Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorSukTing, Pui
dc.contributor.authorMinoi, Jacey-Lynn
dc.contributor.authorLim, Terrin
dc.contributor.authorOliveira, João Fradinho
dc.contributor.authorGillies, Duncan Fyfe
dc.contributor.editorSkala, Václav
dc.date.accessioned2017-11-08T07:31:18Z
dc.date.available2017-11-08T07:31:18Z
dc.date.issued2014
dc.identifier.citationWSCG 2014: communication papers proceedings: 22nd International Conference in Central Europeon Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 179-187.en
dc.identifier.isbn978-80-86943-71-8
dc.identifier.uriwscg.zcu.cz/WSCG2014/!!_2014-WSCG-Communication.pdf
dc.identifier.urihttp://hdl.handle.net/11025/26413
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2014: communication papers proceedingsen
dc.rights@ Václav Skala - UNION Agencycs
dc.subjectextrakce vlastnostícs
dc.subjectlokalizacecs
dc.subjectorientační bodcs
dc.subjectOtsův algoritmuscs
dc.titleFeature extraction and localisation using scale-invariant feature transform on 2.5D imageen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe standard starting point for the extraction of information from human face image data is the detection of key anatomical landmarks, which is a vital initial stage for several applications, such as face recognition, facial analysis and synthesis. Locating facial landmarks in images is an important task in image processing and detecting it automatically still remains challenging. The appearance of facial landmarks may vary tremendously due to facial variations. Detecting and extracting landmarks from raw face data is usually done manually by trained and experienced scientists or clinicians, and the landmarking is a laborious process. Hence, we aim to develop methods to automate as much as possible the process of landmarking facial features. In this paper, we present and discuss our new automatic landmarking method on face data using 2.5-dimensional (2.5D) range images. We applied the Scale-invariant Feature Transform (SIFT) method to extract feature vectors and the Otsu’s method to obtain a general threshold value for landmark localisation. We have also developed an interactive tool to ease the visualisation of the overall landmarking process. The interactive visualization tool has a function which allows users to adjust and explore the threshold values for further analysis, thus enabling one to determine the threshold values for the detection and extraction of important keypoints or/and regions of facial features that are suitable to be used later automatically with new datasets with the same controlled lighting and pose restrictions. We measured the accuracy of the automatic landmarking versus manual landmarking and found the differences to be marginal. This paper describes our own implementation of the SIFT and Otsu’s algorithms, analyzes the results of the landmark detection, and highlights future work.en
dc.subject.translatedfeature extractionen
dc.subject.translatedlocalizationen
dc.subject.translatedlandmarken
dc.subject.translatedOtsu’s algorithmen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2014: Communication Papers Proceedings

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
Sukting.pdfPlný text1,7 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/26413

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Communication Papers
  5. WSCG 2014: Communication Papers Proceedings