A unifying decision-theoretic model-based approach is presented for solving a broad range of vision problems. These include 3-D scene (outdoor and indoor) segmentation of a 2-D image, 3-D surface recognition and shape and position estimation from one or more images, and tracking of a moving camera from a sequence of images of fixed scenes. The image associated with a 3-D surface patch is locally approximated by either a homogeneous Markov random field (MRF) texture model, which is specified by a few parameters having unknown values, by parameterized contour curves having a few unknown parameters, or by other simply parameterized models. The least structured model considered consists of the expectation at each pixel of a single image treated as a completely arbitrary a priori unknown parameter, thus modeling every possible image but requiring a huge number of parameters. 3-D surfaces are modeled as functions described by a priori unknown parameters, ranging from a few to many. To provide a direct link between the image data and the 3-D surface that generates it, the 3-D surface parameters, the camera geometry, the scene lighting, and the image model parameters are related. Because of this linking, 3-D shape recognition, location and orientation estimation, and scene segmentation are possible and can be easily formulated as optimal detection and estimation problems. >
['Fernand S. Cohen', 'David B. Cooper']
A decision theoretic approach for 3-D vision