IEEE Trans Image Process - Weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects.

Tópicos

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Resumo

In this paper, a weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects is proposed. First, we propose a new estimation method for computing the relative depth of the free endpoint on the 1D object and prove its robustness against noise compared with those used in previous literature. The introduced estimator is invariant to image similarity transforms, resulting in a similarity-invariant linear calibration algorithm which is slightly more accurate than the well-known normalized linear algorithm. Then, we use the reciprocals of the standard deviations of the estimated relative depths from different images as the weights on the constraint equations of the similarity-invariant linear calibration algorithm, and propose a weighted similarity-invariant linear calibration algorithm with higher accuracy. Experimental results on synthetic data as well as on real image data show the effectiveness of our proposed algorithm.

Resumo Limpo

paper weight similarityinvari linear algorithm camera calibr rotat d object propos first propos new estim method comput relat depth free endpoint d object prove robust nois compar use previous literatur introduc estim invari imag similar transform result similarityinvari linear calibr algorithm slight accur wellknown normal linear algorithm use reciproc standard deviat estim relat depth differ imag weight constraint equat similarityinvari linear calibr algorithm propos weight similarityinvari linear calibr algorithm higher accuraci experiment result synthet data well real imag data show effect propos algorithm

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