Pose estimation of an active stereo system by principal moments of point features

Dan Cojoc; Paolo Grattoni; Giuseppe Pettiti; R. Nerino

doi:10.1117/12.324558

19 August 1998 Pose estimation of an active stereo system by principal moments of point features

Dan Cojoc, Paolo Grattoni, Giuseppe Pettiti, R. Nerino

Proceedings Volume 3573, OPTIKA '98: 5th Congress on Modern Optics; (1998) https://doi.org/10.1117/12.324558
Event: OPTIKA '98: Fifth Congress on Modern Optics, 1998, Budapest, Hungary

Abstract

The estimation of the pose variation (egomotion) of a vision system while looking at the same scene from two different poses is both of theoretical and practical interest in computer vision. In this work we deal with this problem by developing a method to estimate the relative motion of our stereo vision system with respect to an object surface in a static scene. The images of the object taken from different views are described by a set of well localized 2D features named key points with associated local descriptors. These descriptors are based on coefficients of a Gabor wavelet transform of the images, and on the 3D object co-ordinates of the corresponding points in the space. 3D point co- ordinates are evaluated by means of a fixation process performed by our active stereo vision system. A selection process of key points based on the similarity of their descriptors in the two images produces two sets with the same number of key points, every set having a high probability to be composed by the correspondent points in the other images. The centroids and the principal moment axes (roto-translation invariants) of the 3D point co- ordinates of the two sets are evaluated, and the relative motion between the two poses is recovered from this information, thus avoiding a direct point to point matching.

Citation Download Citation

Dan Cojoc, Paolo Grattoni, Giuseppe Pettiti, and R. Nerino "Pose estimation of an active stereo system by principal moments of point features", Proc. SPIE 3573, OPTIKA '98: 5th Congress on Modern Optics, (19 August 1998); https://doi.org/10.1117/12.324558

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KEYWORDS

3D image processing

Motion estimation

Image processing

Matrices

Stereo vision systems

Wavelet transforms

Computer vision technology

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