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• W2921830172 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." @default.
• W2921830172 created "2019-03-22" @default.
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• W2921830172 date "2019-05-01" @default.
• W2921830172 modified "2023-09-27" @default.
• W2921830172 title "An Evaluation of Recent Local Image Descriptors for Real-World Applications of Image Matching" @default.
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• W2921830172 doi "https://doi.org/10.23919/mva.2019.8757967" @default.
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