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We mainly study the low-rank image recovery problem by proposing a bilinear low-rank coding framework called Tensor Low-Rank Representation. For enhanced low-rank recovery and error correction, our method constructs a low-rank tensor subspace to reconstruct given images along row and column directions simultaneously by computing two low-rank matrices alternately from a nuclear norm minimization problem,...
In this paper, we introduce the non-negative matrix factorization (NMF) to decompose the wood images and structure the feature spaces. Local binary pattern (LBP) is used to extract the original spatial local structure features, such as curly edges, etc. and they have better luminance adaptability. Simultaneously, dual-tree complex wavelet transform (DTCWT) is used to extract the energy based statistical...
Non-negative matrix factorization (NMF) is an unsupervised method whose aim is to find an approximate factorization V ?? WH, which decomposes V = [vij] ?? Rn*m into non-negative matrices W = [wij] ?? Rn*r and H = [hij] ?? Rr*m with wij, hij ?? 0. In this paper, we present an extension to the non-negative matrix factorization called DMNMF and adopt the learned distance metric to measure the between-class...
Nonnegative matrix factorization (NMF) is an unsupervised method whose aim is to find an approximate factorization Vn*m = Wn*r*Hr*m into non-negative matrices Wn*r and Hr*m. This paper presents an extension to NMF and discusses the development and the use of damped Newton based the non-negative matrix factorization called DNNMF with good convergence properties for wood image representation by adding...
The values of a board have a direct relationship with the grading determined by the number and distribution of defects. Currently, wood defects recognition research are one of the key interests in the wood industry. In this paper, DWT and NMF are employed and we apply the decomposition to wood feature selection. The feature images by DWT can describe the characteristics and differences of the defects...
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