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Conference Paper: Pixel-level hand detection with shape-aware structured forests
Title | Pixel-level hand detection with shape-aware structured forests |
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Authors | |
Issue Date | 2014 |
Publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ |
Citation | The 12th Asian Conference on Computer Vision, Singapore, 1-5 November 2014. In Lecture Notes in Computer Science, 2014, v. 9006, p. 64-78 How to Cite? |
Abstract | Hand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask for a pixel using structured forests. This approach can better exploit hand shape information in the training data, and enforce shape constraints in the estimation. Aggregation of multiple predictions generated from neighboring pixels further improves the robustness of our method. We evaluate our method on both ego-centric videos and unconstrained still images. Experiment results show that our method can detect hands efficiently and outperform other state-of-the-art methods. |
Description | LNCS v. 9006 entitled: Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV |
Persistent Identifier | http://hdl.handle.net/10722/219220 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhu, X | - |
dc.contributor.author | Jia, X | - |
dc.contributor.author | Wong, KKY | - |
dc.date.accessioned | 2015-09-18T07:18:04Z | - |
dc.date.available | 2015-09-18T07:18:04Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | The 12th Asian Conference on Computer Vision, Singapore, 1-5 November 2014. In Lecture Notes in Computer Science, 2014, v. 9006, p. 64-78 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/219220 | - |
dc.description | LNCS v. 9006 entitled: Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part IV | - |
dc.description.abstract | Hand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask for a pixel using structured forests. This approach can better exploit hand shape information in the training data, and enforce shape constraints in the estimation. Aggregation of multiple predictions generated from neighboring pixels further improves the robustness of our method. We evaluate our method on both ego-centric videos and unconstrained still images. Experiment results show that our method can detect hands efficiently and outperform other state-of-the-art methods. | - |
dc.language | eng | - |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | - |
dc.relation.ispartof | Lecture Notes in Computer Science | - |
dc.rights | The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16817-3_5 | - |
dc.title | Pixel-level hand detection with shape-aware structured forests | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wong, KKY: kykwong@cs.hku.hk | - |
dc.identifier.authority | Wong, KKY=rp01393 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1007/978-3-319-16817-3_5 | - |
dc.identifier.scopus | eid_2-s2.0-84983488968 | - |
dc.identifier.hkuros | 252038 | - |
dc.identifier.volume | 9006 | - |
dc.identifier.spage | 64 | - |
dc.identifier.epage | 78 | - |
dc.identifier.isi | WOS:000362444500005 | - |
dc.publisher.place | Germany | - |
dc.customcontrol.immutable | sml 151216 | - |
dc.identifier.issnl | 0302-9743 | - |