Abstract
In this paper, a new, glove-free method for recognition of fingerspelled acronyms using hierarchical temporal memory has been proposed. The task is challenging because many signs look similar from the camera viewpoint. Moreover handshapes are distorted strongly as a result of coarticulation and motion blur, especially in the fluent fingerspelling. In the described work, the problem has been tackled by applying the new, bio-inspired recognition engine, based on structural and functional properties of mammalian neocortex, robust to local changes shape descriptors, and a training scheme allowing for capture possible handshape deformations in a manner that is lexicon independent.
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Kapuscinski, T. (2012). Vision-Based Recognition of Fingerspelled Acronyms Using Hierarchical Temporal Memory. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_61
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DOI: https://doi.org/10.1007/978-3-642-29347-4_61
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