Objects extraction and recognition for camera-based interaction : heuristic and statistical approaches

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Wang, Hao
dc.date.accessioned 2012-02-24T08:55:10Z
dc.date.available 2012-02-24T08:55:10Z
dc.date.issued 2007-12-14
dc.identifier.isbn 978-951-22-9134-2
dc.identifier.issn 1457-1404
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2973
dc.description.abstract In this thesis, heuristic and probabilistic methods are applied to a number of problems for camera-based interactions. The goal is to provide solutions for a vision based system that is able to extract and analyze interested objects in camera images and to use that information for various interactions for mobile usage. New methods and new attempts of combination of existing methods are developed for different applications, including text extraction from complex scene images, bar code reading performed by camera phones, and face/facial feature detection and facial expression manipulation. The application-driven problems of camera-based interaction can not be modeled by a uniform and straightforward model that has very strong simplifications of reality. The solutions we learned to be efficient were to apply heuristic but easy of implementation approaches at first to reduce the complexity of the problems and search for possible means, then use developed statistical learning approaches to deal with the remaining difficult but well-defined problems and get much better accuracy. The process can be evolved in some or all of the stages, and the combination of the approaches is problem-dependent. Contribution of this thesis resides in two aspects: firstly, new features and approaches are proposed either as heuristics or statistical means for concrete applications; secondly engineering design combining seveal methods for system optimization is studied. Geometrical characteristics and the alignment of text, texture features of bar codes, and structures of faces can all be extracted as heuristics for object extraction and further recognition. The boosting algorithm is one of the proper choices to perform probabilistic learning and to achieve desired accuracy. New feature selection techniques are proposed for constructing the weak learner and applying the boosting output in concrete applications. Subspace methods such as manifold learning algorithms are introduced and tailored for facial expression analysis and synthesis. A modified generalized learning vector quantization method is proposed to deal with the blurring of bar code images. Efficient implementations that combine the approaches in a rational joint point are presented and the results are illustrated. en
dc.format.extent 68, [62]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Helsinki University of Technology Laboratory of Computational Engineering publications. Report B en
dc.relation.ispartofseries 68 en
dc.relation.haspart Hao Wang, Jari Kangas, Text location in color scene images for information acquisition by mobile terminals, Proceedings of the 5th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2001), Vol. 6, pp. 436-441, Orlando, Florida, USA, 2001, IIIS. [article1.pdf] © 2001 International Institute of Informatics and Systemics (IIIS). By permission.
dc.relation.haspart Hao Wang, Jari Kangas, Character-like region verification for extracting text in scene images, Proceedings of the 6th International Conference on Document Analysis and Recognition (ICDAR 2001), pp. 957-962, Seattle, WA, USA, 2001, IEEE.
dc.relation.haspart Kongqiao Wang, Yanming Zou, Hao Wang, 1D bar code reading on camera phones, International Journal of Image and Graphics, vol. 7, no. 3, pp. 529-550, 2007, World Scientific Publishing, ISSN 0219-4678. [article3.pdf] © 2007 World Scientific Publishing Company. By permission.
dc.relation.haspart Hao Wang, Yanming Zou, 2D bar codes reading: solutions for camera phones, International Journal of Signal Processing, Vol. 3, No. 3, pp. 164-170, 2006, World Academy of Science, Engineering and Technology, ISSN 1304-4478. [article4.pdf] © 2006 World Academy of Science, Engineering and Technology (WASET). By permission.
dc.relation.haspart Hao Wang, Kongqiao Wang, Facial feature extraction and image-based face drawing, Proceedings of the 6th International Conference on Signal Processing (ICSP 2002), Vol. 1, pp. 699-702, Beijing, China, 2002, IEEE.
dc.relation.haspart Hao Wang, Image-based face drawing using active shape models and parametric morphing, Proceedings of the 2003 IEEE International Conference on Neural Networks and Signal Processing (ICNNSP 2003), Vol. 2, pp. 1017-1020, Nanjing, China, 2003, IEEE.
dc.relation.haspart Hao Wang, Kongqiao Wang, Affective interaction based on person-independent facial expression space, Neurocomputing, Special Issue for Vision Research, Vol. 71, No. 10-12, pp. 1889-1901, 2008, Elsevier, ISSN 0925-2312. [article7.pdf] © 2008 by authors and © 2008 Elsevier Science. By permission.
dc.subject.other Electrical engineering en
dc.title Objects extraction and recognition for camera-based interaction : heuristic and statistical approaches en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword camera-based interaction en
dc.subject.keyword text extraction en
dc.subject.keyword bar code en
dc.subject.keyword facial expression en
dc.subject.keyword boosting en
dc.subject.keyword manifold learning en
dc.identifier.urn urn:nbn:fi:tkk-011012
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.contributor.lab Laboratory of Computational Engineering en
dc.contributor.lab Laskennallisen tekniikan laboratorio fi

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