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1 September 1991 Probabilistic modeling of surfaces
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Energy-based surface models are commonly used in computer vision to interpolate sparse data, to smooth noisy depth estimates, and to integrate measurements from multiple sensors and viewpoints. Traditionally, a single surface estimate is produced with such models. Probabilistic surface modeling, which describes distributions over possible surfaces, enables us to integrate such measurements in a statistically optimal fashion, to model the uncertainty in the surfaces, and to develop sequential estimation algorithms. When applied to 2-1/2-D surfaces, probabilistic modeling allows us to incrementally estimate depth maps from motion image sequences and to integrate sparse range data using elevation maps. How to jointly model depth and intensity images to obtain more accurate models of depth, is shown. To better represent the structure of the visual world, full 3-D surface models must be used. These are usually represented using parametric surfaces, which can create difficulties when the surface topology is unknown. To overcome these problems, an incremental patch-based 3-D surface estimation algorithm is developed. Surface and feature-based methods are compared and a unified representation which encompasses both methods is proposed.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard Szeliski "Probabilistic modeling of surfaces", Proc. SPIE 1570, Geometric Methods in Computer Vision, (1 September 1991);


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