Segmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering
| Autor: | M. Aboulfatah, M. Bouksim, F. R. Zakani, T. Gadi, K. Arhid |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
3D shapes 02 engineering and technology Artificial neural network classifier lcsh:Information theory 0202 electrical engineering electronic engineering information engineering lcsh:QC350-467 Segmentation Polygon mesh Electrical and Electronic Engineering spectral clustering Artificial neural network business.industry segmentation 020207 software engineering Pattern recognition 3d shapes lcsh:Q350-390 Atomic and Molecular Physics and Optics Spectral clustering Computer Science Applications 020201 artificial intelligence & image processing Artificial intelligence business artificial neural networks lcsh:Optics. Light |
| Zdroj: | Компьютерная оптика, Vol 42, Iss 2, Pp 312-319 (2018) |
| ISSN: | 2412-6179 0134-2452 |
| DOI: | 10.18287/2412-6179-2018-42-2-312-319 |
| Popis: | 3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature. |
| Databáze: | OpenAIRE |
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