Research:

Detecting Reduplication in Videos of American Sign Language

Zoya Gavrilov, Stan Sclaroff, Carol Neidle and Sven Dickinson

Abstract: A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL). In ASL, reduplication is used for a variety of linguistic purposes, including overt marking of plurality on nouns, aspectual inflection on verbs, and nominalization of verbal forms. Reduplication involves the repetition, often partial, of the articulation of a sign. In this paper, the apriori algorithm for mining frequent patterns in data streams is adapted for finding reduplication in videos of ASL. The proposed algorithm can account for varying weights on items in the apriori algorithm’s input sequence. In addition, the apriori algorithm is extended to allow for inexact matching of similar hand motion subsequences and to provide robustness to noise. The formulation is evaluated on 105 lexical signs produced by two native signers. To demonstrate the formulation, overall hand motion direction and magnitude are considered; however, the formulation should be amenable to combining these features with others, such as hand shape, orientation, and place of articulation.

Published in: Proc. Eighth International Conf. on Language Resources and Evaluation (LREC), 2012.

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Skeletal Part Learning for Efficient Object Indexing

Ongoing work with Sven Dickinson, Diego Macrini, and Richard Zemel

The goal of this project is to construct an indexing and matching framework operating on the graph encodings of object shapes. A parts-based indexing mechanism has greater robustness to occlusion and part articulation, while the graph-based representation provides angle and size invariance. The idea is pair-wise matching object graphs to extract common recurring subgraphs which then constitute the part vocabulary. Given a novel query object, its graph can be matched to the parts which vote for object hypotheses. Classifiers can additionally be used to learn associations between object categories and object-to-part similarity values.

Funded by the Natural Sciences and Engineering Research Council of Canada via the Undergraduate Summer Research Award - NSERC USRA (2010,2011,2012)