finite element methods for active contour models and balloons for 2 d and 3 d images the use of energy minimizing curves known as snakes to extract features of interest in images has been introduced by kass witkin and terzopoulos 1987 a balloon model was introduced by cohen 1991 as a way to generalize and solve some of the problems encountered with the original method a 3 d generalization of the balloon model as a 3 d deformable surface which evolves in 3 d images is presented it is deformed under the action of internal and external forces attracting the surface toward detected edgels by means of an attraction potential we also show properties of energy minimizing surfaces concerning their relationship with 3 d edge points to solve the minimization problem for a surface two simplified approaches are shown first defining a 3 d surface as a series of 2 d planar curves then after comparing finite element method and finite difference method in the 2 d problem we solve the 3 d model using the finite element method yielding greater stability and faster convergence this model is applied for segmenting magnetic resonance images

bayesian image restoration an application to edge preserving surface recovery bayesian methods for recovering a 2 d surface are discussed it is assumed that there is a textural image that can be modeled by a markov random field and that the original surface is composed of different surfaces each of which is associated with one textural state both parametric and nonparametric methods are used to enforce smoothness of these surfaces iterative procedures are examined for simultaneous restoration of the textural image and estimation of underlying parameters from the estimated textural image and the estimated parameters an estimate for the original surface is obtained two illustrative examples are presented

ideal spatial adaptation by wavelet shrinkage summary with ideal spatial adaptation an oracle furnishes information about how best to adapt a spatially variable estimator whether piecewise constant piecewise polynomial variable knot spline or variable bandwidth kernel to the unknown function estimation with the aid of an oracle offers dramatic advantages over traditional linear estimation by nonadaptive kernels however it is a priori unclear whether such performance can be obtained by a procedure relying on the data alone we describe a new principle for spatially adaptive estimation selective wavelet reconstruction we show that variable knot spline fits and piecewise polynomial fits when equipped with an oracle to select the knots are not dramatically more powerful than selective wavelet reconstruction with an oracle we develop a practical spatially adaptive method riskshrink which works by shrinkage of empirical wavelet coefficients riskshrink mimics the performance of an oracle for selective wavelet reconstruction as well as it is possible to do so a new inequality in multivariate normal decision theory which we call the oracle inequality shows that attained performance differs from ideal performance by at most a factor of approximately 2 log n where n is the sample size moreover no estimator can give a better guarantee than this within the class of spatially adaptive procedures riskshrink is essentially optimal relying only on the data it comes within a factor log 2 n of the performance of piecewise polynomial and variableknot spline methods equipped with an oracle in contrast it is unknown how or if piecewise polynomial methods could be made to function this well when denied access to an oracle and forced to rely on data alone

sound propagation over noise barriers with absorbing ground abstract the main purpose of this article is to present a numerical method for calculating the sound pressure around noise barriers of arbitrary geometry varying impedance boundary conditions on the barrier and constant impedance on the ground are assumed sound propagation over an infinite barrier with a constant cross section for an harmonic point source is determined by solving 2d problems only avoiding the computational complexity of the solution of a true 3d problem this can be done by using the solutions of a set of 2d models for a coherent line source for real andimaginarywavenumbers this extension to calculations forimaginarywavenumbers is the main feature of the proposed method a fourier transform of the 2d solutions allows a calculation of the 3d solution for a point source the 2d numerical solutions are obtained by a classical use of the boundary element method moreover the 2d green function forimaginarywavenumbers in the case of a half space bounded by a surface of uniform impedance is developed finally examples are given to estimate the accuracy of the method and some practical cases of calculations around barriers are presented and compared with experimental data

weight adjustment rule of neural networks for computing discrete 2 d gabor transforms image processing it is demonstrated that the weight adjustment rule used in the neural network for computing the 2 d gabor transform proposed by j daugman 1988 can be shown to be equivalent to the jacobi iteration scheme for solving simultaneous linear equations it is shown that faster convergence of the algorithm can be achieved by using gauss seidel iteration successive overrelaxation conjugate gradient algorithms and multigrid methods

multigrid tomographic inversion with variable resolution data and image spaces a multigrid inversion approach that uses variable resolutions of both the data space and the image space is proposed since the computational complexity of inverse problems typically increases with a larger number of unknown image pixels and a larger number of measurements the proposed algorithm further reduces the computation relative to conventional multigrid approaches which change only the image space resolution at coarse scales the advantage is particularly important for data rich applications where data resolutions may differ for different scales applications of the approach to bayesian reconstruction algorithms in transmission and emission tomography with a generalized gaussian markov random field image prior are presented both with a poisson noise model and with a quadratic data term simulation results indicate that the proposed multigrid approach results in significant improvement in convergence speed compared to the fixed grid iterative coordinate descent method and a multigrid method with fixed data resolution

two denoising methods by wavelet transform to wavelet based noise reduction methods are discussed first we improve the traditional spatially selective noise filtration technique proposed by xu et al 1994 second we introduce a new threshold based denoising algorithm based on undecimated discrete wavelet transform simulations and comparisons are given

pointwise shape adaptive dct for high quality denoising and deblocking of grayscale and color images the shape adaptive discrete cosine transform sa dct transform can be computed on a support of arbitrary shape but retains a computational complexity comparable to that of the usual separable block dct b dct despite the near optimal decorrelation and energy compaction properties application of the sa dct has been rather limited targeted nearly exclusively to video compression in this paper we present a novel approach to image filtering based on the sa dct we use the sa dct in conjunction with the anisotropic local polynomial approximation intersection of confidence intervals technique which defines the shape of the transform s support in a pointwise adaptive manner the thresholded or attenuated sa dct coefficients are used to reconstruct a local estimate of the signal within the adaptive shape support since supports corresponding to different points are in general overlapping the local estimates are averaged together using adaptive weights that depend on the region s statistics this approach can be used for various image processing tasks in this paper we consider in particular image denoising and image deblocking and deringing from block dct compression a special structural constraint in luminance chrominance space is also proposed to enable an accurate filtering of color images simulation experiments show a state of the art quality of the final estimate both in terms of objective criteria and visual appearance thanks to the adaptive support reconstructed edges are clean and no unpleasant ringing artifacts are introduced by the fitted transform

stereoscopic ranging by matching image modulations we apply an am fm surface albedo model to analyze the projection of surface patterns viewed through a binocular camera system this is used to support the use of modulation based stereo matching where local image phase is used to compute stereo disparities the local image phase is an advantageous feature for image matching since the problem of computing disparities reduces to identifying local phase shifts between the stereoscopic image data local phase shifts however are problematic at high frequencies due to phase wrapping when disparities exceed spl plusmn spl pi we meld powerful multichannel gabor image demodulation techniques for multiscale coarse to fine computation of local image phase with a disparity channel model for depth computation the resulting framework unifies phase based matching approaches with am fm surface image models we demonstrate the concepts in a stereo algorithm that generates a dense accurate disparity map without the problems associated with phase wrapping

comutations underlying the measuremnt of visual motion abstract the organization of movement in a changing image provides a valuable source of information for analyzing the environment in terms of objects their motion in space and their three dimensional structure a description of this movement is not provided to our visual system directly however it must be inferred from the pattern of changing intensity that reaches the eye this paper examines the problem of motion measurement which we formulate as the computation of an instantaneous two dimensional velocity field initial motion measurements occur at the location of significant intensity changes as suggested by marr and ullman 1 these measurements provide only one component of velocity and must be integrated to compute the two dimensional velocity field a fundamental problem for this integration is that motion is not determined uniquely from the changing image an additional constraint of smoothness of the velocity field is formulated based on the physical assumption that surfaces are generally smooth allowing the computation of a unique solution a theoretical analysis of the conditions under which this computation yields the correct velocity field suggests that the solution is physically plausible empirical studies show the results to be consistent with human motion perception

a fast scalable algorithm for discontinuous optical flow estimation multiple moving objects partially occluded objects or even a single object moving against the background gives rise to discontinuities in the optical flow field in corresponding image sequences while uniform global regularization based moderately fast techniques cannot provide accurate estimates of the discontinuous flow field statistical optimization based accurate techniques suffer from excessive solution time a weighted anisotropic smoothness based numerically robust algorithm is proposed that can generate discontinuous optical flow field with high speed and linear computational complexity weighted sum of the first order spatial derivatives of the flow field is used for regularization less regularization is performed where strong gradient information is available the flow field at any point is interpolated more from those at neighboring points along the weaker intensity gradient component such intensity gradient weighted regularization leads to euler lagrange equations with strong anisotropies coupled with discontinuities in their coefficients a robust multilevel iterative technique that recursively generates coarse level problems based on intensity gradient weighted smoothing weights is employed to estimate discontinuous optical flow field experimental results are presented to demonstrate the efficacy of the proposed technique

fuzzy peer groups for reducing mixed gaussian impulse noise from color images the peer group of an image pixel is a pixel similarity based concept which has been successfully used to devise image denoising methods however since it is difficult to define the pixel similarity in a crisp way we propose to represent this similarity in fuzzy terms in this paper we introduce the fuzzy peer group concept which extends the peer group concept in the fuzzy setting a fuzzy peer group will be defined as a fuzzy set that takes a peer group as support set and where the membership degree of each peer group member will be given by its fuzzy similarity with respect to the pixel under processing the fuzzy peer group of each image pixel will be determined by means of a novel fuzzy logic based procedure we use the fuzzy peer group concept to design a two step color image filter cascading a fuzzy rule based switching impulse noise filter by a fuzzy average filtering over the fuzzy peer group both steps use the same fuzzy peer group which leads to computational savings the proposed filter is able to efficiently suppress both gaussian noise and impulse noise as well as mixed gaussian impulse noise experimental results are provided to show that the proposed filter achieves a promising performance

a general framework for nonlinear multigrid inversion a variety of new imaging modalities such as optical diffusion tomography require the inversion of a forward problem that is modeled by the solution to a three dimensional 3d partial differential equation for these applications image reconstruction is particularly difficult because the forward problem is both nonlinear and computationally expensive to evaluate we propose a general framework for nonlinear multigrid inversion that is applicable to a wide variety of inverse problems the multigrid inversion algorithm results from the application of recursive multigrid techniques to the solution of optimization problems arising from inverse problems the method works by dynamically adjusting the cost functionals at different scales so that they are consistent with and ultimately reduce the finest scale cost functional in this way the multigrid inversion algorithm efficiently computes the solution to the desired fine scale inversion problem importantly the new algorithm can greatly reduce computation because both the forward and inverse problems are more coarsely discretized at lower resolutions an application of our method to bayesian optical diffusion tomography with a generalized gaussian markov random field image prior model shows the potential for very large computational savings numerical data also indicates robust convergence with a range of initialization conditions for this nonconvex optimization problem

