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In this paper we present a method to recover the shading and specularities in the scene from a single image. The method presented here is based on the dichromatic model and enforces a local smoothness assumption over the object surfaces in the scene. This naturally leads to a setting where the estimate of the shading at a particular pixel can be expressed in terms of its neighbours up to a pair of...
Here, we turn our attention to factor graphs and examine their message passing properties for image processing tasks. To this end, we focus on the maximum a posteriori (MAP) inference process in multi-layered graphs and exploit the ability of factor graphs to capture subtle interactions between image tokens, i.e. pixels, super pixels, features, etc. This leads to a general, yet simple belief propagation...
In this paper, we present a method to recover the albedo and depth from a single image. To this end, we depart from the scattering theory in the atmospheric vision model used elsewhere for defogging and dehazing. We then view the image as a relaxed factorial Markov random field (FMRF) of albedo and depth layers. This leads to a formulation which, for each of the layers in the FMRF, is akin to relaxation...
In this paper we present a method to recover a spectra representation for reproduction and recognition on multispectral imagery. To do this, we commence by viewing the spectra in the image as a mixture which can be expressed in terms of the sample mean and a set of basis vectors and weights. This treatment leads to an MAP approach where the sample means is given by the centers yielded by the application...
In this paper, we present a method for segmenting illuminants in non-uniformly lit scenes. Here, we view the illuminant colour at an image location as a mixture of the segmented illuminants. Based on the dichromatic structure of the image radiance space, we perform soft-clustering on the set of dichromatic planes corresponding to the neighbourhoods of pixel-sites in the image. We solve the soft-clustering...
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