title: Multi-modal estimation of collinearity and parallelism in natural image sequences.
abstract: In this paper we address the statistics of second-order relations of feature vectors derived from image sequences. We compute the individual vector components corresponding to the visual modalities orientation, contrast transition, optic flow, and colour by conventional low-level early vision algorithms. As a main result, we observe that collinear (or parallel) line pairs are, with very great likelihood, also associated with other identical features, for example sharing the same flow pattern, or colour or even sharing multiple feature combinations. It is known that low level processes, such as edge detection, optic flow estimation and stereo are ambiguous. Our results provide support for the assumption that the ambiguity of low level processes can be substantially reduced by integrating information across visual modalities. Furthermore, the attempt to model the application of Gestalt laws in computer vision systems based on statistical measurements, as suggested recently by some researchers (Krüger N 1998 Neural Process. Lett. 8 117-29; Elder H and Goldberg R M 1998 Perception Suppl. 27 11; Sigman M, Cecchi G A, Gilbert C D and Magnasco M O 200.1 Proc. Natl Acad. Sci. USA 98 1935-49; Geisler W S, Perry J S, Super B J and Gallogly D P 2002 Vis. Res. 41 711-24), gets further support and the results in this paper suggest formulation of Gestalt principles in artificial vision systems in a multi-modal way.
authors.affiliation: Department of Psychology, Institute for Computational Intelligence and Technology, University of Stirling, Scotland FK9 4LA, UK. norbert@cn.stir.ac.uk
authors.forename: Norbert
authors.initials: N
authors.lastname: Krüger
authors.forename: Florentin
authors.initials: F
authors.lastname: Wörgötter
mesh_terms: D001185:Artificial Intelligence; D008960:Models, Psychological