Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels

Hao Zhu; Xiaoming Sun; Qi Zhang; Qing Wang; Antonio Robles-Kelly; Hongdong Li

doi:10.1007/978-3-030-21074-8_1

Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels

Hao Zhu
, Xiaoming Sun
, Qi Zhang
, Qing Wang
, Antonio Robles-Kelly
, Hongdong Li

School of Computer Science

Northwestern Polytechnical University Xian
Deakin University
Australian National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Optical flow estimation is one of the most important problem in community. However, current methods still can not provide reliable results in occlusion boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) as a slanted plane in 3D. Then the motion of the occluded pixels in central view slice can be optimized by the un-occluded pixels in other views. Thus the optical flow in occlusion boundary areas can be well computed. Experimental results on both synthetic and real light fields demonstrate the advantages over state-of-the-arts and the performance on 4D optical flow computation.

Original language	English
Title of host publication	Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers
Editors	Gustavo Carneiro, Shaodi You
Publisher	Springer Verlag
Pages	3-12
Number of pages	10
ISBN (Print)	9783030210731
DOIs	https://doi.org/10.1007/978-3-030-21074-8_1
State	Published - 2019
Event	2018 Scene Understanding and Modelling Challenge, SUMO 2018, 2018Learning and Inference Methods for High-Performance Imaging, LIMHPI 2018, 2018 Attention/Intention Understanding, AIU 2018, 2018 Museum Exhibit Identification Challenge for Domain Adaptation and Few-Shot Learning, 2018 RGB-D—Sensing and Understanding via Combined Color and Depth, 2018 Dense 3D Reconstruction for Dynamic Scenes, 2018 AI Aesthetics in Art and Media, AIAM 2018, 3rd International Workshop on Robust Reading, IWRR 2018, 2018 Artificial Intelligence for Retinal Image Analysis, AIRIA 2018, 2018 Combining Vision and Language, 1st International Workshop on Advanced Machine Vision for Real-Life and Industrially Relevant Applications, AMV 2018 - Perth, Australia Duration: 2 Dec 2018 → 6 Dec 2018

Publication series

Name	Lecture Notes in Computer Science
Volume	11367 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2018 Scene Understanding and Modelling Challenge, SUMO 2018, 2018Learning and Inference Methods for High-Performance Imaging, LIMHPI 2018, 2018 Attention/Intention Understanding, AIU 2018, 2018 Museum Exhibit Identification Challenge for Domain Adaptation and Few-Shot Learning, 2018 RGB-D—Sensing and Understanding via Combined Color and Depth, 2018 Dense 3D Reconstruction for Dynamic Scenes, 2018 AI Aesthetics in Art and Media, AIAM 2018, 3rd International Workshop on Robust Reading, IWRR 2018, 2018 Artificial Intelligence for Retinal Image Analysis, AIRIA 2018, 2018 Combining Vision and Language, 1st International Workshop on Advanced Machine Vision for Real-Life and Industrially Relevant Applications, AMV 2018
Country/Territory	Australia
City	Perth
Period	2/12/18 → 6/12/18

Keywords

Light field
Optical flow

Access to Document

10.1007/978-3-030-21074-8_1

Cite this

Zhu, H., Sun, X., Zhang, Q., Wang, Q., Robles-Kelly, A., & Li, H. (2019). Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels. In G. Carneiro, & S. You (Eds.), Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers (pp. 3-12). (Lecture Notes in Computer Science; Vol. 11367 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-21074-8_1

@inproceedings{f7fe0fe53e4545acba6b24faaefea945,

title = "Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels",

abstract = "Optical flow estimation is one of the most important problem in community. However, current methods still can not provide reliable results in occlusion boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) as a slanted plane in 3D. Then the motion of the occluded pixels in central view slice can be optimized by the un-occluded pixels in other views. Thus the optical flow in occlusion boundary areas can be well computed. Experimental results on both synthetic and real light fields demonstrate the advantages over state-of-the-arts and the performance on 4D optical flow computation.",

keywords = "Light field, Optical flow",

author = "Hao Zhu and Xiaoming Sun and Qi Zhang and Qing Wang and Antonio Robles-Kelly and Hongdong Li",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 2018 Scene Understanding and Modelling Challenge, SUMO 2018, 2018Learning and Inference Methods for High-Performance Imaging, LIMHPI 2018, 2018 Attention/Intention Understanding, AIU 2018, 2018 Museum Exhibit Identification Challenge for Domain Adaptation and Few-Shot Learning, 2018 RGB-D—Sensing and Understanding via Combined Color and Depth, 2018 Dense 3D Reconstruction for Dynamic Scenes, 2018 AI Aesthetics in Art and Media, AIAM 2018, 3rd International Workshop on Robust Reading, IWRR 2018, 2018 Artificial Intelligence for Retinal Image Analysis, AIRIA 2018, 2018 Combining Vision and Language, 1st International Workshop on Advanced Machine Vision for Real-Life and Industrially Relevant Applications, AMV 2018 ; Conference date: 02-12-2018 Through 06-12-2018",

