We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist of three distinct components: geometry, parameter, and connectivity information. The latter two do not contribute to the reduction of error in a compression setting. Using semi-regular meshes, parameter and connectivity information can be virtually eliminated. Coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction we see improvements in error by a factor four (12dB) compared to other progressive coding schemes. CR Categories and Subject Descriptors: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling - hierarchy and geometric transformations; G.1.2 [Numerical Analysis]: Approximation - approximation of surfaces and contours, wavelets and fractals; I.4.2 [Image Processing and Computer Vision]: Compression (Coding) - Approximate methods Additional K...

We propose an efficient, hybrid Fourier-Wavelet Regularized Deconvolution (ForWaRD) al- gorithm that performs noise regularization via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage exploits the Fourier transform's sparse representation of the colored noise inherent in deconvolution, while the wavelet shrinkage exploits the wavelet do- main's sparse representation of piecewise smooth signals and images. We derive the optimal balance between the amount of Fourier and wavelet regularization by optimizing an approxi- mate mean-squared-error (MSE) metric and find that signals with sparser wavelet representa- tions require less Fourier shrinkage. ForWaRD is applicable to all ill-conditioned deconvolution problems, unlike the purely wavelet-based Wavelet- Vaguelette Deconvolution (WVD), and its es- timate features minimal ringing, unlike purely Fourier-based Wiener deconvolution. We analyze ForWaRD's MSE decay rate as the number of samples increases and demonstrate its improved performance compared to the optimal WVD over a wide range of practical sample-lengths.

In this paper, a new technique for off-line writer identification is presented, using connected-component contours (COCOCOs or CO³s) in upper-case handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the upper-case character set. Using a codebook of CO³s from an independent training set of 100 writers, the probability-density function (PDF) of CO³s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO³ PDF for identifying individual writers on the basis of a single sentence of upper-case characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO³ PDF with an independent edgebased orientation and curvature PDF yielded very high correct identification rates.

This paper proposes a new method for image compression. The method is based on the approximation of an image, regarded as a function, by a linear spline over an adapted triangulation, D(Y ), which is the Delaunay triangulation of a small set Y of significant pixels. The linear spline minimizes the distance to the image, measured by the mean square error, among all linear splines over D(Y ). The significant pixels in Y are selected by an adaptive thinning algorithm, which recursively removes less significant pixels in a greedy way, using a sophisticated criterion for measuring the significance of a pixel. The proposed compression method combines the approximation scheme with a customized scattered data coding scheme. We demonstrate that our compression method outperforms JPEG2000 on two geometric images and performs competitively with JPEG2000 on three popular test cases of real images.

Within distributed computing environments, access to very large geospatial datasets often suffers from slow or unreliable network connections. To allow users to start working with a partially delivered dataset, progressive transmission methods are a viable solution. While incremental and progressive methods have been applied successfully to the transmission of raster images over the World Wide Web, and, in the form of prototypes, of triangular meshes, the transmission of vector map datasets has lacked a similar attention. This paper introduces a solution to the progressive transmission of vector map data that allows users to apply analytical GIS methods to partially transmitted data sets. The architecture follows a client-server model with multiple map representations at the server side, and a thin client that compiles transmitted increments into a topologically consistent format. This paper describes the concepts, develops an architecture, and discusses implementation concerns.

In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for constrained local minima (CLM dn ) in the theory of discrete constrained optimization using Lagrange multipliers developed in our group. The theory proves the equivalence between the set of discrete saddle points and the set of CLM dn , leading to the first-order necessary and sufficient condition for CLM dn .

Summary. Adaptive thinning algorithms are greedy point removal schemes for bivariate scattered data sets with corresponding function values, where the points are recursively removed according to some data-dependent criterion. Each subset of points, together with its function values, defines a linear spline over its Delaunay triangulation. The basic criterion for the removal of the next point is to minimize the error between the resulting linear spline at the bivariate data points and the original function values. This leads to a hierarchy of linear splines of coarser and coarser resolutions. This paper surveys the various removal strategies developed in our earlier papers, and the application of adaptive thinning to terrain modelling and to image compression. In our image test examples, we found that our thinning scheme, adapted to diminish the least squares error, combined with a postprocessing least squares optimization and a customized coding scheme, often gives better or comparable results to the wavelet-based scheme SPIHT. 1

This paper is concerning digital image compression by using adaptive thinning algorithms. Adaptive thinning is a recursive point removal scheme, which works with decremental Delaunay triangulations.

A novel method is proposed for post-processing of JPEGencoded images, in order to reduce coding artifacts and enhance visual quality. Our method simply re-applies JPEG to the shifted versions of the already-compressed image, and forms an average. This approach, despite its simplicity, offers better performance than other known methods, including those based on nonlinear filtering, POCS, and redundant wavelets. INTRODUCTION It is well-known that JPEG-encoded images exhibit visually unpleasant blocking artifacts. Much work has focused on reducing or eliminating the problem. The earliest attempts in this direction involved space-invariant [1] and space-varying filters [2]. More interesting (and effective) methods emerged later. Among these second-generation methods was projection on convex sets (POCS) [3], which was later refined and improved in [4]. Chou et al. proposed another simple but effective nonlinear filtering approach based on continuity on the edges of the JPEG blocks [5]. Amo...

Wavelet-based image coders like the JPEG2000 standard are the state of the art in image compression. Unlike traditional image coders, however, their performance depends to a large degree on the choice of a good wavelet. Most wavelet-based image coders use standard wavelets that are known to perform well on photographic images. However, these wavelets do not perform as well on other common image classes, like scanned documents or fingerprints. In this paper, a method based on the coevolutionary genetic algorithm introduced in [11] is used to evolve specialized wavelets for fingerprint images. These wavelets are compared to the hand-designed wavelet currently used by the FBI to compress fingerprints. The results show that the evolved wavelets consistently outperform the hand-designed wavelet. Using evolution to adapt wavelets to classes of images can therefore significantly increase the quality of compressed images.