Determining the Epipolar Geometry and its Uncertainty: A Review

被引:0
作者
Zhengyou Zhang
机构
[1] INRIA,
来源
International Journal of Computer Vision | 1998年 / 27卷
关键词
epipolar geometry; fundamental matrix; calibration; reconstruction; parameter estimation; robust techniques; uncertainty characterization; performance evaluation; software;
D O I
暂无
中图分类号
学科分类号
摘要
Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3×3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and vehicle navigation. This paper gives an introduction to the epipolar geometry, and provides a complete review of the current techniques for estimating the fundamental matrix and its uncertainty. A well-founded measure is proposed to compare these techniques. Projective reconstruction is also reviewed. The software which we have developed for this review is available on the Internet.
引用
收藏
页码:161 / 195
页数:34
相关论文
共 46 条
[1]  
Aggarwal J.(1988)On the computation of motion from sequences of images-A review Proc. IEEE 76 917-935
[2]  
Nandhakumar N.(1990)Perspective approximations Image and Vision Computing 8 179-192
[3]  
Aloimonos J.(1996)Characterizing the uncertainty of the fundamental matrix Computer Vision and Image Understanding 68 18-36
[4]  
Csurka G.(1995)Stratification of 3-D vision: Projective, affine, and metric representations Journal of the Optical Society of America A 12 465-484
[5]  
Zeller C.(1988)Motion and structure from motion in a piecewise planar environment International Journal of Pattern Recognition and Artificial Intelligence 2 485-508
[6]  
Zhang Z.(1981)Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography Communications of the ACM 24 381-385
[7]  
Faugeras O.(1986)Computer vision theory: The lack thereof Computer Vision, Graphics, and Image Processing 36 372-386
[8]  
Faugeras O.(1994)Projective reconstruction and invariants from multiple images IEEE Transactions on Pattern Analysis and Machine Intelligence 16 1036-1040
[9]  
Faugeras O.(1992)Subspace methods for recovering rigid motion I: Algorithm and implementation The International Journal of Computer Vision 7 95-117
[10]  
Lustman F.(1863)Die cubische gleichung, von welcher die Lösung des problems der homographie von M. Chasles Abhängt. J. Reine Angew. Math. 62 188-192