On computational Gestalt detection thresholds

Abstract

The aim of this paper is to show some recent developments of computational Gestalt theory, as pioneered by Desolneux, Moisan and Morel. The new results allow to predict much more accurately the detection thresholds. This step is unavoidable if one wants to analyze visual detection thresholds in the light of computational Gestalt theory. The paper first recalls the main elements of computational Gestalt theory. It points out a precision issue in this theory, essentially due to the use of discrete probability distributions. It then proposes to overcome this issue by using continuous probability distributions and illustrates it on the meaningful alignment detector of Desolneux et al.