Least-squares fitting for deformable superquadric model based on orthogonal distance

Authors:
Wen Furong;Yuan Baozong
Affiliations:
Institute of Information Science, Northern Jiaotong University, Beijing 100044, China;Institute of Information Science, Northern Jiaotong University, Beijing 100044, China
Venue:
Pattern Recognition Letters
Year:
2004

Citing 8
Cited 0

Perceptual organization and the representation of natural form

Artificial Intelligence
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Planar Curves, Surfaces, and Nonplanar Space Curves Defined by Implicit Equations with Applications to Edge and Range Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate orthogonal distance regression method for fitting quadric surfaces to range data

Pattern Recognition Letters
A parametric deformable model to fit unstructured 3D data

Computer Vision and Image Understanding
Direct Least Square Fitting of Ellipses

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Global and local deformations of solid primitives

SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques

Quantified Score

Hi-index	0.10

Visualization

Abstract

The least-squares fitting minimizes the squares sum of error-of-fit in predefined measures. By the geometric fitting, the error distances are defined as the orthogonal, or shortest distances from the given points to the geometric model to be fitted. Then the nonlinear optimization algorithm can be used to obtain the optimum solution. In this paper, we propose a geometric fitting algorithm for the deformable superquadric model, which is the computation of a measure of vector from each given point to orthogonal contacting point on the superquadric model, and estimates the optimum parameters of the model to minimize the squares sum of error distances. The estimated parameters by the proposed algorithm are invariant to coordinate transformation and we can easily find a physical interpretation of the fitting parameters.