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Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two different(More)
This paper proposes a joint maximum likelihood and Bayesian methodology for estimating Gaussian mixture models. In Bayesian inference, the distributions of parameters are modeled, characterized by hyperparameters. In the case of Gaussian mixtures, the distributions of parameters are considered as Gaussian for the mean, Wishart for the covariance, and(More)
A new methodology for fingerprinting and watermarking three-dimensional (3-D) graphical objects is proposed in this paper. The 3-D graphical objects are described by means of polygonal meshes. The information to be embedded is provided as a binary code. A watermarking methodology has two stages: embedding and detecting the information that has been embedded(More)
—This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation(More)
In this paper, we present a new blind and robust 3-D mesh watermarking scheme that makes use of the recently proposed manifold harmonics analysis. The mesh spectrum coefficient amplitudes obtained by using this analysis are quite robust against various attacks, including connectivity changes. A blind 16-bit watermark is embedded through an iterative scalar(More)
This paper introduces a new nonparametric estimation approach inspired from quantum mechanics. Kernel density estimation associates a function to each data sample. In classical kernel estimation theory the probability density function is calculated by summing up all the kernels. The proposed approach assumes that each data sample is associated with a(More)
Copyright protection of graphical objects and models is important for protecting author rights in animation, multimedia, computer-aided design (CAD), virtual reality, medical imaging, etc. In this paper we suggest a blind watermarking algorithm for 3D models and objects. A string of bits, generated according to a key, is embedded in the geometrical(More)