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31 January 2020 Comparative study of feature detector and descriptor methods for registration
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114333H (2020) https://doi.org/10.1117/12.2559931
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Abstract
Image registration requires a step of detection and matching of primitives. This phase is important to obtain reliable registration. In this paper, we mainly focus on geometric registration methods which are based on the extraction and matching of distinctive feature points in images. Several methods such as SIFT, SURF, BRIEF, BRISK, ORB, FREAK and FRIF, are already proposed. In this paper, we present a comparative study of feature detector and descripts methods for registration which can be classified according to the type of descriptor and can be local classical or binary. We have presented, through this study, the difference between geometric methods of descriptor leveling as well as points of interest detector used and which have an influence on the resetting registration result. We can see that each method has weak points as well as strong points. The major difference is the level of invariance to the type of processing and the temporal complexity.
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Wissal Ben Marzouka, Basel Solaiman, Atef Hamouda, Zouhour Ben Dhiaf, and Khaled Bsaies "Comparative study of feature detector and descriptor methods for registration", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114333H (31 January 2020); https://doi.org/10.1117/12.2559931
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