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Proceedings Paper

2.5d body estimation via refined forest with field-based objective
Author(s): Jaehwan Kim; HoWon Kim
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Paper Abstract

In this paper, we present a 2.5D* body region classification method based on the global refinement of random forest. The refinement of random forest provides the reduction of the size of training model with preserving prediction accuracy. We also incorporate the field-inspired objective to the random forest in consideration of the pairwise spatial relationships between neighboring data points. Numerical and visual experiments with artificial 3D data confirm the usefulness of the proposed method.

Paper Details

Date Published: 13 April 2018
PDF: 5 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962E (13 April 2018); doi: 10.1117/12.2310059
Show Author Affiliations
Jaehwan Kim, Electronics and Telecommunications Research Institute (Korea, Republic of)
HoWon Kim, Electronics and Telecommunications Research Institute (Korea, Republic of)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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