E
Edmond Boyer
Researcher at University of Grenoble
Publications - 151
Citations - 8098
Edmond Boyer is an academic researcher from University of Grenoble. The author has contributed to research in topics: Point cloud & Visual hull. The author has an hindex of 41, co-authored 145 publications receiving 7490 citations. Previous affiliations of Edmond Boyer include French Institute for Research in Computer Science and Automation & Microsoft.
Papers
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Journal ArticleDOI
A survey of vision-based methods for action representation, segmentation and recognition
TL;DR: This survey focuses on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and categorizes them according to how they represent the spatial and temporal structure of actions.
Journal ArticleDOI
Free viewpoint action recognition using motion history volumes
TL;DR: Results indicate that this MHV representation can be used to learn and recognize basic human action classes, independently of gender, body size and viewpoint.
Proceedings ArticleDOI
Action Recognition from Arbitrary Views using 3D Exemplars
TL;DR: A new framework is proposed where actions are model actions using three dimensional occupancy grids, built from multiple viewpoints, in an exemplar-based HMM, where a 3D reconstruction is not required during the recognition phase, instead learned 3D exemplars are used to produce 2D image information that is compared to the observations.
Proceedings ArticleDOI
Surface feature detection and description with applications to mesh matching
TL;DR: A 3D feature detector and feature descriptor for uniformly triangulated meshes, invariant to changes in rotation, translation, and scale are proposed and defined generically for any scalar function, e.g., local curvature.
Proceedings ArticleDOI
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
TL;DR: This work proposes a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity, and obtains excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results.