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
A New One-Parametric Fitting Method for Planar Objects
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
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This paper is an extension of the already published paper Voss/Suesse [11]. In that paper we have developed a new region-based fitting method using the method of normalization. There we have 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 rectangles. 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