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Methods to Automatically Build Point Distribution Models for Objects like Hand Palms and Faces Represented in Images

Maria João M. Vasconcelos1, João Manuel R. S. Tavares1

FEUP-Faculdade de Engenharia da Universidade do Porto / INEGI-Instituto de Engenharia Mecânica e Gestão Industrial, Rua Dr. Roberto Frias S/n, 4200-465 Porto, Portugal, Email:{maria.vasconcelos, tavares}@fe. up. pt.

Computer Modeling in Engineering & Sciences 2008, 36(3), 213-242. https://doi.org/10.3970/cmes.2008.036.213

Abstract

In this work we developed methods to automatically extract significant points of objects like hand palms and faces represented in images that can be used to build Point Distribution Models automatically. These models are further used to segment the modelled objects in new images, through the use of Active Shape Models or Active Appearance Models. These models showed to be efficient in the segmentation of objects, but had as drawback the fact that the labelling of the landmark points was usually manually made and consequently time consuming. Thus, in this paper we describe some methods capable to extract significant points of objects like hand palms and compare the segmentation results in new images.

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APA Style
Vasconcelos, M.J.M., Tavares, J.M.R.S. (2008). Methods to automatically build point distribution models for objects like hand palms and faces represented in images. Computer Modeling in Engineering & Sciences, 36(3), 213-242. https://doi.org/10.3970/cmes.2008.036.213
Vancouver Style
Vasconcelos MJM, Tavares JMRS. Methods to automatically build point distribution models for objects like hand palms and faces represented in images. Comput Model Eng Sci. 2008;36(3):213-242 https://doi.org/10.3970/cmes.2008.036.213
IEEE Style
M.J.M. Vasconcelos and J.M.R.S. Tavares, “Methods to Automatically Build Point Distribution Models for Objects like Hand Palms and Faces Represented in Images,” Comput. Model. Eng. Sci., vol. 36, no. 3, pp. 213-242, 2008. https://doi.org/10.3970/cmes.2008.036.213



cc Copyright © 2008 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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