Techniques for keypoint detection and matching between endoscopic images
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/11318 |
Resumo: | The detection and description of local image features is fundamental for different computer vision applications, such as object recognition, image content retrieval, and structure from motion. In the last few years the topic deserved the attention of different authors, with several methods and techniques being currently available in the literature. The SIFT algorithm, proposed in [2], gained particular prominence because of its simplicity and invariance to common image transformations like scaling and rotation. Unfortunately the approach is not able to cope with non-linear image deformations caused by radial lens distortion. The invariance to radial distortion is highly relevant for applications that either require a wide field of view (e.g. panoramic vision), or employ cameras with specific optical arrangements enabling the visualization of small spaces and cavities (e.g. medical endoscopy). One of the objectives of this thesis is to understand how radial distortion impacts the detection and description of keypoints using the SIFT algorithm. We perform a set of experiments that clearly show that distortion affects both the repeatability of detection and the invariance of the SIFT description. These results are analyzed in detail and explained from a theoretical viewpoint. In addition, we propose a novel approach for detection and description of stable local features in images with radial distortion. The detection is carried in a scale-space image representation built using an adaptive gaussian filter that takes into account distortion, and the feature description is performed after implicit gradient correction using the derivative chain rule. Our approach only requires a rough modeling of the radial distortion function and, for moderate levels of distortion, it outperforms the application of the SIFT algorithm after explicit image correction. |
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Techniques for keypoint detection and matching between endoscopic imagesAlgoritmo SIFTEngenharia biomédicaProjecto ArthroNAV - processamento de imagem endoscópicaSIFT - distorção radialSIFT - métodoSIFT - scale invariant features transform - algoritmoThe detection and description of local image features is fundamental for different computer vision applications, such as object recognition, image content retrieval, and structure from motion. In the last few years the topic deserved the attention of different authors, with several methods and techniques being currently available in the literature. The SIFT algorithm, proposed in [2], gained particular prominence because of its simplicity and invariance to common image transformations like scaling and rotation. Unfortunately the approach is not able to cope with non-linear image deformations caused by radial lens distortion. The invariance to radial distortion is highly relevant for applications that either require a wide field of view (e.g. panoramic vision), or employ cameras with specific optical arrangements enabling the visualization of small spaces and cavities (e.g. medical endoscopy). One of the objectives of this thesis is to understand how radial distortion impacts the detection and description of keypoints using the SIFT algorithm. We perform a set of experiments that clearly show that distortion affects both the repeatability of detection and the invariance of the SIFT description. These results are analyzed in detail and explained from a theoretical viewpoint. In addition, we propose a novel approach for detection and description of stable local features in images with radial distortion. The detection is carried in a scale-space image representation built using an adaptive gaussian filter that takes into account distortion, and the feature description is performed after implicit gradient correction using the derivative chain rule. Our approach only requires a rough modeling of the radial distortion function and, for moderate levels of distortion, it outperforms the application of the SIFT algorithm after explicit image correction.2009-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10316/11318http://hdl.handle.net/10316/11318engLourenço, António Miguel - Techniques for keypoint detection and matching between endoscopic images. Coimbra, 2009Lourenço, António Miguelinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-01-20T17:49:17Zoai:estudogeral.uc.pt:10316/11318Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:00:17.458831Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Techniques for keypoint detection and matching between endoscopic images |
title |
Techniques for keypoint detection and matching between endoscopic images |
spellingShingle |
Techniques for keypoint detection and matching between endoscopic images Lourenço, António Miguel Algoritmo SIFT Engenharia biomédica Projecto ArthroNAV - processamento de imagem endoscópica SIFT - distorção radial SIFT - método SIFT - scale invariant features transform - algoritmo |
title_short |
Techniques for keypoint detection and matching between endoscopic images |
title_full |
Techniques for keypoint detection and matching between endoscopic images |
title_fullStr |
Techniques for keypoint detection and matching between endoscopic images |
title_full_unstemmed |
Techniques for keypoint detection and matching between endoscopic images |
title_sort |
Techniques for keypoint detection and matching between endoscopic images |
author |
Lourenço, António Miguel |
author_facet |
Lourenço, António Miguel |
author_role |
author |
dc.contributor.author.fl_str_mv |
Lourenço, António Miguel |
dc.subject.por.fl_str_mv |
Algoritmo SIFT Engenharia biomédica Projecto ArthroNAV - processamento de imagem endoscópica SIFT - distorção radial SIFT - método SIFT - scale invariant features transform - algoritmo |
topic |
Algoritmo SIFT Engenharia biomédica Projecto ArthroNAV - processamento de imagem endoscópica SIFT - distorção radial SIFT - método SIFT - scale invariant features transform - algoritmo |
description |
The detection and description of local image features is fundamental for different computer vision applications, such as object recognition, image content retrieval, and structure from motion. In the last few years the topic deserved the attention of different authors, with several methods and techniques being currently available in the literature. The SIFT algorithm, proposed in [2], gained particular prominence because of its simplicity and invariance to common image transformations like scaling and rotation. Unfortunately the approach is not able to cope with non-linear image deformations caused by radial lens distortion. The invariance to radial distortion is highly relevant for applications that either require a wide field of view (e.g. panoramic vision), or employ cameras with specific optical arrangements enabling the visualization of small spaces and cavities (e.g. medical endoscopy). One of the objectives of this thesis is to understand how radial distortion impacts the detection and description of keypoints using the SIFT algorithm. We perform a set of experiments that clearly show that distortion affects both the repeatability of detection and the invariance of the SIFT description. These results are analyzed in detail and explained from a theoretical viewpoint. In addition, we propose a novel approach for detection and description of stable local features in images with radial distortion. The detection is carried in a scale-space image representation built using an adaptive gaussian filter that takes into account distortion, and the feature description is performed after implicit gradient correction using the derivative chain rule. Our approach only requires a rough modeling of the radial distortion function and, for moderate levels of distortion, it outperforms the application of the SIFT algorithm after explicit image correction. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-07 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/11318 http://hdl.handle.net/10316/11318 |
url |
http://hdl.handle.net/10316/11318 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lourenço, António Miguel - Techniques for keypoint detection and matching between endoscopic images. Coimbra, 2009 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799133893784240128 |