Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

Alessandro Di Martino; Erik Bodin; Carl Henrik Ek

doi:10.1007/978-3-030-20870-71

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

Alessandro Di Martino, Erik Bodin, Carl Henrik Ek

Bristol Doctoral College

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Original language	Undefined/Unknown
Title of host publication	Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV
Editors	C. V. Jawahar, Hongdong Li, Greg Mori, Konrad Schindler
Publisher	Springer
Pages	3-20
Number of pages	18
Volume	11364
DOIs	https://doi.org/10.1007/978-3-030-20870-71
Publication status	Published - 2018

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer

Access to Document

10.1007/978-3-030-20870-71

https://doi.org/10.1007/978-3-030-20870-71

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Cite this

Martino, A. D., Bodin, E., & Ek, C. H. (2018). Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. In C. V. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV (Vol. 11364, pp. 3-20). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-20870-71

Martino, Alessandro Di ; Bodin, Erik ; Ek, Carl Henrik. / Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV. editor / C. V. Jawahar ; Hongdong Li ; Greg Mori ; Konrad Schindler. Vol. 11364 Springer, 2018. pp. 3-20 (Lecture Notes in Computer Science).

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title = "Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation",

author = "Martino, \{Alessandro Di\} and Erik Bodin and Ek, \{Carl Henrik\}",

year = "2018",

doi = "10.1007/978-3-030-20870-71",

language = "Undefined/Unknown",

volume = "11364",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "3--20",

editor = "Jawahar, \{C. V.\} and Hongdong Li and Greg Mori and Konrad Schindler",

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address = "United States",

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Martino, AD, Bodin, E & Ek, CH 2018, Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. in CV Jawahar, H Li, G Mori & K Schindler (eds), Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV. vol. 11364, Lecture Notes in Computer Science, Springer, pp. 3-20. https://doi.org/10.1007/978-3-030-20870-71

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. / Martino, Alessandro Di; Bodin, Erik; Ek, Carl Henrik.
Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV. ed. / C. V. Jawahar; Hongdong Li; Greg Mori; Konrad Schindler. Vol. 11364 Springer, 2018. p. 3-20 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

AU - Martino, Alessandro Di

AU - Bodin, Erik

AU - Ek, Carl Henrik

PY - 2018

Y1 - 2018

U2 - 10.1007/978-3-030-20870-71

DO - 10.1007/978-3-030-20870-71

M3 - Conference Contribution (Conference Proceeding)

VL - 11364

T3 - Lecture Notes in Computer Science

SP - 3

EP - 20

BT - Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV

A2 - Jawahar, C. V.

A2 - Li, Hongdong

A2 - Mori, Greg

A2 - Schindler, Konrad

PB - Springer

ER -

Martino AD, Bodin E, Ek CH. Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. In Jawahar CV, Li H, Mori G, Schindler K, editors, Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV. Vol. 11364. Springer. 2018. p. 3-20. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-20870-71