Boundary based shape orientation
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
Robust principal axes determination for point-based shapes using least median of squares
Computer-Aided Design
A Hu moment invariant as a shape circularity measure
Pattern Recognition
Measuring the orientability of shapes
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Curvature weighted gradient based shape orientation
Pattern Recognition
Shape elongation from optimal encasing rectangles
Computers & Mathematics with Applications
Measuring Squareness and Orientation of Shapes
Journal of Mathematical Imaging and Vision
Measuring linearity of open planar curve segments
Image and Vision Computing
Boundary based orientation of polygonal shapes
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
ADR shape descriptor - Distance between shape centroids versus shape diameter
Computer Vision and Image Understanding
Robust shape normalization of 3D articulated volumetric models
Computer-Aided Design
Silhouette-based human action recognition using SAX-Shapes
The Visual Computer: International Journal of Computer Graphics
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The orientation of a shape is a useful quantity, and has been shown to affect performance of object recognition in the human visual system. Shape orientation has also been used in computer vision to provide a properly oriented frame of reference, which can aid recognition. However, for certain shapes, the standard moment-based method of orientation estimation fails. We introduce as a new shape feature shape orientability, which defines the degree to which a shape has distinct (but not necessarily unique) orientation. A new method is described for measuring shape orientability, and has several desirable properties. In particular, unlike the standard moment-based measure of elongation, it is able to differentiate between the varying levels of orientability of n-fold rotationally symmetric shapes. Moreover, the new orientability measure is simple and efficient to compute (for an n-gon we describe an O(n) algorithm)