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—We formulate edge detection as statistical inference. This statistical edge detection is data driven, unlike standard methods for edge detection which are model based. For any set of edge detection filters (implementing local edge cues), we use presegmented images to learn the probability distributions of filter responses conditioned on whether they are(More)
We treat the problem of edge detection as one of statistical inference. Local edge cues, implemented by filters, provide information about the likely positions of edges which can be used as input to higher-level models. Different edge cues can be evaluated by the statistical effectiveness of their corresponding filters evaluated on a dataset of 100(More)
— We propose a statistical approach to combining edge cues at multiple scales using data driven probability distributions. These distributions are learnt on the Sowerby and South Florida datasets which include the ground truth positions of edges. We evaluate our results using Chernoff information and conditional entropy. Our results demonstrate the(More)
We describe a novel viewpoint-lighting ambiguity which we call the KGBR. This ambiguity assumes orthographic projection or an affine camera, and uses Lambertian re-flectance functions including cast/attached shadows and multiple light sources. A KGBR transform alters the geometry (by a three-dimensional affine transformation) and albedo properties of(More)
We address the visual ambiguities that arise in estimating object and scene structure from a set of images when the viewpoint and lighting are unknown. We obtain a novel viewpoint-lighting ambiguity called the KGBR that corresponds to a group of three-dimensional affine transformations on the object or scene geometry combined with transformations on the(More)
We treat the problem of edge detection as one of statistical inference. Local edge cues, implemented by lters, provide information about the likely positions of edges which can be used as input to higher-level models. Diierent edge cues can be evaluated by the statistical eeectiveness of their corresponding lters evaluated on a dataset of 100 pre-segmented(More)
—We show that generic viewpoint and lighting assumptions resolve standard visual ambiguities by biasing toward planar surfaces. Our model uses orthographic projection with a two-dimensional affine warp and Lambertian reflectance functions, including cast and attached shadows. We use uniform priors on nuisance variables such as viewpoint direction and the(More)
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