Statistical inference by stereo vision: geometric information criterion

Abstract

Introducing a mathematical model of noise in stereo images, we define the geometric information criterion (geometric AIC) for evaluating the goodness of an assumption about the object we are viewing. We show that we can test whether or not the object is located infinitely far away or the object is a planar surface without using any knowledge about the noise magnitude or any empirically adjustable thresholds. Synthetic and real-image examples are shown to illustrate our theory.