In this paper, we propose a novel method, called local nonnegative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual pat...
Stan Z. Li, XinWen Hou, HongJiang Zhang, QianSheng...
This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual da...
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization...
Correlation is a very effective way to align intensity images. We extend the correlation technique to point set registration using a method we call kernel correlation. Kernel corre...
Abstract. In order to reconstruct 3-D Euclidean shape by the TomasiKanade factorization, one needs to specify an affine camera model such as orthographic, weak perspective, and par...
Ken-ichi Kanatani, Yasuyuki Sugaya, Hanno Ackerman...