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Barbara L. Loeding
Researcher at Florida Polytechnic University
Publications - 20
Citations - 459
Barbara L. Loeding is an academic researcher from Florida Polytechnic University. The author has contributed to research in topics: Sign language & Sign (mathematics). The author has an hindex of 11, co-authored 20 publications receiving 434 citations. Previous affiliations of Barbara L. Loeding include University of South Florida.
Papers
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Journal ArticleDOI
Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming
TL;DR: A framework that can handle movement epenthesis and hand segmentation and grouping in continuous sign language recognition from unaided video sequences is constructed based on an enhanced, nested version of the dynamic programming approach.
Proceedings ArticleDOI
Enhanced Level Building Algorithm for the Movement Epenthesis Problem in Sign Language Recognition
TL;DR: An approach based on version of a dynamic programming framework, called Level Building, to simultaneously segment and match signs to continuous sign language sentences in the presence of movement epenthesis (me).
Book ChapterDOI
Progress in Automated Computer Recognition of Sign Language
TL;DR: This paper reviews the extensive state of the art in automated recognition of continuous signs, from different languages, based on the data sets used, features computed, technique used, and recognition rates achieved.
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.