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ETDs @PUC-Rio
Estatística
Título: IMPLICIT METHOD FOR CURVE RECONSTRUCTION FROM SPARSE POINTS
Autor: SUENI DE SOUZA AROUCA
Colaborador(es): HELIO CORTES VIEIRA LOPES - Orientador
LUIZ CARLOS PACHECO RODRIGUES VELHO - Coorientador
Catalogação: 25/ABR/2006 Língua(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=8188&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=8188&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.8188
Resumo:
In the field of computer vision and image analysis, implicit curves and surfaces have been recognized as the most useful representation for 2D or 3D objects, mainly because they allow description of shapes by a formula. Most of implicit methods uses algebraic curves to fit globally the frontier of the foreground in a binary image. When the foreground shape is complex, it is common to elevate the curve degree in order to obtain more precision on the approximation. An alternative solution is to decompose the domain hierarchicaly in compact parts and obtain local approximation for the object in each part, and then patch all together in order to obtain a global description of the object. The main objective of this work is to present a new method for implicit curve fitting from sparse point that improves the state of the art
Descrição: Arquivo:   
COVER, ACKNOWLEDGEMENTS, RESUMO, ABSTRACT AND SUMMARY PDF    
CHAPTER 1 PDF    
CHAPTER 2 PDF    
CHAPTER 3 PDF    
CHAPTER 4 PDF    
CHAPTER 5 PDF    
REFERENCES PDF