ranging through gabor logons a consistent hierarchical approach in this work the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors inspired by biological evidence these feature vectors are generated by a correlation between a bank of gabor sensors and the intensity image the sensors consist of two dimensional gabor filters at various scales spatial frequencies and orientations which bear close resemblance to the receptive field profiles of simple v1 cells in visual cortex a hierarchical stochastic relaxation method is then used to obtain the dense stereo disparities unlike traditional hierarchical methods for stereo feature based hierarchical processing yields consistent disparities to avoid false matchings due to static occlusion a dual matching based on the imaging geometry is used

wavelet thresholding of multivalued images in this paper a denoising technique for multivalued images exploiting interband correlations is proposed a redundant wavelet transform is applied and denoising is applied by thresholding wavelet coefficients specific functions of the wavelet coefficients are defined that exploit interscale and or interband correlation of the signal three functions are studied the square of the wavelet coefficients products of coefficients at adjacent scales and products of coefficients from different bands for these functions the signal and noise probability density functions pdf become more separated the high signal correlation between bands is exploited by summing these products over all bands in this way separating noise and signal pdfs even more the noise pdf of the proposed quantities is derived analytically and from this a wavelet threshold is derived the technique is demonstrated to outperform single band wavelet thresholding on multispectral remote sensing images and on multimodal mri images

a nonlinear stein based estimator for multichannel image denoising the use of multicomponent images has become widespread with the improvement of multisensor systems having increased spatial and spectral resolutions however the observed images are often corrupted by an additive gaussian noise in this paper we are interested in multichannel image denoising based on a multiscale representation of the images a multivariate statistical approach is adopted to take into account both the spatial and the intercomponent correlations existing between the different wavelet subbands more precisely we propose a new parametric nonlinear estimator which generalizes many reported denoising methods the derivation of the optimal parameters is achieved by applying stein s principle in the multivariate case experiments performed on multispectral remote sensing images clearly indicate that our method outperforms conventional wavelet denoising techniques

wavelet denoising of multicomponent images using gaussian scale mixture models and a noise free image as priors in this paper a bayesian wavelet based denoising procedure for multicomponent images is proposed a denoising procedure is constructed that 1 fully accounts for the multicomponent image covariances 2 makes use of gaussian scale mixtures as prior models that approximate the marginal distributions of the wavelet coefficients well and 3 makes use of a noise free image as extra prior information it is shown that such prior information is available with specific multicomponent image data of e g remote sensing and biomedical imaging experiments are conducted in these two domains in both simulated and real noisy conditions

fuzzy cognitive maps for stereovision matching this paper outlines a method for solving the stereovision matching problem using edge segments as the primitives in stereovision matching the following constraints are commonly used epipolar similarity smoothness ordering and uniqueness we propose a new matching strategy under a fuzzy context in which such constraints are mapped the fuzzy context integrates both fuzzy clustering and fuzzy cognitive maps with such purpose a network of concepts nodes is designed each concept represents a pair of primitives to be matched each concept has associated a fuzzy value which determines the degree of the correspondence the goal is to achieve high performance in terms of correct matches the main findings of this paper are reflected in the use of the fuzzy context that allows building the network of concepts where the matching constraints are mapped initially each concept value is loaded via the fuzzy clustering and then updated by the fuzzy cognitive maps framework this updating is achieved through the influence of the remainder neighboring concepts until a good global matching solution is achieved under this fuzzy approach we gain quantitative and qualitative matching correspondences this method works as a relaxation matching approach and its performance is illustrated by comparative analysis against some existing global matching methods

multiresolution 3 d range segmentation using focus cues this paper describes a novel system for computing a three dimensional 3 d range segmentation of an arbitrary visible scene using focus information the process of range segmentation is divided into three steps an initial range classification a surface merging process and a 3 d multiresolution range segmentation first range classification is performed to obtain quantized range estimates the range classification is performed by analyzing focus cues within a bayesian estimation framework a combined energy functional measures the degree of focus and the gibbs distribution of the class field the range classification provides an initial range segmentation second a statistical merging process is performed to merge the initial surface segments this gives a range segmentation at a coarse resolution third 3 d multiresolution range segmentation 3 d mrs is performed to refine the range segmentation into finer resolutions the proposed range segmentation method does not require initial depth estimates it allows the analysis of scenes containing multiple objects and it provides a rich description of the 3 d structure of a scene

signal matching through scale space given a collection of similar signals that have been deformed with respect to each other the general signal matching problem is to recover the deformation we formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term the minimization reduces to a dynamic system governed by a set of coupled first order differential equations the dynamic system finds an optimal solution at a coarse scale and then tracks it continuously to a fine scale among the major themes in recent work on visual signal matching have been the notions of matching as constrained optimization of variational surface reconstruction and of coarse to fine matching our solution captures these in a precise succinct and unified form results are presented for one dimensional signals a motion sequence and a stereo pair

