Lattice Models for Context-Driven Regularization in Motion Perception

Real-world motion field patterns contain intrinsic statistic properties that allow to define Gestalts as groups of pixels sharing the same motion property. By checking the presence of such Gestalts in optic flow fields we can make their interpretation more confident. We propose a context-sensitive recurrent filter capable of evidencing motion Gestalts corresponding to 1st-order elementary flow components (EFCs). A Gestalt emerges from a noisy flow as a solution of an iterative process of spatially interacting nodes that correlates the properties of the visual context with that of a structural model of the Gestalt. By proper specification of the interconnection scheme, the approach can be straightforwardly extended to model any type of multimodal spatio-temporal relationships (i.e., multimodal spatiotemporal context).

Authors and Affiliations

1. Department of Biophysical and Electronic Engineering, University of Genova, Via Opera Pia 11/a, 16145, Genova, Italy

   Silvio P. Sabatini, Fabio Solari & Giacomo M. Bisio

Editors and Affiliations

1. Dipartimentimento di Scienze dell’Informazione, via Comelico 39/41, 20135, Milano, Italy

   Bruno Apolloni

2. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, (SA), Italy

   Maria Marinaro

3. Department of Mathematics and Informatics, University of Salerno, Via Ponte Don Melillo, 84084, Fisciano, (SA), Italy

   Roberto Tagliaferri

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