Detection of surface orientation and motion from texture by a stereoogical technique.
Artificial Intelligence
Markov random fields for image modelling and analysis
Modelling and applications of stochastic processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biological Cybernetics
Shape From Texture: Integrating Texture-Element Extraction and Surface Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Orientation from Projective Foreshortening of Isotropic Texture Autocorrelation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from texture using the Wigner distribution
Computer Vision, Graphics, and Image Processing
Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Shape from a Shaded and Textural Surface Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape extraction of three-dimensional textured scenes from image data
Shape extraction of three-dimensional textured scenes from image data
Robot Vision
Digital Picture Processing
Shape from texture
The Analysis and Recognition of Real-World Textures in Three Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.