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ICIP 2018

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website.

Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation

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Authors:
Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez and Fernando Alonso-Fernandez
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8 October 2018 - 3:08am
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Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation

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[1] Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez and Fernando Alonso-Fernandez, "Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3615. Accessed: Dec. 10, 2018.
@article{3615-18,
url = {http://sigport.org/3615},
author = {Ester Gonzalez-Sosa; Julian Fierrez; Ruben Vera-Rodriguez and Fernando Alonso-Fernandez },
publisher = {IEEE SigPort},
title = {Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation},
year = {2018} }
TY - EJOUR
T1 - Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation
AU - Ester Gonzalez-Sosa; Julian Fierrez; Ruben Vera-Rodriguez and Fernando Alonso-Fernandez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3615
ER -
Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez and Fernando Alonso-Fernandez. (2018). Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation. IEEE SigPort. http://sigport.org/3615
Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez and Fernando Alonso-Fernandez, 2018. Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation. Available at: http://sigport.org/3615.
Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez and Fernando Alonso-Fernandez. (2018). "Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation." Web.
1. Ester Gonzalez-Sosa, Julian Fierrez, Ruben Vera-Rodriguez and Fernando Alonso-Fernandez. Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and COTS Evaluation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3615

Normal Similarity Network for Generative Modelling

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Authors:
Jay Nandy, Wynne Hsu, Lee Mong Li
Submitted On:
8 October 2018 - 3:12am
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icip (2).pdf

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[1] Jay Nandy, Wynne Hsu, Lee Mong Li, "Normal Similarity Network for Generative Modelling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3614. Accessed: Dec. 10, 2018.
@article{3614-18,
url = {http://sigport.org/3614},
author = {Jay Nandy; Wynne Hsu; Lee Mong Li },
publisher = {IEEE SigPort},
title = {Normal Similarity Network for Generative Modelling},
year = {2018} }
TY - EJOUR
T1 - Normal Similarity Network for Generative Modelling
AU - Jay Nandy; Wynne Hsu; Lee Mong Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3614
ER -
Jay Nandy, Wynne Hsu, Lee Mong Li. (2018). Normal Similarity Network for Generative Modelling. IEEE SigPort. http://sigport.org/3614
Jay Nandy, Wynne Hsu, Lee Mong Li, 2018. Normal Similarity Network for Generative Modelling. Available at: http://sigport.org/3614.
Jay Nandy, Wynne Hsu, Lee Mong Li. (2018). "Normal Similarity Network for Generative Modelling." Web.
1. Jay Nandy, Wynne Hsu, Lee Mong Li. Normal Similarity Network for Generative Modelling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3614

3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images

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Authors:
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola
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8 October 2018 - 3:04am
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ICIP_glioma_grading_IG.pdf

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[1] Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola , "3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3613. Accessed: Dec. 10, 2018.
@article{3613-18,
url = {http://sigport.org/3613},
author = {Chenjie Ge; Qixun Qu; Irene YH Gu; Asgeir S Jakola },
publisher = {IEEE SigPort},
title = {3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images},
year = {2018} }
TY - EJOUR
T1 - 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images
AU - Chenjie Ge; Qixun Qu; Irene YH Gu; Asgeir S Jakola
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3613
ER -
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola . (2018). 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images. IEEE SigPort. http://sigport.org/3613
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola , 2018. 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images. Available at: http://sigport.org/3613.
Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola . (2018). "3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images." Web.
1. Chenjie Ge, Qixun Qu, Irene YH Gu, Asgeir S Jakola . 3D Multi-Scale Convolutional Networks For Glioma Grading using MR Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3613

TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING


Recent low-rank based tensor completion (LRTC) algorithms have been successfully applied into color image inpainting. However, most of existing LRTC algorithms treat each dimension of tensors equally, which ignores the differences of the intrinsic structure correlations among dimensions. In this paper, we make a detailed analysis about the rank properties of each dimension and design a simple yet effective reweighted low-rank tensor completion model that truthfully capture the intrinsic structure correlations with reduced computational burden.

