Local Surface Shape Estimation of 3-D Textured Surfaces using Gaussian Markov Random Fields and Stereo Windows
Issue No. 10 - October (1993 vol. 15)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.254067
<p>The problem of extracting the local shape information of a 3-D texture surface from a single 2-D image by tracking the perceived systematic deformations the texture undergoes by virtue of being present on a 3-D surface and by virtue of being imaged is examined. The surfaces of interest are planar and developable surfaces. The textured objects are viewed as originating by laying 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). A probability distribution function for the texture data obtained by projecting the planar parent texture under a linear camera model is derived, which 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 that, under appropriate transformations, yields the observed texture in the image. The theory is substantiated by experiments on synthesized as well as real images of textured surfaces.</p>
local surface shape estimation; MLE; planar surfaces; 3-D textured surfaces; Gaussian Markov random fields; stereo windows; single 2-D image; perceived systematic deformations; developable surfaces; stationary Gaussian Markov random field; probability distribution function; camera geometry; surface shape parameter estimation; maximum likelihood estimation problem; Markov processes; maximum likelihood estimation; stereo image processing
M. Patel and F. Cohen, "Local Surface Shape Estimation of 3-D Textured Surfaces using Gaussian Markov Random Fields and Stereo Windows," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 1091-1098, 1993.