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dc.contributor.authorBharati, Sushil Pratap
dc.contributor.authorCen, Feng
dc.contributor.authorSharda, Ajay
dc.contributor.authorWang, Guanghui
dc.date.accessioned2019-12-12T21:39:07Z
dc.date.available2019-12-12T21:39:07Z
dc.date.issued2018-08-30
dc.identifier.citationS. P. Bharati, F. Cen, A. Sharda and G. Wang, "RES-Q: Robust Outlier Detection Algorithm for Fundamental Matrix Estimation," in IEEE Access, vol. 6, pp. 48664-48674, 2018. doi: 10.1109/ACCESS.2018.2867915en_US
dc.identifier.urihttp://hdl.handle.net/1808/29856
dc.description.abstractDetection of outliers present in noisy images for an accurate fundamental matrix estimation is an important research topic in the field of 3-D computer vision. Although a lot of research is conducted in this domain, not much study has been done in utilizing the robust statistics for successful outlier detection algorithms. This paper proposes to utilize a reprojection residual error-based technique for outlier detection. Given a noisy stereo image pair obtained from a pair of stereo cameras and a set of initial point correspondences between them, reprojection residual error and 3-sigma principle together with robust statistic-based Qn estimator (RES-Q) is proposed to efficiently detect the outliers and estimate the fundamental matrix with superior accuracy. The proposed RES-Q algorithm demonstrates greater precision and lower reprojection residual error than the state-of-the-art techniques. Moreover, in contrast to the assumption of Gaussian noise or symmetric noise model adopted by most previous approaches, the RES-Q is found to be robust for both symmetric and asymmetric random noise assumptions. The proposed algorithm is experimentally tested on both synthetic and real image data sets, and the experiments show that RES-Q is more effective and efficient than the classical outlier detection algorithms.en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights2018 IEEE.en_US
dc.subjectFundamental matrixen_US
dc.subjectStereo visionen_US
dc.subjectRobust statisticsen_US
dc.subjectOutliers detectionen_US
dc.titleRES-Q: Robust Outlier Detection Algorithm for Fundamental Matrix Estimationen_US
dc.typeArticleen_US
kusw.kuauthorBharati, Sushil Pratap
kusw.kuauthorWang, Guanghui
kusw.kudepartmentElectrical Engineering and Computer Scienceen_US
dc.identifier.doi10.1109/ACCESS.2018.2867915en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3182-104Xen_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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