אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Fitting an algebraic equation to observed data is one of the first steps of many computer vision applications. For example, we fit lines and curves to points in 2D and planes and surfaces in 3D. Computing computing the fundamental matrix or the homography matrix can also be viewed as fitting in a high-dimensional space.

A naive way for this is the least squares, also known as ``algebraic distance minimization'', minimizing the sum of squares of terms that should be zero in the absence of noise, and the solution is obtained by algebraic operations without iterations. However, this is known to be of low accuracy, and known high-accuracy methods are based on maximum likelihood (ML).

The problem of all ML-based methods, including bundle adjustment, FNS, and HEIV, is that they require iterations, which may not converge in the presence of large noise. Also, an appropriate initial guess is necessary to start the iterations. Thus, accurate algebraic procedure that yields high accuracy solution, even though it is not exactly optimal, is very much desired.

In this talk, we propose a new approach to improve the algebraic fitting based on least squares to the utmost accuracy, which we call ''hyperaccuracy''. We analyze the error of a general parameterized algebraic fitting scheme up to high order noise terms and adjust the parameters so that the resulting solution has the highest accuracy. We apply our analysis to ellipse fitting and homography computation. By simulation, we demonstrate that our approach indeed produces an accurate solution even in highly noisy situations where ML-based iterations fail to converge.