Robust Point Matching via Vector Field Consensus
By Jiayi Ma, Ji Zhao, Jinwen Tian, Alan L. Yuille, Zhuowen Tu. IEEE Transactions on Image Processing, 2014
551 citations recorded by the source corpus.
In this paper, we propose an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points. Our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences, or outliers, in addition to a limited number of true correspondences (inliers). Next, we solve for correspondence by interpolating a vector field between the two point sets, which involves estimating a consensus of inlier points whose matching follows a nonparametric geometrical constraint. We formulate this a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. We impose nonparametric geometrical constraints on the correspondence, as a prior distribution, using Tikhonov regularizers in a reproducing kernel Hilbert space. MAP estimation is performed by the EM algorithm which by also estimating the variance of the prior model (initialized to a large value) is able to obtain good estimates very quickly (e.g., avoiding many of the local minima inherent in this formulation). We illustrate this method on data sets in 2D and 3D and demonstrate that it is robust to a very large number of outliers (even up to 90%). We also show that in the special case where there is an underlying parametric geometrical model (e.g., the epipolar line constraint) that we obtain better results than standard alternatives like RANSAC if a large number of outliers are present. This suggests a two-stage strategy, where we use our nonparametric model to reduce the size of the putative set and then apply a parametric variant of our approach to estimate the geometric parameters. Our algorithm is computationally efficient and we provide code for others to use it. In addition, our approach is general and can be applied to other problems, such as learning with a badly corrupted training data set.
Subfields: Aerospace Engineering, Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/c2e3044f8c90bed73c90abdfbd39888445241f94
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Citations in the corpus
- A review of multimodal image matching: Methods and applications (2021, methodology): “In this experiment, 12 mismatch removal methods are used for comparison, namely, RANSAC [301], MAGSAC++ [317], ICF [323], VFC [319], LLT [325], GS [243], SM [241], LPM [330], GMS [329],”
- Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification (2016, methodology): “In our future work, we will extend the proposed feature extraction approach to more applications such as saliency modeling [25] and feature matching [26].”
- Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “(7) The mismatch removal methods include: RANSAC (Fischler and Bolles 1981) (abbreviated as RS), SM (Leordeanu and Hebert 2005), ICF (Li and Hu 2010), GS (Liu and Yan 2010), LO-RANSAC (Lebeda et al. 2012) (abbreviated as LRS), VFC (Ma et al. 2014), LPM (Ma et al. 2019d), GMS (Bian et al. 2017), and ”
- Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming (2015, methodology): “A popular strategy for solving the feature matching problem is to use a two-stage process [34]–[36].”
- Non-Rigid Point Set Registration by Preserving Global and Local Structures (2015, methodology): “introduced Robust Point Matching methods based on local features and a robust L2 E estimator (RPM-L2 E) [28], [29] or Vector Field Consensus [30], [31], which can exploit both global and local structures.”