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  Hand Shape Recognition Using a ToF Camera : An Application to Sign Language

Simonovsky, M. (2011). Hand Shape Recognition Using a ToF Camera: An Application to Sign Language. Master Thesis, Universität des Saarlandes, Saarbrücken.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-11B9-2 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-BC0F-5
Genre: Hochschulschrift
Latex : Hand Shape Recognition Using a {ToF} Camera : An Application to Sign Language

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2011_Martin_Simonovsky_Thesis.pdf (beliebiger Volltext), 4MB
 
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 Urheber:
Simonovsky, Martin1, 2, Autor           
Theobalt, Christian2, Ratgeber           
Müller, Meinard3, Gutachter
Affiliations:
1International Max Planck Research School, MPI for Informatics, Max Planck Society, ou_1116551              
2Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
3External Organizations, ou_persistent22              

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 Zusammenfassung: This master's thesis investigates the benefit of utilizing depth information acquired by a time-of-flight (ToF) camera for hand shape recognition from unrestricted viewpoints. Specifically, we assess the hypothesis that classical 3D content descriptors might be inappropriate for ToF depth images due to the 2.5D nature and noisiness of the data and possible expensive computations in 3D space. Instead, we extend 2D descriptors to make use of the additional semantics of depth images. Our system is based on the appearance-based retrieval paradigm, using a synthetic 3D hand model to generate its database. The system is able to run at interactive frame rates. For increased robustness, no color, intensity, or time coherence information is used. A novel, domain-specific algorithm for segmenting the forearm from the upper body based on reprojecting the acquired geometry into the lateral view is introduced. Moreover, three kinds of descriptors exploiting depth data are proposed and the made design choices are experimentally supported. The whole system is then evaluated on an American sign language fingerspelling dataset. However, the retrieval performance still leaves room for improvements. Several insights and possible reasons are discussed.

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Sprache(n): eng - English
 Datum: 2012-03-222011-03-3020112011
 Publikationsstatus: Erschienen
 Seiten: III, 64 p.
 Ort, Verlag, Ausgabe: Saarbrücken : Universität des Saarlandes
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 618894
Anderer: Local-ID: C125675300671F7B-F6F3ECA002CC444FC1257970006B4EF3-Simonovsky2010
 Art des Abschluß: Master

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