dense features for semi dense stereo correspondence we present a new feature based algorithm for stereo correspondence most of the previous feature based methods match sparse features like edge pixels producing only sparse disparity maps our algorithm detects and matches dense features between the left and right images of a stereo pair producing a semi dense disparity map our dense feature is defined with respect to both images of a stereo pair and it is computed during the stereo matching process not a preprocessing step in essence a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error which is the difference in intensities between corresponding boundary pixels our algorithm produces accurate semi dense disparity maps leaving featureless regions in the scene unmatched it is robust requires little parameter tuning can handle brightness differences between images nonlinear errors and is fast linear complexity

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

image analysis using multigrid relaxation methods image analysis problems posed mathematically as variational principles or as partial differential equations are amenable to numerical solution by relaxation algorithms that are local iterative and often parallel although they are well suited structurally for implementation on massively parallel locally interconnected computational architectures such distributed algorithms are seriously handi capped by an inherent inefficiency at propagating constraints between widely separated processing elements hence they converge extremely slowly when confronted by the large representations of early vision application of multigrid methods can overcome this drawback as we showed in previous work on 3 d surface reconstruction in this paper we develop multiresolution iterative algorithms for computing lightness shape from shading and optical flow and we examine the efficiency of these algorithms using synthetic image inputs the multigrid methodology that we describe is broadly applicable in early vision notably it is an appealing strategy to use in conjunction with regularization analysis for the efficient solution of a wide range of ill posed image analysis problems

information theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients this paper presents an information theoretic analysis of statistical dependencies between image wavelet coefficients the dependencies are measured using mutual information which has a fundamental relationship to data compression estimation and classification performance mutual information is computed analytically for several statistical image models and depends strongly on the choice of wavelet filters in the absence of an explicit statistical model a method is studied for reliably estimating mutual information from image data the validity of the model based and data driven approaches is assessed on representative real world photographic images our results are consistent with empirical observations that coding schemes exploiting inter and intrascale dependencies alone perform very well whereas taking both into account does not significantly improve coding performance a similar observation applies to other image processing applications

a stereo vision technique using curve segments and relaxation matching a multichannel feature based stereo vision technique where curve segments are used as feature primitives in the matching process is described the left image and the right image are filtered by using several laplacian of gaussian operators of different widths channels curve segments are extracted by a tracking algorithm and their centroids are obtained at each channel the generalized hough transform of each curve segment in the left and the right image is evaluated the epipolar constraint on the centroids of the curve segment and the channel size is used to limit the searching space in the right image to resolve the ambiguity of the false targets multiple matches a relaxation technique is used where the initial scores of the node assignments are updated by the compatibility measures between the centroids of the curve segments the node assignments with the highest score are chosen as the matching curve segments

a self adaptive co ordinate transformation for efficient numerical evaluation of general boundary element integrals almost all general purpose boundary element computer packages include a curved geometry modelling capability thus numerical quadrature schemes play an important role in the efficiency of programming the technique the present work discusses this problem in detail and introduces efficient means of computing singular or nearly singular integrals currently found in two dimensional axisymmetric and three dimensional applications emphasis is given to a new third degree polynomial transformation which was found greatly to improve the accuracy of gaussian quadrature scheme s within the near singularity range the procedure can easily be implemented into existing be codes and presents the important feature of being self adaptive i e it produces a variable lumping of the gauss stations toward the singularity depending on the minimum distance from the source point to the element the self adaptiveness of the scheme also makes it inactive when not useful large source distances which makes it very safe for general usage

regularization of inverse visual problems involving discontinuities inverse problems such as the reconstruction problems that arise in early vision tend to be mathematically ill posed through regularization they may be reformulated as well posed variational principles whose solutions are computable standard regularization theory employs quadratic stabilizing functionals that impose global smoothness constraints on possible solutions discontinuities present serious difficulties to standard regularization however since their reconstruction requires a precise spatial control over the smoothing properties of stabilizers this paper proposes a general class of controlled continuity stabilizers which provide the necessary control over smoothness these nonquadratic stabilizing functionals comprise multiple generalized spline kernels combined with noncontinuous continuity control functions in the context of computational vision they may be thought of as controlled continuity constraints these generic constraints are applicable to visual reconstruction problems that involve both continuous regions and discontinuities for which global smoothness constraints fail

on learning conditional random fields for stereo until recently the lack of ground truth data has hindered the application of discriminative structured prediction techniques to the stereo problem in this paper we use ground truth data sets that we have recently constructed to explore different model structures and parameter learning techniques to estimate parameters in markov random fields mrfs via maximum likelihood one usually needs to perform approximate probabilistic inference conditional random fields crfs are discriminative versions of traditional mrfs we explore a number of novel crf model structures including a crf for stereo matching with an explicit occlusion model crfs require expensive inference steps for each iteration of optimization and inference is particularly slow when there are many discrete states we explore belief propagation variational message passing and graph cuts as inference methods during learning and compare with learning via pseudolikelihood to accelerate approximate inference we have developed a new method called sparse variational message passing which can reduce inference time by an order of magnitude with negligible loss in quality learning using sparse variational message passing improves upon previous approaches using graph cuts and allows efficient learning over large data sets when energy functions violate the constraints imposed by graph cuts

