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In this paper, we present a method for fast and robust object r ecognition, especially developed for implementation on an embedd ed platform. As an example, the method is applied to the automatic sorting of co nsumer waste. Out of a stream of different thrown-away food packages, specific items — in this case beverage cartons — can be visually recognised and sorted out . To facilitate and optimise the implementation of this algorithm on an embedde d platform containing parallel hardware, we developed a voting scheme for cons tellations of visual words, i.e. clustered local features (SURF in this case). On top of easy implementation and robust and fast performance, even with large d atabases, an extra advantage is that this method can handle multiple identical v sual features in one model.

TOWARDS EMBEDDED WASTE SORTING - Using Constellations of Visual Words