Computational geometry: an introduction
Computational geometry: an introduction
Detection of generalized principal axes is rotationally symmetric shapes
Pattern Recognition
Machine vision
Digital approximation of moments of convex regions
Graphical Models and Image Processing
Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
Robot Vision
Robust normalization of silhouettes for recognition applications
Pattern Recognition Letters - Special issue: Discrete geometry for computer imagery (DGCI'2002)
A New Convexity Measure for Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Symbolic representation of two-dimensional shapes
Pattern Recognition Letters
CID: an efficient complexity-invariant distance for time series
Data Mining and Knowledge Discovery
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In this paper we consider some questions related to the orientation of shapes. We introduce as a new shape feature shape orientability, i.e. 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.