Robust Feature Matching Using The FESID Detector

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recently, interest point detectors and descriptors have become prominent in the field of computer vision and are typically used to determine correspondences between two images of the same scene. The finite element scale invariant detector (FESID) is based on a similar multi-scale approach to that used in the SURF detector. However, FESID detects point features rather than blob features, and by combining the derivative and smoothing operations into a single operator efficient performance is achieved. We illustrate the performance of the FESID algorithm with respect to both robustness and correct region matching.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherCambridge Scholars Publishing
Number of pages12
ISBN (Print)1-4438-2962-5
Publication statusPublished (in print/issue) - 2011
EventInternational Machine Vision and Image Processing Conference (IMVIP 2010) - Limerick, Ireland
Duration: 1 Jan 2011 → …


ConferenceInternational Machine Vision and Image Processing Conference (IMVIP 2010)
Period1/01/11 → …


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