2.5d body estimation via refined forest with field-based objective

Jaehwan Kim; HoWon Kim

doi:10.1117/12.2310059

13 April 2018 2.5d body estimation via refined forest with field-based objective

Jaehwan Kim, HoWon Kim

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106962E (2018) https://doi.org/10.1117/12.2310059
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

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.

Citation Download Citation

Jaehwan Kim and HoWon Kim "2.5d body estimation via refined forest with field-based objective", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962E (13 April 2018); https://doi.org/10.1117/12.2310059

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