All the methods for estimating the fundamental matrix do not naturally exploit the rank-2 constraint. For these reason some few rank-2 parameterizations of the fundamental matrix ...
Establishing the correct correspondence between features in an image set remains a challenging problem amongst computer vision researchers. In fact, the combinatorial nature of fe...
Non-negative matrix factorization (NMF) is an excellent tool for unsupervised parts-based learning, but proves to be ineffective when parts of a whole follow a specific pattern. ...
Abstract--Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the principal component analysis (PCA), indepe...
Abstract. A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the strict maximum likeli...