IEEE Transactions on Pattern Analysis and Machine Intelligence
A Data-Driven Intermediate Level Feature Extraction Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Localization Performance Measure and Optimal Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization for Scene Segmentation and Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure and Motion from Line Segments in Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vanishing Point Detection by Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ExSel++: A General Framework to Extract Parametric Models
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Viewpoint Estimation in Three-Dimensional Images Taken with Perspective Range Sensors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Method for Mining Regression Classes in Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
The ellipsoidal skeleton in medical applications
Proceedings of the sixth ACM symposium on Solid modeling and applications
A Graph Labelling Approach for Connected Feature Selection
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Adaptive Image Filtering and Segmentation Using Robust Estimation of Intensity
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Using symmetry in robust model fitting
Pattern Recognition Letters
Motion analysis of Omni-Directional video streams for a mobile sentry
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Arc-based evaluation and detection of ellipses
Pattern Recognition
Generalized least squares-based parametric motion estimation
Computer Vision and Image Understanding
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Parametric models play an important role in broad areas of science and technology. This paper presents a novel framework for the fitting of multiple parametric models. It comprises of a module for parameter estimation based on a solution for generalized least squares problems and of a procedure for error propagation, which takes both the geometric arrangement of the input data points and their precision into account. 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. Apart from the input data and its precision, the framework relies on only one additional control parameter: the confidence level on which the various statistical tests for data and model classification are carried out. 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.