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Implementing a Multi-Model Estimation Method

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Abstract

We revisit the problem of parameter estimation in computer vision, reconsidering and implementing what may be called the Kanatani's estimation method, presented here as a simple optimisation problem, so (a) without any direct reference to a probabilistic framework but (b) considering (i) non-linear implicit measurement equations and parameter constraints, plus (ii) robust estimation in the presence of outliers and (iii) multi-model comparisons.

Here, (A) a projection algorithm based on generalisations of square-root decompositions allows an efficient and numerically stable local resolution of a set of non-linear equations. On the other hand, (B) a robust estimation module of a hierarchy of non-linear models has been designed and validated.

A step ahead, (C) the software architecture of the estimation module is discussed with the goal of being integrated in reactive software environments or within applications with time constraints, while an experimentation considering the parameterisation of retinal displacements between two views is proposed as an illustration of the estimation module.

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Authors and Affiliations

  1. Sophia, INRIA, BP93, 06902, Valbonne, France

    T. Vieville, D. Lingrand & F. Gaspard

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  1. T. Vieville
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  2. D. Lingrand
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  3. F. Gaspard
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Vieville, T., Lingrand, D. & Gaspard, F. Implementing a Multi-Model Estimation Method. International Journal of Computer Vision 44, 41–64 (2001). https://doi.org/10.1023/A:1011120419133

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