Locating facial features with active shape models

Simple item page
dc.contributor.advisorNicolls, Freden_ZA
dc.contributor.authorMilborrow, Stephenen_ZA
dc.date.accessioned2014-07-31T10:54:45Z
dc.date.available2014-07-31T10:54:45Z
dc.date.issued2007en_ZA
dc.descriptionIncludes bibliographical references (p. [94]-98).
dc.description.abstractThis dissertation focuses on the problem of locating features in frontal views of upright human faces. The dissertation starts with the Active Shape Model of Cootes et al. [19] and extends it with the following techniques: 1. Selectively using two-instead of one-dimensional landmark profiles. 2. Stacking two Active Shape Models in series. 3. Extending the set of landmarks. 4. Trimming covariance matrices by setting most entries to zero. 5. Using other modifications such as adding noise to the training set. The resulting feature locater is shown to compare favorably with previously published methods.en_ZA
dc.identifier.apacitationMilborrow, S. (2007). <i>Locating facial features with active shape models</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/5161en_ZA
dc.identifier.chicagocitationMilborrow, Stephen. <i>"Locating facial features with active shape models."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2007. http://hdl.handle.net/11427/5161en_ZA
dc.identifier.citationMilborrow, S. 2007. Locating facial features with active shape models. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Milborrow, Stephen AB - This dissertation focuses on the problem of locating features in frontal views of upright human faces. The dissertation starts with the Active Shape Model of Cootes et al. [19] and extends it with the following techniques: 1. Selectively using two-instead of one-dimensional landmark profiles. 2. Stacking two Active Shape Models in series. 3. Extending the set of landmarks. 4. Trimming covariance matrices by setting most entries to zero. 5. Using other modifications such as adding noise to the training set. The resulting feature locater is shown to compare favorably with previously published methods. DA - 2007 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2007 T1 - Locating facial features with active shape models TI - Locating facial features with active shape models UR - http://hdl.handle.net/11427/5161 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5161
dc.identifier.vancouvercitationMilborrow S. Locating facial features with active shape models. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2007 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5161en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleLocating facial features with active shape modelsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
thesis_ebe_2007_milborrow_s.pdf
Size:
4.77 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters