Abstract
The human visual system is a highly interconnected machinery that acquires its stability through integration of information across modalities and time frames. This integration becomes possible by utilizing regularities in visual data, most importantly motion (especially rigid body motion) and statistical regularities reflected in Gestalt principles such as collinearity.
In this paper we describe an artificial vision system which extracts 3D- information from stereo sequences. This system uses deterministic and statistical regularities to aquire stable representations from unreliable sub-modalities such as stereo or edge detection. To make use of the above mentioned regularities we have to work within a complex machinery containing sub-modules such as stereo, pose estimation and an accumulation scheme. The interaction of these modules allows to use the statistical and deterministic regularities for feature disambiguation within a process of recurrent predictions.
This work has been performed to large extent at the University of Kiel
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Krüger, N., Jäger, T., Perwass, C. (2002). Extraction of Object Representations from Stereo Image Sequences Utilizing Statistical and Deterministic Regularities in Visual Data. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_32
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DOI: https://doi.org/10.1007/3-540-36181-2_32
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