2010/03/15 by Xaq Pitkow, Pitkow, Xaq · 1 citation
Computer Science · Neuroscience · Physics and Astronomy · #Advanced Vision and Imaging #Color Science and Applications #Data Analysis #FOS: Biological sciences #FOS: Physical sciences #Neurons and Cognition (q-bio.NC) #Statistics and Probability (physics.data-an) #Visual perception and processing mechanisms
paper · pdf · doi:10.48550/arxiv.1003.2950
openalex publication_date 2010/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To understand the computations of our visual system, it is important to understand also the natural environment it evolved to interpret. Unfortunately, existing models of the visual environment are either unrealistic or too complex for mathematical description. Here we describe a naturalistic image model and present a mathematical solution for the statistical relationships between the image features and model variables. The world described by this model is composed of independent, opaque, textured objects which occlude each other. This simple structure allows us to calculate the joint probability distribution of image values sampled at multiple arbitrarily located points, without approximation. This result can be converted into probabilistic relationships between observable image features as well as between the unobservable properties that caused these features, including object boundaries and relative depth. Using these results we explain the causes of a wide range of natural scene properties, including highly non-gaussian distributions of image features and causal relations between pairs of edges. We discuss the implications of this description of natural scenes for the study of vision.