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Issue No.09 - September (2003 vol.25)
pp: 1172-1177
ABSTRACT
<p><b>Abstract</b>—Hartley's eight-point algorithm has maintained an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalized data. It is first established that the normalized algorithm acts to minimize a specific cost function. It is then shown that this cost function is statistically better founded than the cost function associated with the nonnormalized algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.</p>
INDEX TERMS
Epipolar equation, fundamental matrix, eight-point algorithm, data normalization.
CITATION
Michael J. Brooks, Wojciech Chojnacki, Darren Gawley, "Revisiting Hartley's Normalized Eight-Point Algorithm", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.25, no. 9, pp. 1172-1177, September 2003, doi:10.1109/TPAMI.2003.1227992
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