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The image sequences in many image processing and computer vision applications have problems associated with the accurate modeling and estimation of motion. The use of multiscale models is proposed as a first step towards representing, computing and encoding motion fields. A systematic approach to motion field estimation and coding is presented.

This tutorial paper reviews the theory and design of codes for hiding or embedding information in signals such as images, video, audio, graphics, and text. Such codes have also been called watermarking codes; they can be used in a variety of applications, including copyright protection for digital media, content authentication, media forensics, data binding, and covert communications. Some of these applications imply the presence of an adversary attempting to disrupt the transmission of information to the receiver; other applications involve a noisy, generally unknown, communication channel. Our focus is on the mathematical models, fundamental principles, and code design techniques that are applicable to data hiding. The approach draws from basic concepts in information theory, coding theory, game theory, and signal processing, and is illustrated with applications to the problem of hiding data in images. [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]

Details the derivation of information-theoretic bounds on the performance statistical object recognition systems. Geometric representation of probability sets and information distances; Performance bounds for tank imagery.

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.