robust voxel similarity metrics for the registration of dissimilar single and multimodal images in this paper we develop data driven registration algorithms relying on pixel similarity metrics that enable an accurate subpixel rigid registration of dissimilar single or multimodal 2d 3d images gross dissimilarities are handled by considering similarity measures related to robust m estimators in particular a novel robust similarity metric is proposed for comparing multimodal images the proposed robust similarity metrics are compared to the most popular standard similarity metrics on synthetic as well as on real world image pairs showing gross dissimilarities three case studies are considered the registration of single modal and multimodal 3d medical images the matching of multispectral remotely sensed images and the registration of intensity and range images the proposed robust similarity measures compare favourably with the standard non robust techniques

wavelet transform domain filters a spatially selective noise filtration technique wavelet transforms are multiresolution decompositions that can be used to analyze signals and images they describe a signal by the power at each scale and position edges can be located very effectively in the wavelet transform domain a spatially selective noise filtration technique based on the direct spatial correlation of the wavelet transform at several adjacent scales is introduced a high correlation is used to infer that there is a significant feature at the position that should be passed through the filter the authors have tested the technique on simulated signals phantom images and real mr images it is found that the technique can reduce noise contents in signals and images by more than 80 while maintaining at least 80 of the value of the gradient at most edges the authors did not observe any gibbs ringing or significant resolution loss on the filtered images artifacts that arose from the filtration are very small and local the noise filtration technique is quite robust there are many possible extensions of the technique the authors see its applications in spatially dependent noise filtration edge detection and enhancement image restoration and motion artifact removal they have compared the performance of the technique to that of the weiner filter and found it to be superior

invariant surface segmentation through energy minimization with discontinuities the computational problems in segmenting range data into surface patches based on the invariant surface properties i e mean curvature h and gaussian curvature k are investigated the goal is to obtain reliable hk surface maps two commonly encountered problems are firstly the noise effect in computing derivative estimates and secondly the smoothing across discontinuities here the segmentation is formulated as finding minimization solutions of energy functionals involving discontinuities a two stage approach to the goal is presented stage 1 from a range image to curvature images and stage 2 from the curvature images to the hk maps in both stages solutions are found through minimizing energy functionals that measure the degree of bias of a solution from two constraints the closeness of the solution to the data and the smoothness of the solution controlled by predetermined discontinuities propagation across discontinuities is prevented during minimization which preserves the original surface shapes experimental results are given for a variety of test images

stereo matching with nonlinear diffusion one of the central problems in stereo matching and other image registration tasks is the selection of optimal window sizes for comparing image regions this paper addresses this problem with some novel algorithms based on iteratively diffusing support at different disparity hypotheses and locally controlling the amount of diffusion based on the current quality of the disparity estimate it also develops a novel bayesian estimation technique which significantly outperforms techniques based on area based matching ssd and regular diffusion we provide experimental results on both synthetic and real stereo image pairs

on the foundations of relaxation labeling processes a large class of problems can be formulated in terms of the assignment of labels to objects frequently processes are needed which reduce ambiguity and noise and select the best label among several possible choices relaxation labeling processes are just such a class of algorithms they are based on the parallel use of local constraints between labels this paper develops a theory to characterize the goal of relaxation labeling the theory is founded on a definition of con sistency in labelings extending the notion of constraint satisfaction in certain restricted circumstances an explicit functional exists that can be maximized to guide the search for consistent labelings this functional is used to derive a new relaxation labeling operator when the restrictions are not satisfied the theory relies on variational cal culus it is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality a procedure nearly identical to the relaxation operator derived under restricted circum stances serves in the more general setting further a local convergence result is established for this operator the standard relaxation labeling formulas are shown to approximate our new operator which leads us to conjecture that successful applications of the standard methods are explainable by the theory developed here observations about con vergence and generalizations to higher order compatibility relations are described

multispectral image denoising with optimized vector bilateral filter vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral hyperspectral image denoising it has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios snrs typical vector bilateral filtering described in the literature does not use parameters satisfying optimality criteria we introduce an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means the approach is based on posing the filtering problem as one of nonlinear estimation and minimization of the stein s unbiased risk estimate of this nonlinear estimator along the way we provide a plausibility argument through an analytical example as to why vector bilateral filtering outperforms band wise 2d bilateral filtering in enhancing snr experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared with several other approaches

map based stochastic diffusion for stereo matching and line fields estimation this paper proposes a stochastic approach to estimate the disparity field combined with line field in the maximum a posteriori map method based on markov random field mrf model it is important to optimize and converge the gibbs potential function corresponding to the perturbed disparity field the proposed optimization method stochastic diffusion takes advantage of the probabilistic distribution of the neighborhood fields to diffuse the gibbs potential space iteratively by using the neighborhood distribution in the non random and non deterministic diffusion both the estimation accuracy and the convergence speed are improved in the paper the hierarchical stochastic diffusion is also applied to the disparity field the hierarchical approach reduces the memory and computational load and increases the convergence speed of the potential space the paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure according to the experiments the stochastic diffusion shows good estimation performance the line field improves the estimation at the object boundary and coincides with the object boundary with the useful contours the stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and mrf models

