Portal de Periódicos da CAPES

Sparse Non-Negative Matrix Factorization for Mesh Segmentation

2016; World Scientific; Volume: 16; Issue: 01 Linguagem: Inglês

10.1142/s0219467816500042

1793-6756

Tim McGraw, Ji-Sun Kang, Donald Herring,

Medical Image Segmentation Techniques

In this paper, we present a method for 3D mesh segmentation based on sparse non-negative matrix factorization (NMF). Image analysis techniques based on NMF have been shown to decompose images into semantically meaningful local features. Since the features and coefficients are represented in terms of non-negative values, the features contribute to the resulting images in an intuitive, additive fashion. Like spectral mesh segmentation, our method relies on the construction of an affinity matrix which depends on the geometric properties of the mesh. We show that segmentation based on the NMF is simpler to implement, and can result in more meaningful segmentation results than spectral mesh segmentation.