Dr Helen Cooper
IT Facilities and Project Officer
Email: helen.cooper@surrey.ac.uk
Phone: Work: 01483 68 9851
Room no: 24 AB 05
Further information
Biography
Before University I did a Year In Industry at Cambridge Consultants Ltd. working in the ASICs department. Through this I first got my teeth into programming; learning the basics of Matlab and C. While studying Electronic Engineering with an EU Language at Surrey I returned during my summer holidays to work in Cambridge. My course also involved a years Placement which I completed at Philips Semiconducteurs, Caen, France. I was working on embedded software for mobile phone cameras and it was here that my interest in computer vision began. During my final year I chose a project with Richard Bowden which was an augmented white board, in short a Plasma Screen with 2 cameras that watched for motion and calculated the position of it on the screen. A non-touch touch screen! I graduated with a 1:1 in June 2005.
I started my PhD in October 2005, continuing to work with Richard Bowden, this time in the field of Sign Language Recognition. After submission of my PhD I have continued to work in this field on an EU project called DictaSign
Further details can be found on my personal web page.
Publications
Highlights
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(2011) Sign Language Recognition. in Moeslund TB, Hilton A, Krüger V, Sigal L (eds.) Visual Analysis of Humans: Looking at People
Springer Verlag , pp. 539-562.Full text is available at: http://epubs.surrey.ac.uk/531441/
Abstract
This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets
- . (2010) Sign Language Recognitions: Generalising to More Complex Corpora.. University Of Surrey
- . (2009) 'Learning Signs From Subtitles: A Weakly Supervised Approach To Sign Language Recognition'. Miami, FL, USA : In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 2568-2574.
- . (2007) 'Large lexicon detection of sign language'. SPRINGER-VERLAG BERLIN HUMAN-COMPUTER INTERACTION, PROCEEDINGS, Rio de Janeiro, BRAZIL: IEEE International Workshop on Human - Computer Interaction 4796, pp. 88-97.
Journal articles
- . (2012) 'Sign language recognition using sub-units'. Journal of Machine Learning Research, 13, pp. 2205-2231.
Conference papers
- . (2012) 'Sign Language Recognition using Sequential Pattern Trees'. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2200-2207.
- . (2012) 'Sign Language Recognition using Sequential Pattern Trees'. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, , pp. 2200-2207.
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(2011) 'Reading the Signs: A Video Based Sign Dictionary'. IEEE 2011 International Conference on Computer Vision: 2nd IEEE Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS 2011), Barcelona, Spain: ICCV 2011, pp. 914-919.Full text is available at: http://epubs.surrey.ac.uk/531442/
Abstract
This article presents a dictionary for Sign Language using visual sign recognition based on linguistic subcomponents. We demonstrate a system where the user makes a query, receiving in response a ranked selection of similar results. The approach uses concepts from linguistics to provide sign sub-unit features and classifiers based on motion, sign-location and handshape. These sub-units are combined using Markov Models for sign level recognition. Results are shown for a video dataset of 984 isolated signs performed by a native signer. Recognition rates reach 71.4% for the first candidate and 85.9% for retrieval within the top 10 ranked signs.
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(2011) 'Putting the pieces together: Connected Poselets for human pose estimation'. Proceedings of the IEEE International Conference on Computer Vision, , pp. 1196-1201.Full text is available at: http://epubs.surrey.ac.uk/531437/
Abstract
We propose a novel hybrid approach to static pose estimation called Connected Poselets. This representation combines the best aspects of part-based and example-based estimation. First detecting poselets extracted from the training data; our method then applies a modified Random Decision Forest to identify Poselet activations. By combining keypoint predictions from poselet activitions within a graphical model, we can infer the marginal distribution over each keypoint without any kinematic constraints. Our approach is demonstrated on a new publicly available dataset with promising results. © 2011 IEEE.
