A conditional random field model for 3D reconstruction in image sequences

Dazhi Zhang; Junbin Gong; Yongtao Wang

doi:10.1117/12.832739

30 October 2009 A conditional random field model for 3D reconstruction in image sequences

Dazhi Zhang, Junbin Gong, Yongtao Wang

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961E (2009) https://doi.org/10.1117/12.832739
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

An airborne vehicle must avoid obstacles like towers, fences, tree branches, mountains and building across the flight path. So the ability to detect and locate obstacles using on-board sensors is an essential step in the autonomous navigation of aircraft low-altitude flight. In this paper, a novel passive range method using conditional random field (CRF) is presented to map the 3D scene in front of a moving aircraft with image sequences obtained from a forward-looking imaging sensor. Finally, An dynamic graph cuts method was presented for the CRF model to recursively update thedepth map. Experimental data demonstrates the effectiveness of our approach.

Citation Download Citation

Dazhi Zhang, Junbin Gong, and Yongtao Wang "A conditional random field model for 3D reconstruction in image sequences", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961E (30 October 2009); https://doi.org/10.1117/12.832739

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