Feature Evaluation with High-Resolution Images

authored by: Kai Cordes, Lukas Grundmann, Jörn Ostermann
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.
Organisation(s): Institute of Microwave and Wireless Systems
Institute of Information Processing
Type: Conference contribution
Pages: 374-386
No. of pages: 13
Publication date: 25.08.2015
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Theoretical Computer Science, Computer Science(all)
Electronic version(s): https://doi.org/10.1007/978-3-319-23192-1_31 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@inproceedings{8c18783e32914cd89716b627363d89b7,
title = "Feature Evaluation with High-Resolution Images",
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.",
author = "Kai Cordes and Lukas Grundmann and J{\"o}rn Ostermann",
year = "2015",
month = aug,
day = "25",
doi = "10.1007/978-3-319-23192-1_31",
language = "English",
isbn = "9783319231914",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "374--386",
editor = "George Azzopardi and Nicolai Petkov",
booktitle = "Computer Analysis of Images and Patterns",
address = "Germany",
note = "16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015 ; Conference date: 02-09-2015 Through 04-09-2015",
}