title: Non parametric, self organizing, scalable modeling of spatiotemporal inputs: the sign language paradigm.
abstract: Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed for solving problems that do not specifically address the temporal aspect. The proposed approach deals with temporal and spatial aspects of the spatiotemporal domain in a discriminative as well as coupling manner. Self Organizing Maps (SOM) model the spatial aspect of the problem and Markov models its temporal counterpart. Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability. The proposed scheme is validated both theoretically, through an error propagation study, and experimentally, on the recognition of individual signs, performed by different, native Greek Sign Language users. Results illustrate the architecture's superiority when compared to Hidden Markov Model techniques and variations both in terms of classification performance and computational cost.
authors.affiliation: Intelligent Systems, Content and Interaction Lab, National Technical University of Athens, Greece. gcari@image.ntua.gr
authors.forename: G
authors.initials: G
authors.lastname: Caridakis
authors.forename: K
authors.initials: K
authors.lastname: Karpouzis
authors.forename: A
authors.initials: A
authors.lastname: Drosopoulos
authors.forename: S
authors.initials: S
authors.lastname: Kollias
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005583:Fourier Analysis; D007839:Functional Laterality; D005868:Gestures; D006225:Hand; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual; D012813:Sign Language; D018709:Statistics, Nonparametric