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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A framework for recognizing the simultaneous aspects of American sign language
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Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
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Efficient Shape Matching Using Shape Contexts
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Detecting Coarticulation in Sign Language using Conditional Random Fields
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling and recognition of the linguistic components in American Sign Language
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A sign language consists of two types of action; signs and fingerspellings. Signs are dynamic gestures discriminated by continuous hand motions and hand configurations, while fingerspellings are a combination of continuous hand configurations. Sign language spotting is the task of detection and recognition of signs and fingerspellings in a signed utterance. The internal structures of signs and fingerspellings differ significantly. Therefore, it is difficult to spot signs and fingerspellings simultaneously. In this paper, a novel method for spotting signs and fingerspellings is proposed. It can distinguish signs, fingerspellings and non-sign patterns, and is robust to the various sizes, scales and rotations of the signer's hand. This is achieved through a hierarchical framework consisting of three steps: (1) Candidate segments of signs and fingerspellings are discriminated using a two-layer conditional random field (CRF). (2) Hand shapes of segmented signs and fingerspellings are verified using BoostMap embeddings. (3) The motions of fingerspellings are verified in order to distinguish those which have similar hand shapes and different hand motions. Experiments demonstrate that the proposed method can spot signs and fingerspellings from utterance data at rates of 83% and 78%, respectively.