Calibration from Statistical Properties of the Visual World

What does a blind entity need in order to determine the geometry of the set of photocells that it carries through a changing lightfield? In this paper, we show that very crude knowledge of some statistical properties of the environment is sufficient for this task. We show that some dissimilarity measures between pairs of signals produced by photocells are strongly related to the angular separation between the photocells. Based on real-world data, we model this relation quantitatively, using dissimilarity measures based on the correlation and conditional entropy. We show that this model allows to estimate the angular separation from the dissimilarity. Although the resulting estimators are not very accurate, they maintain their performance throughout different visual environments, suggesting that the model encodes a very general property of our visual world. Finally, leveraging this method to estimate angles from signal pairs, we show how distance geometry techniques allow to recover the complete sensor geometry.