Spatial verification

Spatial verification is a technique in which similar locations can be identified in an automated way through a sequence of images.

The main problem is that outliers (that does not fit or does not match the selected model) affect adjustment called least squares (numerical analysis technique framed in mathematical optimization, which, given an set of ordered pairs: independent variable, dependent variable, and a family of functions, try to find the continuous function).

The most widely used for spatial verification and avoid errors caused by these outliers methods are: Seeks to avoid the impact of outliers, that not fit with the model, so only considers inline which match the model in question.

If an outlier is chosen to calculate the current setting, then the resulting line will have little support from the rest of the points.

This is a technique for detecting shapes in digital images that solves the veracity of space by clusters of points belonging to the model through a voting procedure on a set of parametric figures.