Visual Identification by Signature Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Measuring linearity of planar point sets
Pattern Recognition
Classification of silhouettes using contour fragments
Computer Vision and Image Understanding
Measuring linearity of open planar curve segments
Image and Vision Computing
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In this paper we define a new linearity measure for closed curves. We start with simple closed curves which represent the boundaries of bounded planar regions. It turns out that the method can be extended to closed curves which self-intersect and also to certain configurations consisting of several curves, including open curve segments. In all cases, the measured linearities range over the interval (0,1], and do not change under translation, rotation and scaling transformations of the considered curve. In addition, the highest possible linearity (which is 1) is reached if and only if the measured curve consists of two overlapping (i.e. coincident) straight line segments. The new linearity measure is theoretically well founded and all related statements are supported with rigorous mathematical proofs.