spatially adaptive wavelet thresholding with context modeling for image denoising the method of wavelet thresholding for removing noise or denoising has been researched extensively due to its effectiveness and simplicity much of the literature has focused on developing the best uniform threshold or best basis selection however not much has been done to make the threshold values adaptive to the spatially changing statistics of images such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image such as the identification of smooth or edge regions to be incorporated into the algorithm this work proposes a spatially adaptive wavelet thresholding method based on context modeling a common technique used in image compression to adapt the coder to changing image characteristics each wavelet coefficient is modeled as a random variable of a generalized gaussian distribution with an unknown parameter context modeling is used to estimate the parameter for each coefficient which is then used to adapt the thresholding strategy this spatially adaptive thresholding is extended to the overcomplete wavelet expansion which yields better results than the orthogonal transform experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower mse than the best uniform thresholding with the original image assumed known

restriction of a markov random field on a graph and multiresolution statistical image modeling the association of statistical models and multiresolution data analysis in a consistent and tractable mathematical framework remains an intricate theoretical and practical issue several consistent approaches have been proposed previously to combine markov random field mrf models and multiresolution algorithms in image analysis renormalization group subsampling of stochastic processes mrfs defined on trees or pyramids etc for the simulation or a practical use of these models in statistical estimation an important issue is the preservation of the local markovian property of the representation at the different resolution levels it is shown that this key problem may be studied by considering the restriction of a markov random field defined on some simple finite nondirected graph to a part of its original site set several general properties of the restricted field are derived the general form of the distribution of the restriction is given locality of the field is studied by exhibiting a neighborhood structure with respect to which the restricted field is an mrf sufficient conditions for the new neighborhood structure to be minimal are derived several consequences of these general results related to various multiresolution mrf based modeling approaches in image analysis are presented

hybrid diversification operator based evolutionary approach towards tomographic image reconstruction the proposed algorithm introduces a new and efficient hybrid diversification operator hdo in the evolution cycle to improve the tomographic image reconstruction and diversity in the population by using simulated annealing sa and the modified form of decreasing law of mutation probability this evolutionary approach has been used for parallel ray transmission tomography with the head and lung phantoms the algorithm is designed to address the observation that the convergence of a genetic algorithm slows down as it evolves the hdo is shown to yield a higher image quality as compared with the filtered back projection fbp the multiscale wavelet transform the sa and the hybrid continuous genetic algorithm hcga techniques various crossover operators including uniform block and image row crossover operators have also been analyzed and the latter has been generally found to give better image quality the hdo is shown to yield improvements of up to 92 and 120 when compared with fbp in terms of psnr for 128 t 128 head and lung phantoms respectively

multilevel enhancement and detection of stereo disparity surfaces abstract the problem of stereo vision has been of increasing interest to the computer vision community over the past decade this paper presents a new computational framework for matching a pair of stereo images arising from viewing the same object from two different positions in contrast to previous work this approach formulates the matching problem as detection of a bright coherent disparity surface in a 3d image called the spatio disparity space sds image the sds images represents the goodness of each and every possible match a nonlinear filter is proposed for enhancing the disparity surface in the sds image and for suppressing the noise this filter is used to construct a hyperpyramid representation of the sds image then the disparity surface is detected using a coarse to fine control structure the proposed method is robust to photometric and geometric distortions in the stereo images and has a number of computational advantages it produces good results for complex scenes

an investigation of smoothness constraints for the estimation of displacement vector fields from image sequences a mapping between one frame from an image sequence and the preceding or following frame can be represented as a displacement vector field in most situations the mere gray value variations do not provide sufficient information in order to estimate such a displacement vector field supplementary constraints are necessary for example the postulate that a displacement vector field varies smoothly as a function of the image position taken as a general requirement this creates difficulties at gray value transitions which correspond to occluding contours nagel therefore introduced the oriented smoothness requirement which restricts variations of the displacement vector field only in directions with small or no variation of gray values this contribution reports results of an investigation about how such an oriented smoothness constraint may be formulated and evaluated

geometric constraints and stereo disparity computation most stereo techniques compute disparity assuming that it varies slowly along surfaces we quantify and justify this assumption using weak assumptions about surface orientation distributions in the world to derive the density of disparity surface orientations the small disparity change assumption is justified by the orientation density s heavy bias toward disparity surfaces that are nearly parallel to the image plane in addition the bias strengthens with smaller baselines larger focal lengths and as surfaces move farther from the cameras to analyze current stereo techniques we derive three densities from the first density those of the disparity gradient magnitude the directional derivative of disparity and the difference in disparity between neighboring surface points the latter may be used in bayesian algorithms computing dense disparity fields the directional derivative density and the disparity difference density both show that feature based algorithms should strongly favor small disparity changes contrary to several well known algorithms finally we use our original surface orientation density and the gradient magnitude density to derive two new surfaces from stereo techniques techniques combining feature based matching and surface reconstruction the first uses the densities to severely restrict the search range for the optimum fit the second incorporates the surface orientation density into the optimization criteria producing a bayesian formulation both algorithms are shown to be efficient and effective

