29 December 2000 Markovian motion field regularization based on the Gauss-Laguerre transform
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In this paper we address the classical problem of estimating, from a pair of consecutive frames of a video sequence, the motion (or velocity field, or optical flow) produced by planar translations and rotations, in the context of the Gauss- Laguerre Transform (GLT) theory. This contribution extends some previous works of the authors on wavelet based Optimum Scale-Orientation Independent Pattern Recognition. In particular here we make use of an orthogonal system of Laguerre-Gauss wavelets. Each wavelet represents the image by translated, dilated and rotated versions of a complex waveform whereas, for a fixed resolution, this expansion provides a local representation of the image around any point. In addition each waveform is self-steerable, i.e. it rotates by simple multiplication with a complex factor. These properties allow to derive an iterative joint translation and rotation field Maximum Likelihood (ML) estimation procedure based on a bank of CHWs. In this contribution the coarse estimate obtained by the memoryless, point wise, ML estimator is refined by resorting to a compound Markovian model that takes into account the spatial continuity of the motion field associated to a single object, by heavily penalizing abrupt changes in the motion intensity and direction not located in correspondence of intensity discontinuities (i.e. mostly object boundaries).
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Marco Carli, Marco Carli, Giovanni Jacovitti, Giovanni Jacovitti, Alessandro Neri, Alessandro Neri, } "Markovian motion field regularization based on the Gauss-Laguerre transform", Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); doi: 10.1117/12.411814; https://doi.org/10.1117/12.411814

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