About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Pattern Recognition
Paper
Superellipse fitting to partial data
Abstract
Superellipses can be used to represent in a compact form a large variety of shapes, and are useful for modelling in the fields of computer graphics and computer vision. However, fitting them to data is di0cult and computationally expensive. Moreover, when only partial data is available the parameter estimates become unreliable. This paper attempts to improve the process of fitting to partial data by combining gradient and curvature information with the standard algebraic distance. Tests show that the addition of gradient information seems to enhance the robustness of fit and decrease the number of iterations needed. Curvature information appears To have only marginal effects. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.