Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 14th IAPR International Conference on Machine Vision Applications |
| Subtitle of host publication | MVA 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 295-298 |
| Number of pages | 4 |
| ISBN (electronic) | 9784901122153 |
| Publication status | Published - Jul 2015 |
| Event | 14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan Duration: 18 May 2015 → 22 May 2015 |
Abstract
The scale invariant feature operator (SFOP) detects circular features from an image using a spiral shape model. Special cases of the spiral model are junctions and circular symmetric shapes. The spatial localization is determined with subpixel accuracy which is obtained by an interpolation of the structure tensor in the scale space. For the interpolation, SFOP uses a 3D quadratic function. This leads to suboptimal solutions since the structure tensor surrounding a feature does not show the shape of a 3D quadratic. The aim of this paper is to improve the localization of the features detected by SFOP. A Difference of Gaussians function is proposed for the signal approximation which leads to improved precision values and to more accurate features. The proposed method improves the localization such that 72.5% of the features increase their precision. Hence, more features are extracted while increasing their repeatability by up to 9% on standard benchmarks.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Vision and Pattern Recognition
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Proceedings of the 14th IAPR International Conference on Machine Vision Applications: MVA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 295-298 7153189.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Increasing the Precision of Junction Shaped Features
AU - Cordes, Kai
AU - Ostermann, Jorn
PY - 2015/7
Y1 - 2015/7
N2 - The scale invariant feature operator (SFOP) detects circular features from an image using a spiral shape model. Special cases of the spiral model are junctions and circular symmetric shapes. The spatial localization is determined with subpixel accuracy which is obtained by an interpolation of the structure tensor in the scale space. For the interpolation, SFOP uses a 3D quadratic function. This leads to suboptimal solutions since the structure tensor surrounding a feature does not show the shape of a 3D quadratic. The aim of this paper is to improve the localization of the features detected by SFOP. A Difference of Gaussians function is proposed for the signal approximation which leads to improved precision values and to more accurate features. The proposed method improves the localization such that 72.5% of the features increase their precision. Hence, more features are extracted while increasing their repeatability by up to 9% on standard benchmarks.
AB - The scale invariant feature operator (SFOP) detects circular features from an image using a spiral shape model. Special cases of the spiral model are junctions and circular symmetric shapes. The spatial localization is determined with subpixel accuracy which is obtained by an interpolation of the structure tensor in the scale space. For the interpolation, SFOP uses a 3D quadratic function. This leads to suboptimal solutions since the structure tensor surrounding a feature does not show the shape of a 3D quadratic. The aim of this paper is to improve the localization of the features detected by SFOP. A Difference of Gaussians function is proposed for the signal approximation which leads to improved precision values and to more accurate features. The proposed method improves the localization such that 72.5% of the features increase their precision. Hence, more features are extracted while increasing their repeatability by up to 9% on standard benchmarks.
UR - http://www.scopus.com/inward/record.url?scp=84941253812&partnerID=8YFLogxK
U2 - 10.1109/mva.2015.7153189
DO - 10.1109/mva.2015.7153189
M3 - Conference contribution
AN - SCOPUS:84941253812
SP - 295
EP - 298
BT - Proceedings of the 14th IAPR International Conference on Machine Vision Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IAPR International Conference on Machine Vision Applications, MVA 2015
Y2 - 18 May 2015 through 22 May 2015
ER -