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(2011) 'Search-By-Example in Multilingual Sign Language Databases'. Dundee, UK: 2nd International Workshop on Sign Language Translation and Avatar Technology (SLTAT)Full text is available at: http://epubs.surrey.ac.uk/531443/
Abstract
We describe a prototype Search-by-Example or look-up tool for signs, based on a newly developed 1000-concept sign lexicon for four national sign languages (GSL, DGS, LSF,BSL), which includes a spoken language gloss, a HamNoSys description, and a video for each sign. The look-up tool combines an interactive sign recognition system, supported by Kinect technology, with a real-time sign synthesis system,using a virtual human signer, to present results to the user. The user performs a sign to the system and is presented with animations of signs recognised as similar. The user also has the option to view any of these signs performed in the other three sign languages. We describe the supporting technology and architecture for this system, and present some preliminary evaluation results.
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(2010) 'Sign Language Recognition using Linguistically Derived Sub-Units'. Valetta, Malta : European Language Resources Association (ELRA) Proceedings of 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies, Valetta, Malta: IREC 2010, pp. 57-61.Full text is available at: http://epubs.surrey.ac.uk/531457/
Abstract
This work proposes to learn linguistically-derived sub-unit classifiers for sign language. The responses of these classifiers can be combined by Markov models, producing efficient sign-level recognition. Tracking is used to create vectors of hand positions per frame as inputs for sub-unit classifiers learnt using AdaBoost. Grid-like classifiers are built around specific elements of the tracking vector to model the placement of the hands. Comparative classifiers encode the positional relationship between the hands. Finally, binary-pattern classifiers are applied over the tracking vectors of multiple frames to describe the motion of the hands. Results for the sub-unit classifiers in isolation are presented, reaching averages over 90%. Using a simple Markov model to combine the sub-unit classifiers allows sign level classification giving an average of 63%, over a 164 sign lexicon, with no grammatical constraints.
- . (2010) 'Sign Language Recognition using Linguistically Derived Sub-Units'. Malta: 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies, LREC2010, pp. 57-61.
- . (2009) 'Sign Language Recognition: Working with Limited Corpora'. SPRINGER-VERLAG BERLIN UNIVERSAL ACCESS IN HUMAN-COMPUTER INTERACTION: APPLICATIONS AND SERVICES, PT III, San Diego, CA: 5th International Conference on Universal Access in Human-Computer Interaction held at the HCI International 2009 5616, pp. 472-481.
- . (2009) 'Learning Signs From Subtitles: A Weakly Supervised Approach To Sign Language Recognition'. Miami, FL, USA : In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 2568-2574.
- .
(2007) 'Sign Language Recognition Using Boosted Volumetric Features'. MVA Organisation Proceedings of the IAPR Conference on Machine Vision Applications, Tokyo, Japan: IAPR MVA 2007, pp. 359-362.Full text is available at: http://epubs.surrey.ac.uk/531486/
Abstract
This paper proposes a method for sign language recognition that bypasses the need for tracking by classifying the motion directly. The method uses the natural extension of haar like features into the temporal domain, computed efficiently using an integral volume. These volumetric features are assembled into spatio-temporal classifiers using boosting. Results are presented for a fast feature extraction method and 2 different types of boosting. These configurations have been tested on a data set consisting of both seen and unseen signers performing 5 signs producing competitive results.
- . (2007) 'Large lexicon detection of sign language'. SPRINGER-VERLAG BERLIN HUMAN-COMPUTER INTERACTION, PROCEEDINGS, Rio de Janeiro, BRAZIL: IEEE International Workshop on Human - Computer Interaction 4796, pp. 88-97.
Book chapters
- .
(2011) 'Sign Language Recognition'. in Moeslund TB, Hilton A, Krüger V, Sigal L (eds.) Visual Analysis of Humans: Looking at People
Springer Verlag , pp. 539-562.Full text is available at: http://epubs.surrey.ac.uk/531441/
Abstract
This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets
Reports
- . (2011) Give Me a Sign : A Person Independent Interactive Sign Dictionary. Guildford, UK : Article number VSSP-TR-1/2011
Theses and dissertations
- . (2010) Sign Language Recognitions: Generalising to More Complex Corpora.. University Of Surrey
