A ground truth correspondence measure for benchmarking
(2006) 18th International Conference on Pattern Recognition, ICPR 2006 3. p.568-573- Abstract
- Automatic localisation of correspondences for the construction of Statistical Shape Models from examples has been the focus of intense research during the last decade. Several algorithms are available and benchmarking is needed to rank the different algorithms. Prior work has focused on evaluating the quality of the models produced by the algorithms by measuring compactness, generality and specificity. In this paper problems with these standard measures are discussed. We propose that a ground truth correspondence measure (gcm) is used for benchmarking and in this paper benchmarking is performed on several state of the art algorithms. Minimum Description Length (MDL) with a curvature cost comes out as the winner of the automatic methods.... (More)
- Automatic localisation of correspondences for the construction of Statistical Shape Models from examples has been the focus of intense research during the last decade. Several algorithms are available and benchmarking is needed to rank the different algorithms. Prior work has focused on evaluating the quality of the models produced by the algorithms by measuring compactness, generality and specificity. In this paper problems with these standard measures are discussed. We propose that a ground truth correspondence measure (gcm) is used for benchmarking and in this paper benchmarking is performed on several state of the art algorithms. Minimum Description Length (MDL) with a curvature cost comes out as the winner of the automatic methods. Hand marked models turn out to be best but a semi-automatic method is shown to lie in between the best automatic method and the hand built models in performance. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/616883
- author
- Karlsson, Johan LU and Ericsson, Anders LU
- organization
- publishing date
- 2006
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Statistical Shape Models, Minimum Description Length (MDL)
- host publication
- Proceedings - International Conference on Pattern Recognition
- volume
- 3
- pages
- 568 - 573
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 18th International Conference on Pattern Recognition, ICPR 2006
- conference location
- Hong Kong, China
- conference dates
- 2006-08-20 - 2006-08-24
- external identifiers
-
- wos:000240705600136
- scopus:34147101729
- ISSN
- 1051-4651
- DOI
- 10.1109/ICPR.2006.76
- language
- English
- LU publication?
- yes
- id
- 8589f93f-d276-45f1-ad8e-1b11750b2efa (old id 616883)
- date added to LUP
- 2016-04-01 16:58:07
- date last changed
- 2022-04-23 01:48:41
@inproceedings{8589f93f-d276-45f1-ad8e-1b11750b2efa, abstract = {{Automatic localisation of correspondences for the construction of Statistical Shape Models from examples has been the focus of intense research during the last decade. Several algorithms are available and benchmarking is needed to rank the different algorithms. Prior work has focused on evaluating the quality of the models produced by the algorithms by measuring compactness, generality and specificity. In this paper problems with these standard measures are discussed. We propose that a ground truth correspondence measure (gcm) is used for benchmarking and in this paper benchmarking is performed on several state of the art algorithms. Minimum Description Length (MDL) with a curvature cost comes out as the winner of the automatic methods. Hand marked models turn out to be best but a semi-automatic method is shown to lie in between the best automatic method and the hand built models in performance.}}, author = {{Karlsson, Johan and Ericsson, Anders}}, booktitle = {{Proceedings - International Conference on Pattern Recognition}}, issn = {{1051-4651}}, keywords = {{Statistical Shape Models; Minimum Description Length (MDL)}}, language = {{eng}}, pages = {{568--573}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{A ground truth correspondence measure for benchmarking}}, url = {{http://dx.doi.org/10.1109/ICPR.2006.76}}, doi = {{10.1109/ICPR.2006.76}}, volume = {{3}}, year = {{2006}}, }