Abstract
Inspired by biological findings, we present a system that is able to robustly identify a large number of pre-trained objects in real-time. In contrast to related work, we do not restrict the objects’ pose to characteristic views but rotate them freely in hand in front of a cluttered background. We describe the essential system’s ingredients, like prototype-based figure-ground segmentation, extraction of brain-like analytic features, and a simple classifier on top. Finally we analyze the performance of the system using databases of varying difficulty.
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Hasler, S., Wersing, H., Kirstein, S., Körner, E. (2009). Large-Scale Real-Time Object Identification Based on Analytic Features. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_67
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DOI: https://doi.org/10.1007/978-3-642-04277-5_67
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