Multi-stage feature point detection for 3D human data
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Key-component detection on 3D meshes using local features
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
Perceptual evaluation of automatic 2.5d cartoon modelling
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
Key-components: detection of salient regions on 3D meshes
The Visual Computer: International Journal of Computer Graphics
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In this paper, we present a segmentation algorithm which partitions a mesh based on the premise that a 3D object consists of a core body and its constituent protrusible parts. Our approach is based on prominent feature extraction and core approximation and segments the mesh into perceptually meaningful components. Based upon the aforementioned premise, we present a methodology to compute the prominent features of the mesh, to approximate the core of the mesh and finally to trace the partitioning boundaries which will be further refined using a minimum cut algorithm. Although the proposed methodology is aligned with a general framework introduced by Lin et al. (IEEE Trans. Multimedia 9(1):46–57, 2007), new approaches have been introduced for the implementation of distinct stages of the framework leading to improved efficiency and robustness. The evaluation of the proposed algorithm is addressed in a consistent framework wherein a comparison with the state of the art is performed.