Add to ORKGWe investigate the use of the L∞ cost function in geometric vision problems. This cost function measures the maximum of a set of model-fitting errors, rather than the sum-of-squares, or L 2 cost function that is commonly used (in least-squares fitting)
Previous work of the authors developed a theoretically well-founded scheme (FNS) for finding the min...
We study the minimisation of a cost functional which measures the misfit on the boundary of a domain...
How hard are geometric vision problems with outliers? We show that for most fitting problems, a solu...
Recently, there has been interest in formulating various geometric problems in Computer Vision as L ...
This paper presents a new framework for solving geometric structure and motion problems based on L∞-...
Abstract. We introduce a framework for computing statistically optimal estimates of geometric recons...
We summarize techniques for optimal geometric estimation from noisy observations for computer vision...
This paper presents a new method for computing optimal L∞ solutions for vision geometry problems, pa...
A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric m...
Minimizing L∞ error norm for some geometric vision problems provides global optimization using the w...
Geometric fitting is one of the most fundamental problems of computer vision. In [8], the author der...
We give a formal definition of geometric fitting in a way that suits computer vision applications. W...
We consider the least-squares (L2) minimization problems in multiple view geometry for triangulation...
This paper presents a new framework for solving geometric structure and motion problems based on the...
Previous work of the authors developed a theoretically well-founded scheme (FNS) for finding the min...
Previous work of the authors developed a theoretically well-founded scheme (FNS) for finding the min...
We study the minimisation of a cost functional which measures the misfit on the boundary of a domain...
How hard are geometric vision problems with outliers? We show that for most fitting problems, a solu...
Recently, there has been interest in formulating various geometric problems in Computer Vision as L ...
This paper presents a new framework for solving geometric structure and motion problems based on L∞-...
Abstract. We introduce a framework for computing statistically optimal estimates of geometric recons...
We summarize techniques for optimal geometric estimation from noisy observations for computer vision...
This paper presents a new method for computing optimal L∞ solutions for vision geometry problems, pa...
A rigorous accuracy analysis is given to various techniques for estimating parameters of geometric m...
Minimizing L∞ error norm for some geometric vision problems provides global optimization using the w...
Geometric fitting is one of the most fundamental problems of computer vision. In [8], the author der...
We give a formal definition of geometric fitting in a way that suits computer vision applications. W...
We consider the least-squares (L2) minimization problems in multiple view geometry for triangulation...
This paper presents a new framework for solving geometric structure and motion problems based on the...
Previous work of the authors developed a theoretically well-founded scheme (FNS) for finding the min...
Previous work of the authors developed a theoretically well-founded scheme (FNS) for finding the min...
We study the minimisation of a cost functional which measures the misfit on the boundary of a domain...
How hard are geometric vision problems with outliers? We show that for most fitting problems, a solu...