An evaluation of recent local image descriptors for real-world applications of image matching

Risultato della ricerca: Conference contribution

1 Citazione (Scopus)

Abstract

This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but also the global scene structure. Data-driven approaches are shown to have reached the matching robustness and accuracy of the best hand-crafted descriptors
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
Pagine1-6
Numero di pagine6
Stato di pubblicazionePublished - 2019

Fingerprint

Image matching
Degradation

Cita questo

Bellavia, F. (2019). An evaluation of recent local image descriptors for real-world applications of image matching. In Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019 (pagg. 1-6)

An evaluation of recent local image descriptors for real-world applications of image matching. / Bellavia, Fabio.

Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. 2019. pag. 1-6.

Risultato della ricerca: Conference contribution

Bellavia, F 2019, An evaluation of recent local image descriptors for real-world applications of image matching. in Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. pagg. 1-6.
Bellavia F. An evaluation of recent local image descriptors for real-world applications of image matching. In Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. 2019. pag. 1-6
Bellavia, Fabio. / An evaluation of recent local image descriptors for real-world applications of image matching. Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019. 2019. pagg. 1-6
@inproceedings{d32cb0a3fdd843bd91053f7b7417369b,
title = "An evaluation of recent local image descriptors for real-world applications of image matching",
abstract = "This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but also the global scene structure. Data-driven approaches are shown to have reached the matching robustness and accuracy of the best hand-crafted descriptors",
author = "Fabio Bellavia",
year = "2019",
language = "English",
isbn = "978-4-901122-18-4",
pages = "1--6",
booktitle = "Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019",

}

TY - GEN

T1 - An evaluation of recent local image descriptors for real-world applications of image matching

AU - Bellavia, Fabio

PY - 2019

Y1 - 2019

N2 - This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but also the global scene structure. Data-driven approaches are shown to have reached the matching robustness and accuracy of the best hand-crafted descriptors

AB - This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but also the global scene structure. Data-driven approaches are shown to have reached the matching robustness and accuracy of the best hand-crafted descriptors

UR - http://hdl.handle.net/10447/385505

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8750992

M3 - Conference contribution

SN - 978-4-901122-18-4

SP - 1

EP - 6

BT - Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019

ER -