Fast and Stable Polynomial Equation Solving and Its Application to Computer Vision
This paper presents several new results on techniques for solving systems of polynomial equations in computer vision. Grobner basis techniques for equation solving have been applied successfully to several geometric computer vision problems. However, in many cases these methods are plagued by numerical problems. In this paper we derive a generalization of the Grobner basis method for polynomial equation solving, which improves overall numerical stability. We show how the action matrix can be computed in the general setting of an arbitrary linear basis for a",[x]/I. In particular, two improvements on the stability of the computations are made by studying how the linear basis for a",[x]/I should be selected. The first of these strategies utilizes QR factorization with column pivoting and the second is based on singular value decomposition (SVD). Moreover, it is shown how to improve stability further by an adaptive scheme for truncation of the Grobner basis. These new techniques are studied on some of the latest reported uses of Grobner basis methods in computer vision and we demonstrate dramatically improved numerical stability making it possible to solve a larger class of problems than previously possible.
polynomial equations grobner basis minimal problems structure from motion