標題: Automatic surface inspection for directional textures using nonnegative matrix factorization
作者: Perng, Der-Baau
Chen, Ssu-Han
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Directional texture;Nonnegative matrix factorization;Defect inspection;Machine vision
公開日期: 1-May-2010
摘要: A global image restoration scheme using nonnegative matrix factorization (NMF) is proposed in this paper. This NMF-based image restoration scheme can be used for inspecting the defects in directional texture surfaces automatically. Decomposing the gray level of image pixels into an ensemble of row vectors, we first reduce the data set from original data space into a lower-dimensional NMF space. The repetitive and periodical primitives are well reconstructed by two lower-dimensional basis and weight matrices with nonnegative elements, named nonnegative matrix approximation (NMA). Then the local defects will be revealed by applying image subtraction between the original image and the NMA. As a consequence, the directional textures are eliminated, and only local defects are preserved if they initially are embedded in the surface. A supervised heuristic, elbow of residual curve rule, is devised which helps users to determine a proper basis space size of a specific image. Experiments on a variety of directional texture surfaces are given to demonstrate the effectiveness and robustness of the proposed method.
URI: http://dx.doi.org/10.1007/s00170-009-2294-2
http://hdl.handle.net/11536/5443
ISSN: 0268-3768
DOI: 10.1007/s00170-009-2294-2
期刊: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume: 48
Issue: 5-8
起始頁: 671
結束頁: 689
Appears in Collections:Articles


Files in This Item:

  1. 000277951800025.pdf