Results 1 - 10 of 4,630

A Signal Processing Approach To Fair Surface Design

by Gabriel Taubin , 1995
"... In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals -- functions defined on polyhedral surfaces of arbitrary topology --, we reduce the problem of surface smoothing, or fai ..."
Abstract - Cited by 654 (15 self) - Add to MetaCart
In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals -- functions defined on polyhedral surfaces of arbitrary topology --, we reduce the problem of surface smoothing

A computational approach to edge detection

by John Canny - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1986
"... This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumpti ..."
Abstract - Cited by 4675 (0 self) - Add to MetaCart
. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals

Context-Sensitive Interprocedural Points-to Analysis in the Presence of Function Pointers

by Maryam Emami, Rakesh Ghiya, Laurie J. Hendren , 1994
"... This paper reports on the design, implementation, and empirical results of a new method for dealing with the aliasing problem in C. The method is based on approximating the points-to relationships between accessible stack locations, and can be used to generate alias pairs, or used directly for other ..."
Abstract - Cited by 414 (23 self) - Add to MetaCart
for other analyses and transformations. Our method provides context-sensitive interprocedural information based on analysis over invocation graphs that capture all calling contexts including recursive and mutually-recursive calling contexts. Furthermore, the method allows the smooth integration for handling

Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time

by Daniel A. Spielman, Shang-hua Teng , 2003
"... We introduce the smoothed analysis of algorithms, which continuously interpolates between the worst-case and average-case analyses of algorithms. In smoothed analysis, we measure the maximum over inputs of the expected performance of an algorithm under small random perturbations of that input. We me ..."
Abstract - Cited by 202 (12 self) - Add to MetaCart
We introduce the smoothed analysis of algorithms, which continuously interpolates between the worst-case and average-case analyses of algorithms. In smoothed analysis, we measure the maximum over inputs of the expected performance of an algorithm under small random perturbations of that input. We

Noise power spectral density estimation based on optimal smoothing and minimum statistics

by Rainer Martin - IEEE TRANS. SPEECH AND AUDIO PROCESSING , 2001
"... We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algo-rithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a ..."
Abstract - Cited by 276 (7 self) - Add to MetaCart
smoothing of the power spectral density of the noisy speech signal. Based on the optimally smoothed power spec-tral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore

Implementing approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations: A manual for the inla-program

by Sara Martino, Nicolas Chopin , 2008
"... Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalised) linear models, (generalised) additive models, smoothing-spline models, state-space models, semiparametric regression, spatial and spatio-temp ..."
Abstract - Cited by 294 (20 self) - Add to MetaCart
Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalised) linear models, (generalised) additive models, smoothing-spline models, state-space models, semiparametric regression, spatial and spatio

On the Convergence of Pattern Search Algorithms

by Virginia Torczon
"... . We introduce an abstract definition of pattern search methods for solving nonlinear unconstrained optimization problems. Our definition unifies an important collection of optimization methods that neither computenor explicitly approximate derivatives. We exploit our characterization of pattern sea ..."
Abstract - Cited by 243 (13 self) - Add to MetaCart
search methods to establish a global convergence theory that does not enforce a notion of sufficient decrease. Our analysis is possible because the iterates of a pattern search method lie on a scaled, translated integer lattice. This allows us to relax the classical requirements on the acceptance

The Fourier-Series Method For Inverting Transforms Of Probability Distributions

by Joseph Abate, Ward Whitt , 1991
"... This paper reviews the Fourier-series method for calculating cumulative distribution functions (cdf's) and probability mass functions (pmf's) by numerically inverting characteristic functions, Laplace transforms and generating functions. Some variants of the Fourier-series method are remar ..."
Abstract - Cited by 211 (52 self) - Add to MetaCart
, the cdf can be perturbed slightly by convolution smoothing or windowing to produce a truncation error bound independent of the original cdf. Although error bounds can be determined, an effective approach is to use two different methods without elaborate error analysis. For this...

Vertex component analysis: A fast algorithm to unmix hyperspectral data

by José M. P. Nascimento, José M. Bioucas Dias - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2005
"... Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for ..."
Abstract - Cited by 193 (16 self) - Add to MetaCart
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method

Image Decomposition via the Combination of Sparse Representations and a Variational Approach

by J. -l. Starck, M. Elad, D.L. Donoho - IEEE Transactions on Image Processing , 2004
"... The separation of image content into semantic parts plays a vital role in applications such as compression, enhancement, restoration, and more. In recent years several pioneering works suggested such a separation based on variational formulation, and others using independent component analysis and s ..."
Abstract - Cited by 219 (28 self) - Add to MetaCart
and sparsity. This paper presents a novel method for separating images into texture and piecewise smooth (cartoon) parts, exploiting both the variational and the sparsity mechanisms. The method combines the Basis Pursuit Denoising (BPDN) algorithm and the Total-Variation (TV) regularization scheme. The basic
Results 1 - 10 of 4,630