Computer Vision – ACCV 2007: 8th Asian Conference on - download pdf or read online
By Ramesh Raskar (auth.), Yasushi Yagi, Sing Bing Kang, In So Kweon, Hongbin Zha (eds.)
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Additional resources for Computer Vision – ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part I
We consider a problem in which a set of measurements are made and we need to ﬁt a parametrized model of some kind to these measurements. Optimization involves ﬁnding the set of parameters of the model that best ﬁt the purpose. The optimization problem is deﬁned in terms of a deﬁned cost function which must be minimized over the set of 16 R. Hartley and F. Kahl meaningful parameters. However, what particular cost functions merit being called “optimal” will be considered in the rest of this section.
Camera and car mount used for urban mapping. Images from non-overlapping cameras on both sides of the car can be used to do odometry of the vehicle. An algorithm based on triangulation and SOCP is proposed in . (The image is used with permission of the UNC Vision group). 10 Concluding Remarks The application of new optimization methods to the problems of Multiview Geometry have led to the development of reliably and provably optimal solutions under diﬀerent geometrically meaningful cost functions.
We do not attempt here to enumerate all such problems considered in the literature. One notable example, however, is the relative orientation (two-view reconstruction) problem with 5 point correspondences. 3 The second of these references gives a very pleasing derivation of this result. Recent simple algorithms for solving this problem by ﬁnding the roots of a polynomial have been given in [31,26]. Methods for computing a polynomial solution need not result in a polynomial of the smallest possible degree.
Computer Vision – ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part I by Ramesh Raskar (auth.), Yasushi Yagi, Sing Bing Kang, In So Kweon, Hongbin Zha (eds.)