Estimating Vision Parameters Given Data with Covariances

A new parameter estimation method is presented, applicable to many computer vision problems. It operates under the assumption that the data (typically image point locations) are accompanied by covariance matrices characterising data uncertainty. An MLE-based cost function is first formulated and a new minimisation scheme is then developed. Unlike Sampson’s method or the renormalisation technique of Kanatani, the new scheme has as its theoretical limit the true minimum of the cost function. It also has the advantages of being simply expressed, efficient, and unsurpassed in our comparative testing.

Estimating Vision Parameters Given Data with Covariances

A new parameter estimation method is presented, applicable to many computer vision problems. It operates under the assumption that the data (typically image point locations) are accompanied by covariance matrices characterising data uncertainty. An MLE-based cost function is first formulated and a new minimisation scheme is then developed. Unlike Sampson’s method or the renormalisation technique of Kanatani, the new scheme has as its theoretical limit the true minimum of the cost function. It also has the advantages of being simply expressed, efficient, and unsurpassed in our comparative testing.