MLESAC

Guided-MLESAC: faster image transform estimation by using matching priors. MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This paper explains the consequences of that omission and describes how the algorithm’s theoretical standing and practical performance can be enhanced by deriving estimates of these prior probabilities. Using the priors in guided-MLESAC is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter. This paper describes two further modifications to guided-MLESAC. The first shows how all putative matches, rather than just the best, from a particular feature can be taken forward into the sampling stage, albeit at the expense of additional computation. The second suggests how to propagate the output from one frame forward to successive frames. The additional information makes guided-MLESAC computationally realistic at video-rates for correspondence sets modeled by two transformations and clutter.


References in zbMATH (referenced in 8 articles )

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  1. Li, Qiming; Lan, Xiaodong; Li, Jun: Information fusion on the two-layer network for robust estimation of multiple geometric structures (2020)
  2. Shi, Buhai; Zhang, Qingming; Xu, Haibo: A geometrical-information-assisted approach for local feature matching (2019)
  3. İmre, Evren; Hilton, Adrian: Order statistics of RANSAC and their practical application (2015)
  4. Li, Xiangru; Wang, Guanghui; Wu, Q. M. Jonathan: Research on the fundamental principles and characteristics of correspondence function (2015)
  5. Tran, Quoc Huy; Chin, Tat-Jun; Chojnacki, Wojciech; Suter, David: Sampling minimal subsets with large spans for robust estimation (2014)
  6. Li, Xiangru; Hu, Zhanyi: Rejecting mismatches by correspondence function (2010) ioport
  7. Scherer-Negenborn, Norbert; Schaefer, Rolf: Model fitting with sufficient random sample coverage (2010) ioport
  8. Tordoff, Ben J.; Murray, David W.: Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors (2005) ioport