Gabor wavelet networks for object representation
(2001) 10th International Workshop on Theoretical Foundations of Computer Vision, 2000 In Lecture Notes in Computer Science 2032. p.115-128- Abstract
In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor filters ensured that geometrical and textural object features are encoded. The feasibility of the Gabor filters as a model for local object features ensures a considerable data reduction while at the same time allowing any desired precision of the object representation ranging from a sparse to a photo-realistic representation. In the second part of the paper we will present an approach for the estimation of a head pose that is... (More)
In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor filters ensured that geometrical and textural object features are encoded. The feasibility of the Gabor filters as a model for local object features ensures a considerable data reduction while at the same time allowing any desired precision of the object representation ranging from a sparse to a photo-realistic representation. In the second part of the paper we will present an approach for the estimation of a head pose that is based on the Gabor wavelet networks.
(Less)
- author
- Krüger, Volker LU and Sommer, Gerald
- publishing date
- 2001-05-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Multi-Image Analysis : 10th International Workshop on Theoretical Foundations of Computer Vision, Revised Papers - 10th International Workshop on Theoretical Foundations of Computer Vision, Revised Papers
- series title
- Lecture Notes in Computer Science
- editor
- Klette, Reinhard ; Gimel’farb, Georgy and Huang, Thomas
- volume
- 2032
- pages
- 14 pages
- publisher
- Springer
- conference name
- 10th International Workshop on Theoretical Foundations of Computer Vision, 2000
- conference location
- Dagstuhl Castle, Germany
- conference dates
- 2000-03-12 - 2000-03-17
- external identifiers
-
- scopus:84872563097
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 354042122X
- 9783540421221
- DOI
- 10.1007/3-540-45134-X_9
- language
- English
- LU publication?
- no
- id
- b481f384-759e-4772-8c58-14b97f4f13ed
- date added to LUP
- 2019-07-08 21:28:40
- date last changed
- 2024-01-01 15:54:21
@inbook{b481f384-759e-4772-8c58-14b97f4f13ed, abstract = {{<p>In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor filters ensured that geometrical and textural object features are encoded. The feasibility of the Gabor filters as a model for local object features ensures a considerable data reduction while at the same time allowing any desired precision of the object representation ranging from a sparse to a photo-realistic representation. In the second part of the paper we will present an approach for the estimation of a head pose that is based on the Gabor wavelet networks.</p>}}, author = {{Krüger, Volker and Sommer, Gerald}}, booktitle = {{Multi-Image Analysis : 10th International Workshop on Theoretical Foundations of Computer Vision, Revised Papers}}, editor = {{Klette, Reinhard and Gimel’farb, Georgy and Huang, Thomas}}, isbn = {{354042122X}}, issn = {{0302-9743}}, language = {{eng}}, month = {{05}}, pages = {{115--128}}, publisher = {{Springer}}, series = {{Lecture Notes in Computer Science}}, title = {{Gabor wavelet networks for object representation}}, url = {{http://dx.doi.org/10.1007/3-540-45134-X_9}}, doi = {{10.1007/3-540-45134-X_9}}, volume = {{2032}}, year = {{2001}}, }