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| Richard I. Hartley, "In Defense of the Eight-Point Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 580-593, June, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/34.601246, author = {Richard I. Hartley}, title = {In Defense of the Eight-Point Algorithm}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {6}, issn = {0162-8828}, year = {1997}, pages = {580-593}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.601246}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - In Defense of the Eight-Point Algorithm IS - 6 SN - 0162-8828 SP580 EP593 EPD - 580-593 A1 - Richard I. Hartley, PY - 1997 KW - Fundamental matrix KW - eight-point algorithm KW - condition number KW - epipolar structure KW - stereo vision. VL - 19 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images.
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