The results from error propagation are used to complement each model parameter with a precision estimate, to assign an inlier set of data points supporting the fit to each extracted model, and to determine the a priori unknown total number of meaningful models in the data. Although the models are extracted sequentially, the final result is almost independent of the extraction order. This is achieved by further statistical processing which controls the mutual exchange of inlier data between the models. Consequently, sound data classification as well as robust fitting are guaranteed even in areas where different models intersect or touch each other. We demonstrate the algorithmic performance by fitting straight lines in 2D and planes in 3D with applications to problems of computer vision and pattern recognition.
Synthetic data is used to show the robustness and accuracy of the scheme.
Image data and range data are used to illustrate its applicability and relevance in respect of real-world problems, e.g., in the domain of image feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. Inc. All rights reserved.