Robust Estimation of Camera Parameters using Combinatorial Optimization

S. Rupp, M. Elter, and C. Winter (Germany)


Camera Calibration, Image Selection, Optimization, Monte Carlo Method, Heuristic and Genetic Algorithms.


The estimation of the parameters of the visual system is an indispensable step for augmented reality or image guided applications where quantitative information should be de rived from the images. Usually, the estimation process is called camera calibration and it is performed by observing a special calibration object from different directions. From these observations the image coordinates of the projected calibration marks are extracted and the mapping from the 3d world coordinates to the 2d image coordinates is cal culated. To attain a well-suited mapping, the calibration images must suffice certain constraints in order to ensure that the underlying mathmatical algorithms are well-posed. Thus, the choice of the input images influences the estima tion process and consequencely the quality of the derived information. In this paper we present a generic approach for camera calibration that is robust against ill-posed con figurations. For this, we apply combinatorial optimization technique in order to determine the optimal subset of the pool of aquired images yielding the best calibration result with respect to the model fit error. Our approach is generic in the sense that it is independent of a certain calibration algorithm because it only makes use of a quality measure that acts as an objective function for the optimization.

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