Deep Patch-Based Human Segmentation

Dongbo Zhang; Zheng Fang; Xuequan Lu; Hong Qin; Antonio Robles-Kelly; Chao Zhang; Ying He

doi:10.1007/978-3-030-63830-6_20

Deep Patch-Based Human Segmentation

Dongbo Zhang
, Zheng Fang
, Xuequan Lu
, Hong Qin
, Antonio Robles-Kelly
, Chao Zhang
, Ying He

Computer Science

Beihang University
Nanyang Technological University
Deakin University
University of Fukui

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

4 Scopus citations

Abstract

3D human segmentation has seen noticeable progress in recent years. It, however, still remains a challenge to date. In this paper, we introduce a deep patch-based method for 3D human segmentation. We first extract a local surface patch for each vertex and then parameterize it into a 2D grid (or image). We then embed identified shape descriptors into the 2D grids which are further fed into the powerful 2D Convolutional Neural Network for regressing corresponding semantic labels (e.g., head, torso). Experiments demonstrate that our method is effective in human segmentation, and achieves state-of-the-art accuracy.

Original language	English
Title of host publication	Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
Editors	Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	229-240
Number of pages	12
ISBN (Print)	9783030638290
DOIs	https://doi.org/10.1007/978-3-030-63830-6_20
State	Published - 2020
Event	27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand Duration: Nov 18 2020 → Nov 22 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12532 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Neural Information Processing, ICONIP 2020
Country/Territory	Thailand
City	Bangkok
Period	11/18/20 → 11/22/20

Keywords

Deep learning
Human segmentation
Parameterization
Shape descriptors

Access to Document

10.1007/978-3-030-63830-6_20

Cite this

Zhang, D., Fang, Z., Lu, X., Qin, H., Robles-Kelly, A., Zhang, C., & He, Y. (2020). Deep Patch-Based Human Segmentation. In H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings (pp. 229-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12532 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63830-6_20

Zhang, Dongbo ; Fang, Zheng ; Lu, Xuequan et al. / Deep Patch-Based Human Segmentation. Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. editor / Haiqin Yang ; Kitsuchart Pasupa ; Andrew Chi-Sing Leung ; James T. Kwok ; Jonathan H. Chan ; Irwin King. Springer Science and Business Media Deutschland GmbH, 2020. pp. 229-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{027e890f857f41eea7891f3ad7f1e3a4,

title = "Deep Patch-Based Human Segmentation",

abstract = "3D human segmentation has seen noticeable progress in recent years. It, however, still remains a challenge to date. In this paper, we introduce a deep patch-based method for 3D human segmentation. We first extract a local surface patch for each vertex and then parameterize it into a 2D grid (or image). We then embed identified shape descriptors into the 2D grids which are further fed into the powerful 2D Convolutional Neural Network for regressing corresponding semantic labels (e.g., head, torso). Experiments demonstrate that our method is effective in human segmentation, and achieves state-of-the-art accuracy.",

keywords = "Deep learning, Human segmentation, Parameterization, Shape descriptors",

author = "Dongbo Zhang and Zheng Fang and Xuequan Lu and Hong Qin and Antonio Robles-Kelly and Chao Zhang and Ying He",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 27th International Conference on Neural Information Processing, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",

year = "2020",

doi = "10.1007/978-3-030-63830-6\_20",

language = "English",

isbn = "9783030638290",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "229--240",

editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, \{Andrew Chi-Sing\} and Kwok, \{James T.\} and Chan, \{Jonathan H.\} and Irwin King",

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}

Zhang, D, Fang, Z, Lu, X, Qin, H, Robles-Kelly, A, Zhang, C & He, Y 2020, Deep Patch-Based Human Segmentation. in H Yang, K Pasupa, AC-S Leung, JT Kwok, JH Chan & I King (eds), Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12532 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 229-240, 27th International Conference on Neural Information Processing, ICONIP 2020, Bangkok, Thailand, 11/18/20. https://doi.org/10.1007/978-3-030-63830-6_20

Deep Patch-Based Human Segmentation. / Zhang, Dongbo; Fang, Zheng; Lu, Xuequan et al.
Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. ed. / Haiqin Yang; Kitsuchart Pasupa; Andrew Chi-Sing Leung; James T. Kwok; Jonathan H. Chan; Irwin King. Springer Science and Business Media Deutschland GmbH, 2020. p. 229-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12532 LNCS).

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

TY - GEN

T1 - Deep Patch-Based Human Segmentation

AU - Zhang, Dongbo

AU - Fang, Zheng

AU - Lu, Xuequan

AU - Qin, Hong

AU - Robles-Kelly, Antonio

AU - Zhang, Chao

AU - He, Ying

PY - 2020

Y1 - 2020

N2 - 3D human segmentation has seen noticeable progress in recent years. It, however, still remains a challenge to date. In this paper, we introduce a deep patch-based method for 3D human segmentation. We first extract a local surface patch for each vertex and then parameterize it into a 2D grid (or image). We then embed identified shape descriptors into the 2D grids which are further fed into the powerful 2D Convolutional Neural Network for regressing corresponding semantic labels (e.g., head, torso). Experiments demonstrate that our method is effective in human segmentation, and achieves state-of-the-art accuracy.

AB - 3D human segmentation has seen noticeable progress in recent years. It, however, still remains a challenge to date. In this paper, we introduce a deep patch-based method for 3D human segmentation. We first extract a local surface patch for each vertex and then parameterize it into a 2D grid (or image). We then embed identified shape descriptors into the 2D grids which are further fed into the powerful 2D Convolutional Neural Network for regressing corresponding semantic labels (e.g., head, torso). Experiments demonstrate that our method is effective in human segmentation, and achieves state-of-the-art accuracy.

KW - Deep learning

KW - Human segmentation

KW - Parameterization

KW - Shape descriptors

UR - https://www.scopus.com/pages/publications/85097075532

U2 - 10.1007/978-3-030-63830-6_20

DO - 10.1007/978-3-030-63830-6_20

M3 - Conference contribution

AN - SCOPUS:85097075532

SN - 9783030638290

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 229

EP - 240

BT - Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings

A2 - Yang, Haiqin

A2 - Pasupa, Kitsuchart

A2 - Leung, Andrew Chi-Sing

A2 - Kwok, James T.

A2 - Chan, Jonathan H.

A2 - King, Irwin

PB - Springer Science and Business Media Deutschland GmbH

T2 - 27th International Conference on Neural Information Processing, ICONIP 2020

Y2 - 18 November 2020 through 22 November 2020

ER -

Zhang D, Fang Z, Lu X, Qin H, Robles-Kelly A, Zhang C et al. Deep Patch-Based Human Segmentation. In Yang H, Pasupa K, Leung ACS, Kwok JT, Chan JH, King I, editors, Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 229-240. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63830-6_20

Deep Patch-Based Human Segmentation

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Keywords

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