in the present investigation attempts were made for the first time to use the fundamental color stimulus as the input for a fixed optimized neural network match prediction system. four sets of data having different origins (i.e. different substrate, different colorant sets and different dyeing procedures) were used to train and test the performance of the network. the results showed that the us...

A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the strict maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most prac...

In this paper, we propose a method of tracking a soccer player using multiple cameras. For tracking soccer players, occlusion is always the big problem and tracking is often failed when only a single camera is used to take the scene. Therefore, we use multiple view images for avoiding the occlusion problem, so that we can obtain robustness in player tracking. In our method, inner-camera operati...

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 have been proposed over the years. In general they can be or an over-parameterization (12 parameters) and being generally valid, or use a minimal set of parameters (eight) but do not cover all the rank-2 matrices. We p...

In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied. The accuracy of the estimation imposes a significant influence on subsequent tasks such as the camera trajectory determination and 3D reconstruction. In this paper we propose a new method for fundamental matrix estimation that makes use of clustering a group of 4D vectors. The key ...

Multilinear and tensor decompositions are a popular tool in linear and multilinear algebra and have a wide range of important applications to modern computing. Our paper of 1972 presented the first nontrivial application of such decompositions to fundamental matrix computations and was also a landmark in the history of the acceleration of matrix multiplication. Published in 1972 in Russian, it ...

We compare algorithms for fundamental matrix computation, which we classify into “a posteriori correction”, “internal access”, and “external access”. Doing experimental comparison, we show that the 7-parameter Levenberg-Marquardt (LM) search and the extended FNS (EFNS) exhibit the best performance and that additional bundle adjustment does not increase the accuracy to any noticeable degree.