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The Direct Linear Transform (DLT) is widely used in the calibration and reconstruction problem in computer vision. The calibration/reconstruction problem can be written as [Y] equals [X][B] + [e]. Where [Y] is a known vector, [X] is a known matrix, [B] is a vector on unknowns and [e] is a vector of unknown errors. In this paper we present methods for detecting outliers in the observations that compose our set of linear equations and apply them to experimental data. One set of methods uses the philosophy of 'identification', i.e., the outlying or influential cases are identified by determining the variation they have on the obtained results and points of high influence are removed from the data set. Another set of methods (the method of robust statistics) try to minimize the effects of errors that occur due to idealized assumptions in statistics; so robust methods permit the contamination of data without their removal and thus fall into the class of 'accommodation' methods. The methods of 'identification' and 'accommodation' were applied to experimentally observed data. Each method is compared for accuracy and it is found that these methods be chosen on the basis of computational considerations since their accuracies are compatible with each other.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. |
