Visuelles Erkennen von Objekten mit Ausprägungsvarianzen

Computational visual object recognition has a big potential of application not only in the domain of automation technology, where visual object recognition is already established. A lot of other applications are imaginable, if more from the biological visual capabilities could be implemented. A special challenge consists in the recognition of real world objects having changing appearance. A universal object recognition system has to cope with that. However until now, it is unclear, which principles of functionality are used by biological object recognition. Scientific findings about that are still incomplete and allow no direct reproduction. Based on these findings, the only way to proceed is to assume principles of functionality and validate them in computational object recognition systems. In this contribution an explicit representation of changing appearance is investigated. For this purpose an object recognition system is built, which realises some new processing properties. The analysis of visual context is not realized with rigid filter masks as usual. Here context analysis is done by a new developed diffusion technique. The technique needs no pre-processing. A layer-wise representation of part objects with increasing complexity is built. For that no reduction of resolution is needed on the layers. A detection of a part object can depend on an arbitrary set of representations of underlying layers and not only from the one direct underlying. The evaluation of the object recognition system shows, that the explicit coding of changing appearance can be applied successfully