We explore new algorithms for computer vision based on multilinear algebra. Firstly, we learn the expression subspace and person subspace from a corpus of images based on Higher-Order Singular Value Decomposition (HOSVD), and investigate their applications in facial expression synthesis, face recognition and facial expression recognition. Secondly, we explore new algorithms for image ensembles/video representation and recognition using tensor rank-one decomposition and tensor rank-R approximation.
Image Ensembles/ Video analysis Using Image-As-Matrix Representation
The goal of this project is to explore new algorithms based on multilinear algebra for representation of multidimensional data in computer vision.
- H. Wang and N. Ahuja, Rank-R Approximation of Tensors Using Image-as-Matrix Representation, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, San Diego, CA, June 2005, 346-353.
- H. Wang and N. Ahuja, Compact Representation of Multidimensional Data Using Tensor Rank-One Decomposition, IEEE International Conference on Pattern Recognition, Cambridge, UK, August 2004, Vol. 1, 44-47.