potentials valleys and dynamic global coverings we present a new approach to effect the transition between local and global representations it is based on the notion of a covering or a collection of objects whose union is equivalent to the full one the mathematics of computing global coverings are developed in the context of curve detection where an intermediate representation the tangent field provides a reliable local description of curve structure this local information is put together globally in the form of a potential distribution the elements of the covering are then short curves each of which evolves in parallel to seek the valleys of the potential distribution the initial curve positions are also derived from the tangent field and their evolution is governed by variational principles when stationary configurations are achieved the global dynamic covering is defined by the union of the local dynamic curves

intensity and gradient based stereo matching using hierarchical gaussian basis functions we propose a stereo correspondence method by minimizing intensity and gradient errors simultaneously in contrast to conventional use of image gradients the gradients are applied in the deformed image space although a uniform smoothness constraint is imposed it is applied only to nonfeature regions to avoid local minima in the function minimization we propose to parameterize the disparity function by hierarchical gaussians both the uniqueness and the ordering constraints can be easily imposed in our minimization framework besides we propose a method to estimate the disparity map and the camera response difference parameters simultaneously experiments with various real stereo images show robust performances of our algorithm

estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising we develop three novel wavelet domain denoising methods for subband adaptive spatially adaptive and multivalued image denoising the core of our approach is the estimation of the probability that a given coefficient contains a significant noise free component which we call signal of interest in this respect we analyze cases where the probability of signal presence is 1 fixed per subband 2 conditioned on a local spatial context and 3 conditioned on information from multiple image bands all the probabilities are estimated assuming a generalized laplacian prior for noise free subband data and additive white gaussian noise the results demonstrate that the new subband adaptive shrinkage function outperforms bayesian thresholding approaches in terms of mean squared error the spatially adaptive version of the proposed method yields better results than the existing spatially adaptive ones of similar and higher complexity the performance on color and on multispectral images is superior with respect to recent multiband wavelet thresholding

extracting view dependent depth maps from a collection of images stereo correspondence algorithms typically produce a single depth map in addition to the usual problems of occlusions and textureless regions such algorithms cannot model the variation in scene or object appearance with respect to the viewing position in this paper we propose a new representation that overcomes the appearance variation problem associated with an image sequence rather than estimating a single depth map we associate a depth map with each input image or a subset of them our representation is motivated by applications such as view interpolation and depth based segmentation for model building or layer extraction we describe two approaches to extract such a representation from a sequence of images the first approach which is more classical computes the local depth map associated with each chosen reference frame independently the novelty of this approach lies in its combination of shiftable windows temporal selection and graph cut optimization the second approach simultaneously optimizes a set of self consistent depth maps at multiple key frames since multiple depth maps are estimated simultaneously visibility can be modeled explicitly and disparity consistency imposed across the different depth maps results which include a difficult specular scene example show the effectiveness of our approach

a model based on local graphs for colour images and its application for gaussian noise smoothing in this paper a new model for processing colour images is presented a graph is built for each image pixel taking into account some constraints on links each pixel is characterized depending on the features of its related graph which allows to process it appropriately as an example we provide a characterization of each pixel based on the link cardinality of its connected component this feature enables us to properly distinguish flat image regions respect to edge and detail regions according to this we have designed a hybrid filter for colour image smoothing it combines a filter able to properly process flat image regions with another one that is more appropriate for details and texture experimental results show that our model performs appropriately we also see that our proposed filter is competitive with respect to state of the art methods it is close closer to the corresponding optimal switching filter respect to other analogous hybrid method

analysis of multiscale products for step detection and estimation we analyze discrete wavelet transform dwt multiscale products for detection and estimation of steps here the dwt is an over complete approximation to smoothed gradient estimation with smoothing varied over dyadic scale as developed by mallat and zhong 1992 the multiscale product approach was first proposed by rosenfeld 1970 for edge detection we develop statistics of the multiscale products and characterize the resulting non gaussian heavy tailed densities the results may be applied to edge detection with a false alarm constraint the response to impulses steps and pulses is also characterized to facilitate the analysis we employ a new general closed form expression for the cramer rao bound crb for discrete time step change location estimation the crb can incorporate any underlying continuous and differentiable edge model including an arbitrary number of steps the crb analysis also includes sampling phase offset effects and is valid in both additive correlated gaussian and independent and identically distributed i i d non gaussian noise we consider location estimation using multiscale products and compare results to the appropriate crb

stochastic stereo matching over scale a stochastic optimization approach to stereo matching is presented unlike conventional correlation matching and feature matching the method provides a dense array of disparities eliminating the need for interpolation first the stereo matching problem is defined in terms of finding a disparity map that satisfies two competing constraints 1 matched points should have similar image intensity and 2 the disparity map should vary as slowly as possible these constraints are interpreted as specifying the potential energy of a system of oscillators ground states are approximated by a new variant of simulated annealing which has two important features first the microcanonical ensemble is simulated using a new algorithm that is more efficient and more easily implemented than the familiar metropolis algorithm which simulates the canonical ensemble secondly it uses a hierarchical coarse to fine control structure employing gaussian or laplacian pyramids of the stereo images in this way quickly computed results at low resolutions are used to initialize the system at higher resolutions

