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A statistical rationalisation of Hartley’s normalised eight-point algorithm (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Chojnacki, Wojciech Brooks, Michael J. Hengel, Anton Van Den Gawley, Darren |
| Description | In Proc. 12th Int. Conf. Image Analysis and Processing The eight-point algorithm of Hartley occupies 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 eightpoint algorithm that results from using normalised data. A first step is singling out a cost function that the normalised algorithm acts to minimise. The cost function is then shown to be statistically better founded than the cost function associated with the non-normalised 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. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Improved Performance Statistical Rationalisation Coherent Framework Important Place Cost Function First Step Novel Explanation Eightpoint Algorithm Estimation Technique Normalised Algorithm Act Non-normalised Algorithm Iterative Estimation Method Pivotal Matrix Original Argument Eight-point Algorithm Wider Effort Fundamental Matrix Experimental Result Initial Value Computer Vision |
| Content Type | Text |
| Resource Type | Article |
