Outlier rejection by oriented tracks to aid pose estimation from video

Grinstead, Brad; Koschan, Andreas; Gribok, Andrei V.; Abidi, Mongi A.; Gorsich, David

doi:10.1016/j.patrec.2005.06.013

Cited by 3 publications

(2 citation statements)

References 31 publications

(26 reference statements)

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“…There has been research in terms of rejecting outliers when initial matches include a large number of outliers (Adam et al, 2001;Grinstead and Koschan, 2006). Adam et al (2001) proved that all the lines joining the correct matches make a common orientation when the feature points in one image are rotated by a certain angle.…”

Section: Introductionmentioning

confidence: 99%

“…This limitation is critical for intelligent vehicle applications because many current applications adopt wide-angle cameras (Fintze1 et al, 2004;Gandhi and Trivedi, 2005;Jung et al, 2006;Mouragnon et al, 2006a,b;Royer et al, 2005;Suhr et al, 2007;Takahashi et al, 2002;Vestri et al, 2005). Grinstead and Koschan (2006) found that the orientations of the lines joining the correct matches were the same when camera motion was perpendicular to its optical axis. They calculated the orientations of all the joining lines and estimated the probability distribution in order to find the common orientation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Outlier rejection for cameras on intelligent vehicles

Suhr

Jung

Bae

et al. 2008

Pattern Recognition Letters

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This paper proposes an algorithm for rejecting false matches (known as outliers) in image pairs acquired with automobile-mounted cameras. Many intelligent vehicle applications require point correspondences for motion estimation and 3D reconstruction which can be affected by outliers. We use the property of automobile motion to reject outliers. Automobile motion mostly represented by two translations and one rotation. The proposed algorithm eliminates the rotational effect and estimates the focus of expansion (FOE). Once the FOE is estimated, the joining line directions with respect to the FOE are used for rejecting the outliers. This algorithm is simple and its computational cost is independent of the outlier percentage. Experimental results show that the proposed algorithm rejects a large number of outliers but retains most inliers when working with synthetic and real image pairs. It works even when the initial matches are contaminated by 80-90% of the outliers. As a pre-processing step of RANSAC, it significantly reduces the computational cost when initial matches include a large number of outliers.

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Section: Introductionmentioning

confidence: 99%