Details
| Original language | English |
|---|---|
| Title of host publication | Computer Vision - ECCV 2010 |
| Subtitle of host publication | 11th European Conference on Computer Vision, Proceedings |
| Pages | 128-141 |
| Number of pages | 14 |
| Edition | PART 2 |
| Publication status | Published - 2010 |
| Event | 11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece Duration: 10 Sept 2010 → 11 Sept 2010 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 6312 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In order to achieve a complete image description, we introduce no-feature-features (NF-features) representing object regions where regular interest point detectors do not detect features. As these regions are usually non-textured, stable re-localization in different images with conventional methods is not possible. Therefore, a technique is presented which re-localizes once-detected NF-features using correspondences of regular features. Furthermore, a distinctive NF descriptor for non-textured regions is derived which has invariance towards affine transformations and changes in illumination. For the matching of NF descriptors, an approach is introduced that is based on local image statistics. NF-features can be used complementary to all kinds of regular feature detection and description approaches that focus on textured regions, i.e. points, blobs or contours. Using SIFT, MSER, Hessian-Affine or SURF as regular detectors, we demonstrate that our approach is not only suitable for the description of non-textured areas but that precision and recall of the NF-features is significantly superior to those of regular features. In experiments with high variation of the perspective or image perturbation, at unchanged precision we achieve NF recall rates which are better by more than a factor of two compared to recall rates of regular features.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Computer Vision - ECCV 2010: 11th European Conference on Computer Vision, Proceedings. PART 2. ed. 2010. p. 128-141 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6312 LNCS, No. PART 2).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - NF-features
T2 - 11th European Conference on Computer Vision, ECCV 2010
AU - Dragon, Ralf
AU - Shoaib, Muhammad
AU - Rosenhahn, Bodo
AU - Ostermann, Joern
PY - 2010
Y1 - 2010
N2 - In order to achieve a complete image description, we introduce no-feature-features (NF-features) representing object regions where regular interest point detectors do not detect features. As these regions are usually non-textured, stable re-localization in different images with conventional methods is not possible. Therefore, a technique is presented which re-localizes once-detected NF-features using correspondences of regular features. Furthermore, a distinctive NF descriptor for non-textured regions is derived which has invariance towards affine transformations and changes in illumination. For the matching of NF descriptors, an approach is introduced that is based on local image statistics. NF-features can be used complementary to all kinds of regular feature detection and description approaches that focus on textured regions, i.e. points, blobs or contours. Using SIFT, MSER, Hessian-Affine or SURF as regular detectors, we demonstrate that our approach is not only suitable for the description of non-textured areas but that precision and recall of the NF-features is significantly superior to those of regular features. In experiments with high variation of the perspective or image perturbation, at unchanged precision we achieve NF recall rates which are better by more than a factor of two compared to recall rates of regular features.
AB - In order to achieve a complete image description, we introduce no-feature-features (NF-features) representing object regions where regular interest point detectors do not detect features. As these regions are usually non-textured, stable re-localization in different images with conventional methods is not possible. Therefore, a technique is presented which re-localizes once-detected NF-features using correspondences of regular features. Furthermore, a distinctive NF descriptor for non-textured regions is derived which has invariance towards affine transformations and changes in illumination. For the matching of NF descriptors, an approach is introduced that is based on local image statistics. NF-features can be used complementary to all kinds of regular feature detection and description approaches that focus on textured regions, i.e. points, blobs or contours. Using SIFT, MSER, Hessian-Affine or SURF as regular detectors, we demonstrate that our approach is not only suitable for the description of non-textured areas but that precision and recall of the NF-features is significantly superior to those of regular features. In experiments with high variation of the perspective or image perturbation, at unchanged precision we achieve NF recall rates which are better by more than a factor of two compared to recall rates of regular features.
UR - http://www.scopus.com/inward/record.url?scp=78149353401&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15552-9_10
DO - 10.1007/978-3-642-15552-9_10
M3 - Conference contribution
AN - SCOPUS:78149353401
SN - 3642155510
SN - 9783642155512
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 128
EP - 141
BT - Computer Vision - ECCV 2010
Y2 - 10 September 2010 through 11 September 2010
ER -