( en | de | ru )

 

Disambiguation of "Namioka, Yasuo"

Author of the publication

copydeleteadd this publication to your clipboard
Identification from ceiling: unconstrained person identification for tabletops using multiview learning.

, , and . MUM, page 273-284. ACM, (2016)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Yasuo Kamata

Yasuo Tokunaga

Yasuo Nakayama

 

Other publications of authors with the same name

Robust Unsupervised Factory Activity Recognition with Body-worn Accelerometer Using Temporal Structure of Multiple Sensor Data Motifs., , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4 (3): 97:1-97:30 (2020)Acceleration-based Human Activity Recognition of Packaging Tasks Using Motif-guided Attention Networks., , , , , and . PerCom, page 1-12. IEEE, (2022)Preliminary Evaluation of a Framework for Overhead Skeleton Tracking in Factory Environments using Kinect., , , and . iWOAR, page 12:1-12:6. ACM, (2017)Automatic Programming for Sequence Control., , , , and . IAAI, page 315-331. AAAI, (1992)Identification from ceiling: unconstrained person identification for tabletops using multiview learning., , and . MUM, page 273-284. ACM, (2016)Visual Environments for Designing Sequence Control Program with AI Planning., and . VL, page 434-441. IEEE Computer Society, (1997)Toward practical factory activity recognition: unsupervised understanding of repetitive assembly work in a factory., , , and . UbiComp, page 1088-1099. ACM, (2016)Knowledge Compilation for Interactive Design of Sequence Control Programs., and . IEA/AIE, page 363-368. Gordon and Breach Science Publishers, (1996)Towards Overhead Semantic Part Segmentation of Workers in Factory Environments from Depth Images using a FCN., , , , , , and . CBS, page 204-209. IEEE, (2019)Comparative Analysis of High- and Low-Performing Factory Workers with Attention-Based Neural Networks., , , , and . MobiQuitous, volume 419 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 469-480. Springer, (2021)