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We present a novel method to model and estimate elastic geometric deformations of an observed object, whether they are caused by the object's own dynamic behavior, or by the dynamic behavior of the imaging device, or both. A procedure for estimating the space of possible deformations the object may undergo based only on a set of observations is derived. This information is then employed to derive...
Image matting is the process of extracting a foreground element from a single image with limited user input. To solve the inherently ill-posed problem, there exist various methods which use specific color model. One representative method assumes that the colors of the foreground and background elements satisfy the linear color model. The other recent method considers line-point color model and point-point...
We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined...
Natural image matting is an extremely challenging image processing problem due to its ill-posed nature. It often requires skilled user interaction to aid definition of foreground and background regions. Current algorithms use these predefined regions to build local foreground and background colour models. In this paper we propose a novel approach which uses non-parametric statistics to model image...
We propose a novel background and foreground estimation algorithm in MAP-MRF approach for binarization of degraded document image. In the proposed algorithm, an assumption that background whiteness and foreground blackness is not employed differently from the conventional algorithm, and we employ characters' irregularities based on local statistics. This makes the method possible to apply to the image...
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