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Masaaki Mochimaru

Researcher at National Institute of Advanced Industrial Science and Technology

Publications -  232
Citations -  2495

Masaaki Mochimaru is an academic researcher from National Institute of Advanced Industrial Science and Technology. The author has contributed to research in topics: Active shape model & Motion capture. The author has an hindex of 25, co-authored 229 publications receiving 2229 citations. Previous affiliations of Masaaki Mochimaru include National Tsing Hua University.

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Journal ArticleDOI

Analysis of 3-D human foot forms using the Free Form Deformation method and its application in grading shoe lasts.

TL;DR: The present method with FFD is not only useful for classifying 3-D human body forms, but also has potential as applications for designing well-fitting products.
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Errors in landmarking and the evaluation of the accuracy of traditional and 3D anthropometry.

TL;DR: Quantitative data on the intra- and inter-observer landmarking errors in the present study may be useful as a reference when evaluating and comparing the performance of software for calculating landmark locations for 3D anthropometry.
Proceedings ArticleDOI

Dollhouse VR: a multi-view, multi-user collaborative design workspace with VR technology

TL;DR: This work presents a collaborative design system, Dollhouse, that allows users to discuss the design of the space from two viewpoints simultaneously and supports a set of interaction techniques to facilitate communication between these two user groups.
Journal ArticleDOI

Age-independent and age-dependent sex differences in gait pattern determined by principal component analysis.

TL;DR: The whole waveform of lower-extremity joint kinematics obtained from 191 healthy adults using a principal component analysis (PCA) concluded that the movement related to this PCV is age-independent and is the most dominant sex difference in the gaits observed during normal walking.
Proceedings ArticleDOI

Simultaneous self-calibration of a projector and a camera using structured light

TL;DR: To alleviate the sensitivity issue in estimating and decomposing the radial fundamental matrix, this work proposes an optimization approach that guarantees the possible solution using a prior for the principal points.