The gesture recognition toolkit
The Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and gesture recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting fl...
| Hoofdauteurs: | , |
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| Formaat: | Artikel |
| Taal: | en_US |
| Gepubliceerd in: |
MIT Press
2016
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| Online toegang: | http://hdl.handle.net/1721.1/103640 https://orcid.org/0000-0002-0719-7104 |
| _version_ | 1826206925308559360 |
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| author | Gillian, Nicholas Paradiso, Joseph A. |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Gillian, Nicholas Paradiso, Joseph A. |
| author_sort | Gillian, Nicholas |
| collection | MIT |
| description | The Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and gesture recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting. |
| first_indexed | 2024-09-23T13:40:45Z |
| format | Article |
| id | mit-1721.1/103640 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:40:45Z |
| publishDate | 2016 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/1036402022-09-28T15:28:18Z The gesture recognition toolkit Gillian, Nicholas Paradiso, Joseph A. Massachusetts Institute of Technology. Media Laboratory Gillian, Nicholas Paradiso, Joseph A. The Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and gesture recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting. 2016-07-18T15:20:24Z 2016-07-18T15:20:24Z 2014-10 2014-05 Article http://purl.org/eprint/type/JournalArticle 1533-7928 1532-4435 http://hdl.handle.net/1721.1/103640 Gillian, Nicholas, and Joseph A. Paradiso. "The gesture recognition toolkit." Journal of Machine Learning Research 15 (2014), pp.3483-3487. https://orcid.org/0000-0002-0719-7104 en_US http://jmlr.org/papers/v15/gillian14a.html Journal of Machine Learning Research Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press Journal of Machine Learning Research |
| spellingShingle | Gillian, Nicholas Paradiso, Joseph A. The gesture recognition toolkit |
| title | The gesture recognition toolkit |
| title_full | The gesture recognition toolkit |
| title_fullStr | The gesture recognition toolkit |
| title_full_unstemmed | The gesture recognition toolkit |
| title_short | The gesture recognition toolkit |
| title_sort | gesture recognition toolkit |
| url | http://hdl.handle.net/1721.1/103640 https://orcid.org/0000-0002-0719-7104 |
| work_keys_str_mv | AT gilliannicholas thegesturerecognitiontoolkit AT paradisojosepha thegesturerecognitiontoolkit AT gilliannicholas gesturerecognitiontoolkit AT paradisojosepha gesturerecognitiontoolkit |