IEICE TRANSACTIONS on Information
Non-rigid Object Recognition Using Multidimensional Index Geometric Hashing
Summary :
A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.
- Publication
- IEICE TRANSACTIONS on Information Vol.E81-D No.8 pp.901-908
- Publication Date
- 1998/08/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Kridanto SURENDRO, Yuichiro ANZAI, "Non-rigid Object Recognition Using Multidimensional Index Geometric Hashing" in IEICE TRANSACTIONS on Information,
vol. E81-D, no. 8, pp. 901-908, August 1998, doi: .
Abstract: A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_8_901/_p
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@ARTICLE{e81-d_8_901,
author={Kridanto SURENDRO, Yuichiro ANZAI, },
journal={IEICE TRANSACTIONS on Information},
title={Non-rigid Object Recognition Using Multidimensional Index Geometric Hashing},
year={1998},
volume={E81-D},
number={8},
pages={901-908},
abstract={A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Non-rigid Object Recognition Using Multidimensional Index Geometric Hashing
T2 - IEICE TRANSACTIONS on Information
SP - 901
EP - 908
AU - Kridanto SURENDRO
AU - Yuichiro ANZAI
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1998
AB - A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.
ER -
English
