Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Fitting affine invariant conics to curves
Geometric invariance in computer vision
Geometric invariants and object recognition
International Journal of Computer Vision
A note on the least squares fitting of ellipses
Pattern Recognition Letters
The Method of Normalization to Determine Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Fitting of Planar Objects by Primitives
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Squares Fitting of Ellipses
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Orthogonal Distance Fitting of Implicit Curves and Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Framework of Invariant Fitting
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Robust fitting of 3D objects by affinely transformed superellipsoids using normalization
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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This paper is an extension of the already published paper by Voss and Suesse [11]. In that paper, wedeveloped a new region-based fitting method using the method of normalization. There we demonstrated the zero-parametric fitting of lines, triangles, parallelograms, circles, and ellipses. In the present paper, we discuss this normalization idea for fitting of closed regions using circular segments, elliptical segments, and super-ellipses. As features, we use the area-based low order moments. We show that we have to solve only one-dimensional optimization problems in these cases.