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Learning Signs From Subtitles: A Weakly Supervised Approach To Sign Language Recognition

Cooper, H and Bowden, R (2009) Learning Signs From Subtitles: A Weakly Supervised Approach To Sign Language Recognition In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, 2009-06-20 - 2009-06-25, Miami.

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Abstract

This paper introduces a fully-automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Based on head and hand tracking, a novel temporally constrained adaptation of apriori mining is used to extract similar regions of video, with the aid of a proposed contextual negative selection method. These regions are refined in the temporal domain to isolate the occurrences of similar signs in each example. The system is shown to automatically identify and segment signs from standard news broadcasts containing a variety of topics.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Cooper, HUNSPECIFIEDUNSPECIFIED
Bowden, RUNSPECIFIEDUNSPECIFIED
Date : 20 June 2009
Identification Number : https://doi.org/10.1109/CVPRW.2009.5206647
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 28 Mar 2017 14:42
URI: http://epubs.surrey.ac.uk/id/eprint/531463

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