6533b85bfe1ef96bd12baae4
RESEARCH PRODUCT
An evaluation of recent local image descriptors for real-world applications of image matching
Carlo ColomboFabio Bellaviasubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - InformaticaImage matchingComputer sciencebusiness.industryVisual descriptorsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognition02 engineering and technology03 medical and health sciences0302 clinical medicineLocal Image Descriptors; Image MatchingRobustness (computer science)Computer Science::Computer Vision and Pattern RecognitionComputer Science::Multimedia030221 ophthalmology & optometry0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessImage matching Data-driven approach Descriptors Evaluation results Local descriptors Local image descriptors Performance degradation Real-worldScene structure Computer visiondescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-05-01 |