Detection of generalized principal axes is rotationally symmetric shapes
Pattern Recognition
Generalized Affine Invariant Image Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust normalization of silhouettes for recognition applications
Pattern Recognition Letters - Special issue: Discrete geometry for computer imagery (DGCI'2002)
Notes on shape orientation where the standard method does not work
Pattern Recognition
Boundary based shape orientation
Pattern Recognition
An Alternative Approach to Computing Shape Orientation with an Application to Compound Shapes
International Journal of Computer Vision
A unifying approach to moment-based shape orientation and symmetry classification
IEEE Transactions on Image Processing
Affine-permutation invariance of 2-D shapes
IEEE Transactions on Image Processing
On the Orientability of Shapes
IEEE Transactions on Image Processing
Robust shape normalization of 3D articulated volumetric models
Computer-Aided Design
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Determining the orientation of a shape is a common task in many image processing applications. It is usually part of the image preprocessing stages and further processing may rely on an adequate method to determine the orientation. There are several methods for computing the orientation of a shape, each of them with its own strengths and weaknesses; a method which performs outstandingly for one application may have a poor performance for a different application. In this paper we present a new method for computing shape orientation based on the projection of the tangent vectors of a shape onto a line and weighting them using a function of the curvature. Some of the results from Zunic (2008) [14] are particular cases of the results presented here.