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Authors:
Fei Jiang, Ruimin Shen
Submitted On:
8 October 2018 - 3:01am
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ICIP183018.pdf

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[1] Fei Jiang, Ruimin Shen, "TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3612. Accessed: Dec. 10, 2018.
@article{3612-18,
url = {http://sigport.org/3612},
author = {Fei Jiang; Ruimin Shen },
publisher = {IEEE SigPort},
title = {TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING},
year = {2018} }
TY - EJOUR
T1 - TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING
AU - Fei Jiang; Ruimin Shen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3612
ER -
Fei Jiang, Ruimin Shen. (2018). TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING. IEEE SigPort. http://sigport.org/3612
Fei Jiang, Ruimin Shen, 2018. TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING. Available at: http://sigport.org/3612.
Fei Jiang, Ruimin Shen. (2018). "TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING." Web.
1. Fei Jiang, Ruimin Shen. TOTAL VARIATION REGULARIZED REWEIGHTED LOW-RANK TENSOR COMPLETION FOR COLOR IMAGE INPAINTING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3612

A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding

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8 October 2018 - 2:50am
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A machine learning approach to accurate sequence-level rate control scheme for video coding.1.1.pdf

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[1] , "A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3611. Accessed: Dec. 10, 2018.
@article{3611-18,
url = {http://sigport.org/3611},
author = { },
publisher = {IEEE SigPort},
title = {A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding},
year = {2018} }
TY - EJOUR
T1 - A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3611
ER -
. (2018). A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding. IEEE SigPort. http://sigport.org/3611
, 2018. A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding. Available at: http://sigport.org/3611.
. (2018). "A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding." Web.
1. . A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3611

A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS

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Authors:
Bruna Frade, Erickson Naecimento
Submitted On:
8 October 2018 - 2:49am
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ICIP18_Poster.pdf

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[1] Bruna Frade, Erickson Naecimento, "A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3610. Accessed: Dec. 10, 2018.
@article{3610-18,
url = {http://sigport.org/3610},
author = {Bruna Frade; Erickson Naecimento },
publisher = {IEEE SigPort},
title = {A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS},
year = {2018} }
TY - EJOUR
T1 - A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS
AU - Bruna Frade; Erickson Naecimento
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3610
ER -
Bruna Frade, Erickson Naecimento. (2018). A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS. IEEE SigPort. http://sigport.org/3610
Bruna Frade, Erickson Naecimento, 2018. A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS. Available at: http://sigport.org/3610.
Bruna Frade, Erickson Naecimento. (2018). "A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS." Web.
1. Bruna Frade, Erickson Naecimento. A TWO-STEP LEARNING METHOD FOR DETECTING LANDMARKS ON FACES FROM DIFFERENT DOMAINS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3610

Visual-Quality-driven Learning for Underwater Vision Enhancement


The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process.

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Authors:
Walysson V Barbosa, Henrique G B Amaral, Thiago L Rocha, Erickson R Nascimento
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8 October 2018 - 2:42am
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ICIP2018 (2).pdf

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[1] Walysson V Barbosa, Henrique G B Amaral, Thiago L Rocha, Erickson R Nascimento, "Visual-Quality-driven Learning for Underwater Vision Enhancement", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3609. Accessed: Dec. 10, 2018.
@article{3609-18,
url = {http://sigport.org/3609},
author = {Walysson V Barbosa; Henrique G B Amaral; Thiago L Rocha; Erickson R Nascimento },
publisher = {IEEE SigPort},
title = {Visual-Quality-driven Learning for Underwater Vision Enhancement},
year = {2018} }
TY - EJOUR
T1 - Visual-Quality-driven Learning for Underwater Vision Enhancement
AU - Walysson V Barbosa; Henrique G B Amaral; Thiago L Rocha; Erickson R Nascimento
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3609
ER -
Walysson V Barbosa, Henrique G B Amaral, Thiago L Rocha, Erickson R Nascimento. (2018). Visual-Quality-driven Learning for Underwater Vision Enhancement. IEEE SigPort. http://sigport.org/3609
Walysson V Barbosa, Henrique G B Amaral, Thiago L Rocha, Erickson R Nascimento, 2018. Visual-Quality-driven Learning for Underwater Vision Enhancement. Available at: http://sigport.org/3609.
Walysson V Barbosa, Henrique G B Amaral, Thiago L Rocha, Erickson R Nascimento. (2018). "Visual-Quality-driven Learning for Underwater Vision Enhancement." Web.
1. Walysson V Barbosa, Henrique G B Amaral, Thiago L Rocha, Erickson R Nascimento. Visual-Quality-driven Learning for Underwater Vision Enhancement [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3609

Can DNNs Learn to Lipread Full Sentences ?


