S
Sunita Nayak
Researcher at University of South Florida
Publications - 8
Citations - 128
Sunita Nayak is an academic researcher from University of South Florida. The author has contributed to research in topics: Sign (mathematics) & Sign language. The author has an hindex of 5, co-authored 8 publications receiving 119 citations.
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Proceedings ArticleDOI
Automated extraction of signs from continuous sign language sentences using Iterated Conditional Modes
TL;DR: This work considers a framework where the modeler just provides multiple video sequences of sign language sentences, constructed to contain the vocabulary of interest, and learns the models of the recurring signs, automatically and shows the ability to automatically extract common spoken words in audio.
Journal ArticleDOI
Distribution-Based Dimensionality Reduction Applied to Articulated Motion Recognition
TL;DR: The core theory in this paper concerns embedding the frame-wise distributions into a low-dimensional space so that the authors can estimate various meaningful probabilistic distances such as the Chernoff, Bhattacharya, Matusita, Kullback-Leibler (KL) or symmetric-KL distances based on dot products between points in this space.
Book ChapterDOI
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs
TL;DR: In this paper, a probabilistic framework is presented to automatically learn recurring signs from multiple sign language video sequences containing the vocabulary of interest, which is robust to the variations produced by adjacent signs.
Proceedings ArticleDOI
Unsupervised Modeling of Signs Embedded in Continuous Sentences
TL;DR: A continuous state space model, where the states are based on purely image-based features, without the use of special gloves, is proposed and an unsupervised approach to both extract and learn models for continuous basic units of signs, which are term as signemes, from continuous sentences is presented.
Proceedings ArticleDOI
Segmentation-robust representations, matching, and modeling for sign language
TL;DR: A novel framework is formulated using a nested, level-building based dynamic programming approach that works for matching two instances of signs as well as for matching an instance to an abstracted statistical model in the form of a Hidden Markov Model (HMM).