Precision of Computer Vision algorithms
For example, Hartley &amp; Zisserman suggest using preconditioning prior to homography estimation, because taking direct matrix inverse can lead to huge errors or instabilities.
This applies to any numerical method working with matrix inverse.

Feature detection algorithms often use sub-pixel approximation of interest point location.

Most books discussing numerical methods also deal with their stability analysis.

Sometimes you need to do some statistics to analyze precision and accuracy of your estimator (be it a least-squares estimator or maximum likelihood estimator). This is useful in algorithms like RANSAC, which deal with outliers. You would also like to know, how well the estimated transform fit your data and possibly discard results that are too inaccurate.

When working with finite differencing or doing some filtering, a slight Gaussian blurring is done to remove noise, which would otherwise cause huge errors in second derivatives.

Some problems in computer vision are ill-posed. A method of regularization (such a Tikchonov regularization) is necessary in order to solve them. Examples where this is necessary include computing anisotropic diffusion.