIENCY
AU - Gilles Pitard; Gaetan Le Goic; Alamin Mansouri; Hugues Favrelière; Maurice Pillet; Sony George; Jon Yngve Hardeberg
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2213
ER -
Gilles Pitard, Gaetan Le Goic, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George, Jon Yngve Hardeberg. (2017). REFLECTANCE-BASED SURFACE SALIENCY. IEEE SigPort. http://sigport.org/2213
Gilles Pitard, Gaetan Le Goic, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George, Jon Yngve Hardeberg, 2017. REFLECTANCE-BASED SURFACE SALIENCY. Available at: http://sigport.org/2213.
Gilles Pitard, Gaetan Le Goic, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George, Jon Yngve Hardeberg. (2017). "REFLECTANCE-BASED SURFACE SALIENCY." Web.
1. Gilles Pitard, Gaetan Le Goic, Alamin Mansouri, Hugues Favrelière, Maurice Pillet, Sony George, Jon Yngve Hardeberg. REFLECTANCE-BASED SURFACE SALIENCY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2213

LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING

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Authors:
Giuseppe Valenzise, Frederic Dufaux
Submitted On:
17 September 2017 - 7:07am
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[1] Giuseppe Valenzise, Frederic Dufaux, "LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2212. Accessed: May. 26, 2018.
@article{2212-17,
url = {http://sigport.org/2212},
author = {Giuseppe Valenzise; Frederic Dufaux },
publisher = {IEEE SigPort},
title = {LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING},
year = {2017} }
TY - EJOUR
T1 - LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING
AU - Giuseppe Valenzise; Frederic Dufaux
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2212
ER -
Giuseppe Valenzise, Frederic Dufaux. (2017). LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING. IEEE SigPort. http://sigport.org/2212
Giuseppe Valenzise, Frederic Dufaux, 2017. LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING. Available at: http://sigport.org/2212.
Giuseppe Valenzise, Frederic Dufaux. (2017). "LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING." Web.
1. Giuseppe Valenzise, Frederic Dufaux. LEARNING-BASED TONE MAPPING OPERATOR FOR IMAGE MATCHING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2212

Deep Discovery of Facial Motions using a Shallow Embedding Layer


Unique encoding of the dynamics of facial actions has potential to provide a spontaneous facial expression recognition system. The most promising existing approaches rely on deep learning of facial actions. However, current approaches are often computationally intensive and require a great deal of memory/processing time, and typically the temporal aspect of facial actions are often ignored, despite the potential wealth of information available from the spatial dynamic movements and their temporal evolution over time from neutral state to apex state.

ICIP.pdf

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Authors:
Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes
Submitted On:
17 September 2017 - 4:03am
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[1] Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes, "Deep Discovery of Facial Motions using a Shallow Embedding Layer", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2211. Accessed: May. 26, 2018.
@article{2211-17,
url = {http://sigport.org/2211},
author = {Afsaneh Ghasemi; Mahsa Baktashmotlagh; Simon Denman; Sridha Sridharan; Dung Nguyen Tien; Clinton Fookes },
publisher = {IEEE SigPort},
title = {Deep Discovery of Facial Motions using a Shallow Embedding Layer},
year = {2017} }
TY - EJOUR
T1 - Deep Discovery of Facial Motions using a Shallow Embedding Layer
AU - Afsaneh Ghasemi; Mahsa Baktashmotlagh; Simon Denman; Sridha Sridharan; Dung Nguyen Tien; Clinton Fookes
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2211
ER -
Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes. (2017). Deep Discovery of Facial Motions using a Shallow Embedding Layer. IEEE SigPort. http://sigport.org/2211
Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes, 2017. Deep Discovery of Facial Motions using a Shallow Embedding Layer. Available at: http://sigport.org/2211.
Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes. (2017). "Deep Discovery of Facial Motions using a Shallow Embedding Layer." Web.
1. Afsaneh Ghasemi, Mahsa Baktashmotlagh, Simon Denman, Sridha Sridharan, Dung Nguyen Tien, Clinton Fookes. Deep Discovery of Facial Motions using a Shallow Embedding Layer [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2211

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