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Issue No.09 - September (2006 vol.28)
pp: 1501-1512
E. Rahtu , Dept. of Electr. & Inf. Eng., Oulu Univ.
ABSTRACT
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 would consider this in detail. Finally, we illustrate the behavior of our measure in different situations and compare it to other similar ones
INDEX TERMS
Shape measurement, Image analysis, Image segmentation, Area measurement, Particle measurements, Gray-scale, Fast Fourier transforms, Biology computing, Calibration, Image registration,affine invariance., Shape analysis, object classification
CITATION
E. Rahtu, M. Salo, J. Heikkila, "A new convexity measure based on a probabilistic interpretation of images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.28, no. 9, pp. 1501-1512, September 2006, doi:10.1109/TPAMI.2006.175
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