Shape from texture using Markov random field models and stereo-windows

Abstract

The problem of extracting the local shape information of a 3D textured surface from a single 2D image is addressed. The textured objects of interest are planar and developable surfaces that are viewed as originating by laying down a rubber planar sheet with a homogeneous parent texture on it onto the objects. The homogeneous planar parent texture is modeled by a stationary Gaussian Markov random field (GMRF). The probability density function of the projected planar parent texture is an explicit function of the parent GMRF parameters, the surface shape parameters, and the camera geometry. The surface shape parameter estimation is posed as a maximum-likelihood estimation problem. A stereo-windows concept is introduced to obtain a unique and consistent parent texture from the image data.<>