PROJECT TITLE :

Iterative Block Tensor Singular Value Thresholding For Extraction Of Low Rank Component Of Image Data - 2017

ABSTRACT:

Tensor principal component analysis (TPCA) is a multi-linear extension of principal component analysis which converts a collection of correlated measurements into many principal components. During this paper, we tend to propose a new robust TPCA method to extract the principal components of the multi-means knowledge primarily based on tensor singular value decomposition. The tensor is split into a range of blocks of the same size. The low rank component of each block tensor is extracted using iterative tensor singular price thresholding methodology. The principal parts of the multi-method data are the concatenation of all the low rank components of all the block tensors. We have a tendency to give the block tensor incoherence conditions to ensure the successful decomposition. This factorization has similar optimality properties to that of low rank matrix derived from singular value decomposition. Experimentally, we demonstrate its effectiveness in 2 applications, including motion separation for surveillance videos and illumination normalization for face images.