S.B. Pollard, T.P. Pridmore, J. Porrill, J.E.W. Mayhew, and J.P. Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):3--32, 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....approach include the extension to multiple cameras and performance improvements by the use of robust methods. Two important choices in the design of the vision system for the work conducted in this thesis were, first, whether to use data driven structural recovery methods for polyhedra (e.g. [106,107]) or model based methods [17,18,19,20,21,22,23,24] and, secondly, whether to use calibrated vision to recover Euclidean structure or un or partially calibrated vision to recover structure modulo an unknown transformation. For the latter choice, whilst there are certainly tasks in hand eye ....

S.B. Pollard, T.P. Pridmore, J. Porrill, J.E.W. Mayhew, and J.P. Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):3--32, 1989.

....a lower bound on the number of features required from each image for it to do so. 1 Introduction The Kalman filter [1] has proved to be an immensely popular tool in computer vision, particularly in the field of tracking, e.g. 3, 5, 2] and also in parameter estimation [13] and stereo matching [7]. By contrast, another two issues raised by Kalman, controllability and observability, are seldom seen in the machine vision literature, with the exception of [12] who has shown their value as a design aid for linear time invariant filters. This paper gives the definitions of the two concepts, ....

S B Pollard, T P Pridmore, J Porrill, J E W Mayhew, and J P Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):132--138, 1989. British Machine Vision Conference 173

....on fragmented boundary structures, even if these structures are geometrically accurate. It is clear that accurate geometric information can be reliably extracted from intensity images as demonstratedby the assortment of working vision systems that have appeared over recent years in the literature [1, 2, 14, 15, 25, 31, 39]. These systems generally assume that only fragmentary topological structure is available. For example, two adjacent boundaries meeting at a vertex is considered to be a major feature discovery. By carrying out exhaustive grouping it is possible to align specific 3D models with set of ....

Pollard, S.B, Pridmore, T.P, Porrill, J., Mayhew, J.E.W. and Frisby, J.P. "Geometrical Modelling from Multiple Stereo Views," International Journal of Robotics Research, Vol. 8, No. 4, p.132-138, 1989.

....of camera focal length, results are presented from real experiments demonstrating that convergence is achieved for initial errors up to 50 . I. Introduction Scene reconstruction and object recognition are areas of computer vision which have been plagued by the need for accurate camera calibration [19]. Calibration typically requires objects made to high precision to be placed in front of the cameras [13] 8] and requires considerable experimental care, making the methods impractical for an autonomous robot acting in an unstructured world. For these reasons much emphasis has been placed ....

S.B. Pollard, T.P. Pridmore, J. Porrill, J.E.W. Mayhew, and J.P. Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):3--32, 1989.

....a lower bound on the number of features required from each image for it to do so. 1 Introduction The Kalman filter [1] has proved to be an immensely popular tool in computer vision, particularly in the field of tracking, e.g. 3, 5, 2] and also in parameter estimation [13] and stereo matching [7]. By contrast, another two issues raised by Kalman, controllability and observability, are seldom seen in the machine vision literature, with the exception of [12] who has shown their value as a design aid for linear time invariant filters. This paper gives the definitions of the two concepts, ....

S B Pollard, T P Pridmore, J Porrill, J E W Mayhew, and J P Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):132--138, 1989.

....proposed in [3] Because of that, the proposed covariance matrix CP is diagonal, and its uncertainty is independent of the location of the projection in the image plane. The values of oe 2 yP and oe 2 OEP depend on the size of the pixel and the length of the projection, as proposed in [6]. I L E f P z x y x y C x y x z y W Figure 1: The 2D edge (E) is represented by the plane defined by a point, the optical center (C) and a line , the projection (P ) of the 3D line (L reference) in the image plane (I) a ycp F NaN F NaN xcp fp rcp Un I P x y y Figure 2: Image parameters of the ....

S. B. Pollard, T. P. Pridmore, J. Porrill, J.E.W. Mayeheb, and J.P. Frisby. Geometrical modelling from multiple stereo views. Int. J. Robotics Research, 8(4):3--32, 1989.

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S.B. Pollard, T.P. Pridmore, J. Porrill, J.E.W. Mayhew, and J.P. Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):3--32, 1989.

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S.B. Pollard, T.P. Pridmore, J. Porrill, J.E.W. Mayhew, and J.P. Frisby. Geometrical modelling from multiple stereo views. International Journal of Robotics Research, 8(4):3--32, 1989.

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