year = "2019",

doi = "10.1007/978-3-030-21074-8\_1",

language = "英语",

isbn = "9783030210731",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

pages = "3--12",

editor = "Gustavo Carneiro and Shaodi You",

booktitle = "Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers",

}

Zhu, H, Sun, X, Zhang, Q, Wang, Q, Robles-Kelly, A & Li, H 2019, Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels. in G Carneiro & S You (eds), Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers. Lecture Notes in Computer Science, vol. 11367 LNCS, Springer Verlag, pp. 3-12, 2018 Scene Understanding and Modelling Challenge, SUMO 2018, 2018Learning and Inference Methods for High-Performance Imaging, LIMHPI 2018, 2018 Attention/Intention Understanding, AIU 2018, 2018 Museum Exhibit Identification Challenge for Domain Adaptation and Few-Shot Learning, 2018 RGB-D—Sensing and Understanding via Combined Color and Depth, 2018 Dense 3D Reconstruction for Dynamic Scenes, 2018 AI Aesthetics in Art and Media, AIAM 2018, 3rd International Workshop on Robust Reading, IWRR 2018, 2018 Artificial Intelligence for Retinal Image Analysis, AIRIA 2018, 2018 Combining Vision and Language, 1st International Workshop on Advanced Machine Vision for Real-Life and Industrially Relevant Applications, AMV 2018, Perth, Australia, 2/12/18. https://doi.org/10.1007/978-3-030-21074-8_1

Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels. / Zhu, Hao; Sun, Xiaoming; Zhang, Qi et al.
Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers. ed. / Gustavo Carneiro; Shaodi You. Springer Verlag, 2019. p. 3-12 (Lecture Notes in Computer Science; Vol. 11367 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels

AU - Zhu, Hao

AU - Sun, Xiaoming

AU - Zhang, Qi

AU - Wang, Qing

AU - Robles-Kelly, Antonio

AU - Li, Hongdong

PY - 2019

Y1 - 2019

N2 - Optical flow estimation is one of the most important problem in community. However, current methods still can not provide reliable results in occlusion boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) as a slanted plane in 3D. Then the motion of the occluded pixels in central view slice can be optimized by the un-occluded pixels in other views. Thus the optical flow in occlusion boundary areas can be well computed. Experimental results on both synthetic and real light fields demonstrate the advantages over state-of-the-arts and the performance on 4D optical flow computation.

AB - Optical flow estimation is one of the most important problem in community. However, current methods still can not provide reliable results in occlusion boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) as a slanted plane in 3D. Then the motion of the occluded pixels in central view slice can be optimized by the un-occluded pixels in other views. Thus the optical flow in occlusion boundary areas can be well computed. Experimental results on both synthetic and real light fields demonstrate the advantages over state-of-the-arts and the performance on 4D optical flow computation.

KW - Light field

KW - Optical flow

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DO - 10.1007/978-3-030-21074-8_1

M3 - 会议稿件

AN - SCOPUS:85068479114

SN - 9783030210731

T3 - Lecture Notes in Computer Science

SP - 3

EP - 12

BT - Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers

A2 - Carneiro, Gustavo

A2 - You, Shaodi

PB - Springer Verlag

T2 - 2018 Scene Understanding and Modelling Challenge, SUMO 2018, 2018Learning and Inference Methods for High-Performance Imaging, LIMHPI 2018, 2018 Attention/Intention Understanding, AIU 2018, 2018 Museum Exhibit Identification Challenge for Domain Adaptation and Few-Shot Learning, 2018 RGB-D—Sensing and Understanding via Combined Color and Depth, 2018 Dense 3D Reconstruction for Dynamic Scenes, 2018 AI Aesthetics in Art and Media, AIAM 2018, 3rd International Workshop on Robust Reading, IWRR 2018, 2018 Artificial Intelligence for Retinal Image Analysis, AIRIA 2018, 2018 Combining Vision and Language, 1st International Workshop on Advanced Machine Vision for Real-Life and Industrially Relevant Applications, AMV 2018

Y2 - 2 December 2018 through 6 December 2018

ER -

Zhu H, Sun X, Zhang Q, Wang Q, Robles-Kelly A, Li H. Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels. In Carneiro G, You S, editors, Computer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers. Springer Verlag. 2019. p. 3-12. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-21074-8_1

Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels

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