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

Fabio Bellavia, Fabio Bellavia, Carlo Colombo

Risultato della ricerca: Conference contribution

3 Citazioni (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

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Signal Processing
  • Computer Vision and Pattern Recognition

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