Search

Fundamental performance bounds for statistical estimation of parametric surfaces embedded in [R.sup.3], in which the reconstruction of the shape of binary or homogenous objects could be represented by asymptotic global confidence regions that facilitate geometric inference and optimization of the imaging system is derived. Simulation results reveal the tightness of the resulting bound and the efficiency of the three-dimensional global confidence region approach.

A joint hashing/watermarking scheme is developed in which a short hash of the host signal is available to a detector and potential applications include content tracking on public networks and forensic identification. The joint hashing/watermarking scheme outperforms the traditional hashless watermarking technique.

This paper derives fundamental performance bounds for statistical estimation of parametric surfaces embedded in R3. Unlike conventional pixel-based image reconstruction approaches, our problem is reconstruction of the shape of binary or homogeneous objects. The fundamental uncertainty of such estimation problems can be represented by global confidence regions, which facilitate geometric inference and optimization of the imaging system. Compared to our previous work on global confidence region analysis for curves [two-dimensional (2-D) shapes], computation of the probability that the entire surface estimate lies within the confidence region is more challenging because a surface estimate is an inhomogeneous random field continuously indexed by a 2-D variable. We derive an asymptotic lower bound to this probability by relating it to the exceedence probability of a higher dimensional Gaussian random field, which can, in turn, be evaluated using the tube formula due to Sun. Simulation results demonstrate the tightness of the resulting bound and the usefulness of the three-dimensional global confidence region approach. [ABSTRACT FROM AUTHOR]

We address the problem of computing fundamental performance bounds for estimation of object boundaries from noisy measurements in inverse problems, when the boundaries are parameterized by a finite number of unknown variables. Our model applies to multiple unknown objects, each with its own unknown gray level, or color, and boundary parameterization, on an arbitrary known background. While such fundamental bounds on the performance of shape estimation algorithms can in principle be derived from the Cramér-Rao lower bounds, very few results have been reported due to the difficulty of computing the derivatives of a functional with respect to shape deformation. In this paper, we provide a general formula for computing CramérRao lower bounds in inverse problems where the observations are related to the object by a general linear transform, followed by a possibly nonlinear and noisy measurement system. As an illustration, we derive explicit formulas for computed tomography, Fourier imaging, and deconvolution problems. The bounds reveal that highly accurate parametric reconstructions are possible in these examples, using severely limited and noisy data. [ABSTRACT FROM AUTHOR]

Evaluates the data-hiding capacity of image sources using an information-theoretic model. Construction of a hyper media document; Rejection of the host-signal interference at the decoder; Derivation of the fundamental limits on watermarking.

Presents an information-theoretic analysis of statistical dependencies between image wavelet coefficients. Formulation of the modeling problem in terms of mutual information; Illustration of concepts in the case of an AR-1 Gaussian image process; Information on reduced-order models.

Presents a study which examined an approach to image denoising based on complexity regularization. Overview of complexity-regularized denoising algorithms; Theoretical analysis; Experimental results.

Presents a systematic approach to forward-motion-compensated predictive video coding. Definition of a flexible model which represents motion fields; How inhomogeneity and spatial coherence properties of motion fields are captured.