The nature of statistical learning theory
The nature of statistical learning theory
Introduction to algorithms
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
A New Convexity Measure for Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine Invariant Pattern Recognition Using Multiscale Autoconvolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring Elongation from Shape Boundary
Journal of Mathematical Imaging and Vision
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A Hu moment invariant as a shape circularity measure
Pattern Recognition
Measuring Cubeness of 3D Shapes
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Shape elongation from optimal encasing rectangles
Computers & Mathematics with Applications
Measuring Squareness and Orientation of Shapes
Journal of Mathematical Imaging and Vision
Articulation-invariant representation of non-planar shapes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Tunable cubeness measures for 3D shapes
Pattern Recognition Letters
Measuring linearity of open planar curve segments
Image and Vision Computing
ADR shape descriptor - Distance between shape centroids versus shape diameter
Computer Vision and Image Understanding
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In this paper, we present a novel convexity measure for object shape analysis. The proposed method is based on the idea of generating pairs of points from a set and measuring the probability that a point dividing the corresponding line segments belongs to the same set. The measure is directly applicable to image functions representing shapes and also to gray-scale images which approximate image binarizations. The approach introduced gives rise to a variety of convexity measures which make it possible to obtain more information about the object shape. The proposed measure turns out to be easy to implement using the Fast Fourier Transform and we will consider this in detail. Finally, we illustrate the behavior of our measure in different situations and compare it to other similar ones.