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In this paper, we present a novel method for constructing a generative model to analyze the structure of labeled data. Given a time-series of sample graphs, we aim to learn a so-called “supergraph” that best describes the underlying average connectivity structure presenting in the data. In this time-series the vertex set is fixed and labeled and the set of possible connections between vertices change...
Traditional machine learning and pattern recognition techniques are intimately linked to the notion of feature spaces. Adopting this view, each object is described in terms of a vector of numerical attributes and is, therefore, mapped to a point in a Euclidean (geometric) vector space, so that the distances between the points reflect the observed (dis)similarities between the respective objects. This...
In this paper a polarisation model which predicts surface reflection as a function of refractive index and angle of incidence is introduced. We present the underlying physics of polarisation which is based on the Fresnel theory and Malus' law. The proposed model can be used to recover the shape of the objects for images taken under polarised light. The traditional way of shape recovery using diffuse...
In this paper we develop a practical method for estimating shape and reflectance using only three polarised images. Using polarised light and retro-reflection settings during image acquisition, we separate the diffuse and specular reflectance components using Blind Source Separation without the accurate knowledge of the polariser angle information. Next, we compare the capacities of five chosen reflectance...
This talk focusses on work aimed at developing a principled probabilistic and information theoretic framework for learning generative models of relational structure. The aim is develop methods that can be used to learn models that can capture the variability present in graph-structures used to represent shapes or arrangements of shape-primitives in images. Here nodes represent the parts of shape-primitives...
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