Robust 3D feature detection using dictionary-based relaxation

Nigel Sharp; Edwin R. Hancock

doi:10.1117/12.186013

16 September 1994 Robust 3D feature detection using dictionary-based relaxation

Nigel Sharp, Edwin R. Hancock

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.186013
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

The problem of grouping 3D coplanar line segmented obtained from a single view is addressed. The proposed method is efficient and has been tested on both synthetic and real images. First, a Hough-based algorithm is used to detect 2D line segments in a sequence of images representing a 3D scene. Secondly, the 3D coordinates of the line segments are estimated, at each time instant, by means of an extended Kalman filter, based on the displacements (u,v) of the line segment endpoints on the image plane. Finally, 3D coplanar segments are grouped by a 3D voting approach. The novelty of this method lies in the possibility of using a simple voting scheme similar to that associated with the standard Hough transform for line extraction, where each edge point votes for a sheaf of rectilinear lines. In the proposed approach, each line segment votes for a sheaf of planes.

Citation Download Citation

Nigel Sharp and Edwin R. Hancock "Robust 3D feature detection using dictionary-based relaxation", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.186013

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