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Learning Boundary Edges for 3D‐Mesh Segmentation
Author(s) -
Benhabiles Halim,
Lavoué Guillaume,
Vandeborre JeanPhilippe,
Daoudi Mohamed
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01967.x
Subject(s) - computer science , polygon mesh , segmentation , boundary (topology) , artificial intelligence , pipeline (software) , classifier (uml) , image segmentation , enhanced data rates for gsm evolution , computer vision , pattern recognition (psychology) , algorithm , computer graphics (images) , mathematics , programming language , mathematical analysis
This paper presents a 3D‐mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D‐meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state‐of‐the‐art.