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Bayesian Segmentation Based Local Geometrically Invariant Image Watermarking

Wang X., Yang H., Wang J., Chen L., Niu P.

Fundamenta Informaticae

2013

Vol. 128, nr 4

475--501

Robust digital watermarking has been an active research topic in the last decade. As one of the promising approaches, feature point based image watermarking has attracted many researchers. However, the related work usually suffers from the following limitations: 1) The feature point detector is sensitive to texture region, and some noise feature points are always detected in the texture region. 2) The feature points focus too much on high contrast region, and the feature points are distributed unevenly. Based on Bayesian image segmentation, we propose a local geometrically invariant image watermarking scheme with good visual quality in this paper. Firstly, the Bayesian image segmentation is used to segment the host image into several homogeneous regions. Secondly, for each homogeneous region, image feature points are extracted using the multiscale Harris-Laplace detector, and the corresponding invariant local image regions are constructed adaptively. Finally, by taking the human visual system (HVS) into account, digital watermark is repeatedly embedded into local image regions by modulating the magnitudes of DFT coefficients. By binding the digital watermark with the invariant local image regions, the watermark detection can be done without synchronization error. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as sharpening, noise adding, and JPEG compression etc, but also robust against the geometric distortions.

Pairwise Markov random fields and segmentation of textured images

Pieczynski W., Tebbache A. N.

Machine Graphics and Vision

2000

Vol. 9, No. 3

705-718

The use of random fields, which allows one to take into account the spatial interaction among random variables in complex systems, becomes a frequent tool in numerous problems of statistical mechanics, spatial statistics, neural network modelling, and others. In particular, Markov random field based techniques can be of exceptional efficiency in some image processing problems, like segmentation or edge detection. In statistical image segmentation, that we address in this work, the model is generally defined by the propability distributions of the observations field conditional to the class field. Under some hypotheses, thr a posteriori distribution of the class field, i.e. conditional to the observations field, a still a Markov distribution and the latter property allows one to apply different bayesian methods of segmentation like Maximum a Posteriori (MAP) or Maximum a Posterior MOde (MPM). However, in such models the segmentation of textured images is difficult to perform and one has to resort to some model approximations. The originality of our contribution is to consider the markovianity of the couple (class field, observations field). We obtain a different model; in particular, the class field is not necessarily a Markov field. However, the posterior distribution of the class field is a Markov distribution, which makes possible bayesian MAP and MPM segmentations. Furthermore, the model proposed makes possible textured image segmentation with no approximations.