Layered embeddings for amodal instance segmentation

Yanfeng Liu; Eric T. Psota; Lance C. Pérez

doi:10.1007/978-3-030-27202-9_9

Layered embeddings for amodal instance segmentation

Yanfeng Liu, Eric T. Psota, Lance C. Pérez

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

Abstract

The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings.

Original language	English (US)
Title of host publication	Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
Editors	Fakhri Karray, Alfred Yu, Aurélio Campilho
Publisher	Springer Verlag
Pages	102-111
Number of pages	10
ISBN (Print)	9783030272012
DOIs	https://doi.org/10.1007/978-3-030-27202-9_9
State	Published - 2019
Event	16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada Duration: Aug 27 2019 → Aug 29 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11662 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Image Analysis and Recognition, ICIAR 2019
Country/Territory	Canada
City	Waterloo
Period	8/27/19 → 8/29/19

Keywords

Amodal segmentation
Occlusion recovery
Pixel embedding
Semantic instance segmentation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-27202-9_9

Cite this

Liu, Y., Psota, E. T., & Pérez, L. C. (2019). Layered embeddings for amodal instance segmentation. In F. Karray, A. Yu, & A. Campilho (Eds.), Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings (pp. 102-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11662 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-27202-9_9

Layered embeddings for amodal instance segmentation. / Liu, Yanfeng; Psota, Eric T.; Pérez, Lance C.

Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings. ed. / Fakhri Karray; Alfred Yu; Aurélio Campilho. Springer Verlag, 2019. p. 102-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11662 LNCS).

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

Liu, Y, Psota, ET & Pérez, LC 2019, Layered embeddings for amodal instance segmentation. in F Karray, A Yu & A Campilho (eds), Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11662 LNCS, Springer Verlag, pp. 102-111, 16th International Conference on Image Analysis and Recognition, ICIAR 2019, Waterloo, Canada, 8/27/19. https://doi.org/10.1007/978-3-030-27202-9_9

Liu Y, Psota ET, Pérez LC. Layered embeddings for amodal instance segmentation. In Karray F, Yu A, Campilho A, editors, Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings. Springer Verlag. 2019. p. 102-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-27202-9_9

Liu, Yanfeng ; Psota, Eric T. ; Pérez, Lance C. / Layered embeddings for amodal instance segmentation. Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings. editor / Fakhri Karray ; Alfred Yu ; Aurélio Campilho. Springer Verlag, 2019. pp. 102-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at https://github.com/yanfengliu/layered_embeddings.",

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Layered embeddings for amodal instance segmentation

Abstract

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Keywords

ASJC Scopus subject areas

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