IEEE Trans Image Process - Stochastic uncertainty models for the luminance consistency assumption.

Tópicos

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Resumo

In this paper, a stochastic formulation of the brightness consistency used in many computer vision problems involving dynamic scenes (for instance, motion estimation or point tracking) is proposed. Usually, this model, which assumes that the luminance of a point is constant along its trajectory, is expressed in a differential form through the total derivative of the luminance function. This differential equation linearly links the point velocity to the spatial and temporal gradients of the luminance function. However, when dealing with images, the available information only holds at discrete time and on a discrete grid. In this paper, we formalize the image luminance as a continuous function transported by a flow known only up to some uncertainties related to such a discretization process. Relying on stochastic calculus, we define a formulation of the luminance function preservation in which these uncertainties are taken into account. From such a framework, it can be shown that the usual deterministic optical flow constraint equation corresponds to our stochastic evolution under some strong constraints. These constraints can be relaxed by imposing a weaker temporal assumption on the luminance function and also in introducing anisotropic intensity-based uncertainties. We also show that these uncertainties can be computed at each point of the image grid from the image data and hence provide meaningful information on the reliability of the motion estimates. To demonstrate the benefit of such a stochastic formulation of the brightness consistency assumption, we have considered a local least-squares motion estimator relying on this new constraint. This new motion estimator significantly improves the quality of the results.

Resumo Limpo

paper stochast formul bright consist use mani comput vision problem involv dynam scene instanc motion estim point track propos usual model assum lumin point constant along trajectori express differenti form total deriv lumin function differenti equat linear link point veloc spatial tempor gradient lumin function howev deal imag avail inform hold discret time discret grid paper formal imag lumin continu function transport flow known uncertainti relat discret process reli stochast calculus defin formul lumin function preserv uncertainti taken account framework can shown usual determinist optic flow constraint equat correspond stochast evolut strong constraint constraint can relax impos weaker tempor assumpt lumin function also introduc anisotrop intensitybas uncertainti also show uncertainti can comput point imag grid imag data henc provid meaning inform reliabl motion estim demonstr benefit stochast formul bright consist assumpt consid local leastsquar motion estim reli new constraint new motion estim signific improv qualiti result

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