Meaningful Matches in Stereovision

TL;DR

This paper presents a statistical 'a contrario' method for reliable stereovision matching, using a background model to control false matches and detect occlusions and motion inconsistencies.

Contribution

It introduces a novel statistical framework for stereovision matching that quantifies match reliability and detects occlusions without requiring parameters.

Findings

The method effectively controls false match rates.

It detects occlusions and incoherent motions.

Experimental results validate the approach's reliability.

Abstract

This paper introduces a statistical method to decide whether two blocks in a pair of of images match reliably. The method ensures that the selected block matches are unlikely to have occurred "just by chance." The new approach is based on the definition of a simple but faithful statistical "background model" for image blocks learned from the image itself. A theorem guarantees that under this model not more than a fixed number of wrong matches occurs (on average) for the whole image. This fixed number (the number of false alarms) is the only method parameter. Furthermore, the number of false alarms associated with each match measures its reliability. This "a contrario" block-matching method, however, cannot rule out false matches due to the presence of periodic objects in the images. But it is successfully complemented by a parameterless "self-similarity threshold." Experimental evidence shows that the proposed method also detects occlusions and incoherent motions due to vehicles and pedestrians in non simultaneous stereo.