Finding visual features and suitable models for lipreading tasks that are more complex than a well-constrained vocabulary has proven challenging. This paper explores state-of-the-art Deep Neural Network architectures for lipreading based on a Sequence to Sequence Recurrent Neural Network. We report results for both hand-crafted and 2D/3D Convolutional Neural Network visual front-ends, online monotonic attention, and a joint Connectionist Temporal Classification-Sequence-to-Sequence loss.

slides.pdf

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Authors:
George Sterpu, Christian Saam, Naomi Harte
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8 October 2018 - 1:50am
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slides.pdf

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[1] George Sterpu, Christian Saam, Naomi Harte, "Can DNNs Learn to Lipread Full Sentences ?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3608. Accessed: Dec. 10, 2018.
@article{3608-18,
url = {http://sigport.org/3608},
author = {George Sterpu; Christian Saam; Naomi Harte },
publisher = {IEEE SigPort},
title = {Can DNNs Learn to Lipread Full Sentences ?},
year = {2018} }
TY - EJOUR
T1 - Can DNNs Learn to Lipread Full Sentences ?
AU - George Sterpu; Christian Saam; Naomi Harte
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3608
ER -
George Sterpu, Christian Saam, Naomi Harte. (2018). Can DNNs Learn to Lipread Full Sentences ?. IEEE SigPort. http://sigport.org/3608
George Sterpu, Christian Saam, Naomi Harte, 2018. Can DNNs Learn to Lipread Full Sentences ?. Available at: http://sigport.org/3608.
George Sterpu, Christian Saam, Naomi Harte. (2018). "Can DNNs Learn to Lipread Full Sentences ?." Web.
1. George Sterpu, Christian Saam, Naomi Harte. Can DNNs Learn to Lipread Full Sentences ? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3608

A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES


Great progresses have been achieved on object detection in the wild. However, it still remains a challenging problem due to tiny objects. In this paper, we present a Three-category Classification Neural Network to find tiny faces under complex environments by leveraging contextual information around faces. Tiny faces (within 20x20 pixels) are so fuzzy that the facial patterns are not clear or even ambiguous for detection.

poster.pdf

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Authors:
Feng Jiang, Jie Zhang, Liping Yan, Yuanqing Xia, Shiguang Shan
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8 October 2018 - 12:41am
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poster.pdf

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[1] Feng Jiang, Jie Zhang, Liping Yan, Yuanqing Xia, Shiguang Shan, "A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3607. Accessed: Dec. 10, 2018.
@article{3607-18,
url = {http://sigport.org/3607},
author = {Feng Jiang; Jie Zhang; Liping Yan; Yuanqing Xia; Shiguang Shan },
publisher = {IEEE SigPort},
title = {A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES},
year = {2018} }
TY - EJOUR
T1 - A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES
AU - Feng Jiang; Jie Zhang; Liping Yan; Yuanqing Xia; Shiguang Shan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3607
ER -
Feng Jiang, Jie Zhang, Liping Yan, Yuanqing Xia, Shiguang Shan. (2018). A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES. IEEE SigPort. http://sigport.org/3607
Feng Jiang, Jie Zhang, Liping Yan, Yuanqing Xia, Shiguang Shan, 2018. A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES. Available at: http://sigport.org/3607.
Feng Jiang, Jie Zhang, Liping Yan, Yuanqing Xia, Shiguang Shan. (2018). "A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES." Web.
1. Feng Jiang, Jie Zhang, Liping Yan, Yuanqing Xia, Shiguang Shan. A THREE-CATEGORY FACE DETECTOR WITH CONTEXTUAL INFORMATION ON FINDING TINY FACES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3607

TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA


Light field acquisition technologies capture spatial and angular information of the scene. The angular information paves
the way for various post-processing applications, e.g. scene reconstruction, refocusing, synthetic aperture. The light field
is usually captured by a single plenoptic camera or by multiple traditional cameras. The former captures dense light

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Authors:
Roger Olsson, Mårten Sjöström
Submitted On:
8 October 2018 - 12:39am
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AhmadWaqas_ICIP2018.pptx

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[1] Roger Olsson, Mårten Sjöström, "TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3606. Accessed: Dec. 10, 2018.
@article{3606-18,
url = {http://sigport.org/3606},
author = {Roger Olsson; Mårten Sjöström },
publisher = {IEEE SigPort},
title = {TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA},
year = {2018} }
TY - EJOUR
T1 - TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA
AU - Roger Olsson; Mårten Sjöström
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3606
ER -
Roger Olsson, Mårten Sjöström. (2018). TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA. IEEE SigPort. http://sigport.org/3606
Roger Olsson, Mårten Sjöström, 2018. TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA. Available at: http://sigport.org/3606.
Roger Olsson, Mårten Sjöström. (2018). "TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA." Web.
1. Roger Olsson, Mårten Sjöström. TOWARDS A GENERIC COMPRESSION SOLUTION FOR DENSELY AND SPARSELY SAMPLED LIGHT FIELD DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3606

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