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2004

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	A. Akav	1篇
	G. H. Zalmanson	1篇
	Y. Doytsher	1篇

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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

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1.For the past fifteen years line photogrammetry has been an extremely active area of research in the photogrammetry and computer vision communities. It differs from traditional analytical photogrammetry in the nature of the primitives employed in a variety of its fundamental tasks. While in traditional photogrammetry zero-dimensional entities, i.e., points are exclusively used as a driving power in its various orientation and exploitation procedures – in line photogrammetry as the name suggests linear, that is one-dimensional features often corresponding to elongated man-made features in the object space are employed. Of course, that means that no prior correspondence between distinct point in object space and its projection in the image is required and the entire linear feature (with arbitrary geometry) is accommodated in the appropriate mathematical model. However, despite a great effort in that field, only the resection problem, i.e. the solution of the exterior orientation from linear features' correspondences has been thoroughly investigated so far. Two additional fundamental photogrammetric problems - space intersection and relative orientation, completing a triple of the most basic photogrammetic procedures needed to support feature-based triangulation have not been adequately addressed in the literature. This paper provides that missing link by presenting a procedure for relative orientation parameters estimation from linear features. We restrict our attention in this paper to planer curves only. We start with the simple idea of optimization procedure using ICP algorithm and proceed to the recovery of the homography matrix induced by the plane of the curve in space

作　　者：A. Akav ;G. H. Zalmanson ;Y. Doytsher

出　　处：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2004 ;XXXV Part B3:7-12.

出版社：Copernicus Publications

文章 ID：181187499

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