Image histogram thresholding using Gaussian kernel density estimation (English)
Çetin, A. Enis
2013 21st Signal Processing and Communications Applications Conference (SIU)
Item Usage Stats
In this article, image histogram thresholding is carried out using the likelihood of a mixture of Gaussians. In the proposed approach, a prob ability density function (PDF) of the histogram is computed using Gaussian kernel density estimation in an iterative manner. The threshold is found by iteratively computing a mixture of Gaussians for the two clusters. This process is aborted when the current bin is assigned to a different cluster than its predecessor. The method does not envolve an exhaustive search. Visual examples of our segmentation versus Otsu's thresholding method are presented. © 2013 IEEE.
Mixture of Gaussians
Published Version (Please cite this version)http://dx.doi.org/10.1109/SIU.2013.6531341
Showing items related by title, author, creator and subject.
Turgut, Esma; Gezici, Sinan (IEEE, 2013)In this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of ...
Ali, Syed Amjad; Ince, E.A. (IEEE, 2007)The statistical characteristics of impulsive noise differ greatly from those of Gaussian noise. Hence, the performance of conventional decoders, optimized for additive white Gaussian noise (AWGN) channels is not promising ...
Alp, Y. K.; Arıkan, Orhan (Elsevier, 2012-05-18)Since Hermite-Gaussian (HG) functions provide an orthonormal basis with the most compact time-frequency supports (TFSs), they are ideally suited for time-frequency component analysis of finite energy signals. For a signal ...