Revisiting Hartley's normalized eight-point algorithm
Date
2003
Authors
Chojnacki, W.
Brooks, M.
Van Den Hengel, A.
Gawley, D.
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Journal article
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(9):1172-1177
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Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawley
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Abstract
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 I!; 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.
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Copyright © 2003 IEEE