Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Computer Analysis of Images and Patterns |
| Untertitel | 16th International Conference, CAIP 2015, Proceedings, Part I |
| Herausgeber/-innen | George Azzopardi, Nicolai Petkov |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 374-386 |
| Seitenumfang | 13 |
| ISBN (Print) | 9783319231914 |
| Publikationsstatus | Veröffentlicht - 25 Aug. 2015 |
| Veranstaltung | 16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015 - Valletta, Malta Dauer: 2 Sept. 2015 → 4 Sept. 2015 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 9256 |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
The extraction of scale invariant image features is a fundamental task for many computer vision applications. Features are localized in the scale space of the image. A descriptor is build for each feature which is used to determine the correspondence to a second feature, usually extracted from a second image. For the evaluation of detectors and descriptors, benchmark image sets are used. The benchmarks consist of image sequences and homographies which determine the ground truth for the mapping between the images. The repeatability criterion evaluates the detection accuracy of the detectors while precision and recall measure the quality of the descriptors. Current data sets provide images with resolutions of less than one megapixel. A recent data set provides challenging images and highly accurate homographies. It allows for the evaluation at different image resolutions with the same scene content. Thus, the scale invariant properties of the extracted features can be examined. This paper presents a comprehensive evaluation of state of the art detectors and descriptors on this data set. The results show significant differences compared to the standard benchmark. Furthermore, it is shown that some detectors perform differently on different resolutions. It follows that high resolution images should be considered for future feature evaluations.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Proceedings, Part I. Hrsg. / George Azzopardi; Nicolai Petkov. Springer Verlag, 2015. S. 374-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9256).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Feature Evaluation with High-Resolution Images
AU - Cordes, Kai
AU - Grundmann, Lukas
AU - Ostermann, Jörn
PY - 2015/8/25
Y1 - 2015/8/25
N2 - The extraction of scale invariant image features is a fundamental task for many computer vision applications. Features are localized in the scale space of the image. A descriptor is build for each feature which is used to determine the correspondence to a second feature, usually extracted from a second image. For the evaluation of detectors and descriptors, benchmark image sets are used. The benchmarks consist of image sequences and homographies which determine the ground truth for the mapping between the images. The repeatability criterion evaluates the detection accuracy of the detectors while precision and recall measure the quality of the descriptors. Current data sets provide images with resolutions of less than one megapixel. A recent data set provides challenging images and highly accurate homographies. It allows for the evaluation at different image resolutions with the same scene content. Thus, the scale invariant properties of the extracted features can be examined. This paper presents a comprehensive evaluation of state of the art detectors and descriptors on this data set. The results show significant differences compared to the standard benchmark. Furthermore, it is shown that some detectors perform differently on different resolutions. It follows that high resolution images should be considered for future feature evaluations.
AB - The extraction of scale invariant image features is a fundamental task for many computer vision applications. Features are localized in the scale space of the image. A descriptor is build for each feature which is used to determine the correspondence to a second feature, usually extracted from a second image. For the evaluation of detectors and descriptors, benchmark image sets are used. The benchmarks consist of image sequences and homographies which determine the ground truth for the mapping between the images. The repeatability criterion evaluates the detection accuracy of the detectors while precision and recall measure the quality of the descriptors. Current data sets provide images with resolutions of less than one megapixel. A recent data set provides challenging images and highly accurate homographies. It allows for the evaluation at different image resolutions with the same scene content. Thus, the scale invariant properties of the extracted features can be examined. This paper presents a comprehensive evaluation of state of the art detectors and descriptors on this data set. The results show significant differences compared to the standard benchmark. Furthermore, it is shown that some detectors perform differently on different resolutions. It follows that high resolution images should be considered for future feature evaluations.
UR - http://www.scopus.com/inward/record.url?scp=84945980029&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23192-1_31
DO - 10.1007/978-3-319-23192-1_31
M3 - Conference contribution
AN - SCOPUS:84945980029
SN - 9783319231914
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 374
EP - 386
BT - Computer Analysis of Images and Patterns
A2 - Azzopardi, George
A2 - Petkov, Nicolai
PB - Springer Verlag
T2 - 16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015
Y2 - 2 September 2015 through 4 September 2015
ER -