title: Exact feature probabilities in images with occlusion.
abstract: 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. We show that the image model is sufficient to explain a wide range of natural scene properties. Finally, we discuss the implications of this description of natural scenes for the study of vision.
authors.affiliation: Center for Theoretical Neuroscience, Columbia University, USA. xaq@neurotheory.columbia.edu
authors.forename: Xaq
authors.initials: X
authors.lastname: Pitkow
mesh_terms: D015350:Contrast Sensitivity / Q000502:physiology; D003867:Depth Perception / Q000502:physiology; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D008962:Models, Theoretical; D010775:Photic Stimulation / Q000379:methods*; D011336:Probability; D014795:Visual Pathways / Q000502:physiology*