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|Title:||Revisiting Hartley's normalized eight-point algorithm|
Van Den Hengel, A.
|Citation:||IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(9):1172-1177|
|Publisher:||IEEE Computer Soc|
|Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawley|
|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.|
|Keywords:||Epipolar equation; fundamental matrix; eight-point algorithm; data normalization|
|Description:||Copyright © 2003 IEEE|
|Appears in Collections:||Computer Science publications|
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