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학술대회 2.5D Body Estimation via Refined Forest with Field-based Objective
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저자
김재환, 김호원
발행일
201711
출처
International Conference on Machine Vision (ICMV) 2017, pp.1-5
DOI
https://dx.doi.org/10.1117/12.2310059
협약과제
17CS1100, 모바일 3D 콘텐츠 서비스를 위한 모바일 실측 3D 모델링 및 실감체험 기술 개발(표준화연계), 김호원
초록
In this paper, we present a 2.5D?닓 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.
키워드
2.5D body estimation, classification, random forest, support vector machine
KSP 제안 키워드
3D data, Classification method, Prediction accuracy, Random forest, Region classification, Spatial relationship, Support VectorMachine(SVM), Visual experiments, training model