IEEE Trans Image Process - A Bayesian hierarchical factorization model for vector fields.

Tópicos

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Resumo

Factorization-based techniques explain arrays of observations using a relatively small number of factors and provide an essential arsenal for multi-dimensional data analysis. Most factorization models are, however, developed on general arrays of scalar values. For a class of practical data arising from observing spatial signals including images, it is desirable for a model to consider general observations, e.g., handling a vector field and non-exchangeable factors, e.g., handling spatial connections between the columns and the rows of the data. In this paper, a probabilistic model for factorization is proposed. We adopt Bayesian hierarchical modeling and treat the factors as latent random variables. A Markov structure is imposed on the distribution of factors to account for the spatial connections. The model is designed to represent vector arrays sampled from fields of continuous domains. Therefore, a tailored observation model is developed to represent the link between the factor product and the data. The proposed technique has been shown effective in analyzing optical flow fields computed on both synthetic images and real-life videoclips.

Resumo Limpo

factorizationbas techniqu explain array observ use relat small number factor provid essenti arsenal multidimension data analysi factor model howev develop general array scalar valu class practic data aris observ spatial signal includ imag desir model consid general observ eg handl vector field nonexchang factor eg handl spatial connect column row data paper probabilist model factor propos adopt bayesian hierarch model treat factor latent random variabl markov structur impos distribut factor account spatial connect model design repres vector array sampl field continu domain therefor tailor observ model develop repres link factor product data propos techniqu shown effect analyz optic flow field comput synthet imag reallif videoclip

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