variational optical flow computation in real time this paper investigates the usefulness of bidirectional multigrid methods for variational optical flow computations although these numerical schemes are among the fastest methods for solving equation systems they are rarely applied in the field of computer vision we demonstrate how to employ those numerical methods for the treatment of variational optical flow formulations and show that the efficiency of this approach even allows for real time performance on standard pcs as a representative for variational optic flow methods we consider the recently introduced combined local global method it can be considered as a noise robust generalization of the horn and schunck technique we present a decoupled as well as a coupled version of the classical gau spl szlig seidel solver and we develop several multigrid implementations based on a discretization coarse grid approximation in contrast with standard bidirectional multigrid algorithms we take advantage of intergrid transfer operators that allow for nondyadic grid hierarchies as a consequence no restrictions concerning the image size or the number of traversed levels have to be imposed in the experimental section we juxtapose the developed multigrid schemes and demonstrate their superior performance when compared to unidirectional multigrid methods and nonhierachical solvers for the well known 316 spl times 252 yosemite sequence we succeeded in computing the complete set of dense flow fields in three quarters of a second on a 3 06 ghz pentium4 pc this corresponds to a frame rate of 18 flow fields per second which outperforms the widely used gau spl szlig seidel method by almost three orders of magnitude

an extremum principle for shape from contour an extremum principle is developed that determines three dimensional surface orientation from a two dimensional contour the principle maximizes the ratio of the area to the square of the perimeter a measure of the compactness or symmetry of the three dimensional surface the principle interprets regular figures correctly and it interprets skew symmetries as oriented real symmetries the maximum likelihood method approximates the principle on irregular figures but we show that it consistently overestimates the slant of an ellipse

the laplacian pyramid as a compact image code we describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions the representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space pixel to pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself the result is a net data compression since the difference or error image has low variance and entropy and the low pass filtered image may represented at reduced sample density further data compression is achieved by quantizing the difference image these steps are then repeated to compress the low pass image iteration of the process at appropriately expanded scales generates a pyramid data structure the encoding process is equivalent to sampling the image with laplacian operators of many scales thus the code tends to enhance salient image features a further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression fast algorithms are described for coding and decoding

a multiple baseline stereo a stereo matching method that uses multiple stereo pairs with various baselines generated by a lateral displacement of a camera to obtain precise distance estimates without suffering from ambiguity is presented matching is performed simply by computing the sum of squared difference ssd values the ssd functions for individual stereo pairs are represented with respect to the inverse distance and are then added to produce the sum of ssds this resulting function is called the sssd in inverse distance it is shown that the sssd in inverse distance function exhibits a unique and clear minimum at the correct matching position even when the underlying intensity patterns of the scene include ambiguities or repetitive patterns the authors first define a stereo algorithm based on the sssd in inverse distance and present a mathematical analysis to show how the algorithm can remove ambiguity and increase precision experimental results with real stereo images are presented to demonstrate the effectiveness of the algorithm

numerical shape from shading and occluding boundaries an iterative method for computing shape from shading using occluding boundary information is proposed some applications of this method are shown we employ the stereographic plane to express the orientations of surface patches rather than the more commonly used gradient space use of the stereographic plane makes it possible to incorporate occluding boundary information but forces us to employ a smoothness constraint different from the one previously proposed the new constraint follows directly from a particular definition of surface smoothness we solve the set of equations arising from the smoothness constraints and the image irradiance equation iteratively using occluding boundary information to supply boundary conditions good initial values are found at certain points to help reduce the number of iterations required to reach a reasonable solution numerical experiments show that the method is effective and robust finally we analyze scanning electron microscope sem pictures using this method other applications are also proposed

performance of double three dimensional rigid barriers used to create an acoustic space a normal derivative integral equationapproach abstract this paper simulates the propagation of sound generated by point pressure sources in the vicinity of double three dimensional 3d barriers placed so as to create an indoor acoustic space the barriers are assumed to be very thin rigid elements the problem is solved by developing and implementing a 3d boundary element method formulation using a normal derivative integral equation tbem thereby allowing the definition of models in which only the discretization of the barriers as single open surfaces is required the tbem is formulated in the frequency domain and the resulting hypersingular terms are computed analytically after the verification of the model against two and a half dimensional 2 5d bem solutions several numerical applications are described to illustrate the applicability and usefulness of the proposed approaches different barrier shape geometries and their relative position with respect to a lateral wall are analyzed to evaluate the performance of double 3d rigid barriers in the creation of an acoustic space

computation and analysis of image motion a synopsis of current problems and methods the goal of this paper is to offer a structured synopsis of the problems in image motion computation and analysis and of the methods proposed exposing the underlying models and supporting assumptions a sufficient number of pointers to the literature will be given concentrating mostly on recent contributions emphasis will be on the detection measurement and segmentation of image motion tracking and deformable motion issues will be also addressed finally a number of related questions which could require more investigations will be presented

using chromatic information in dense stereo correspondence abstract the use of chromatic information in dense stereo correspondence is investigated specifically the chromatic photometric constraint which is used to specify a mathematical optimality criterion for solving the dense stereo correspondence problem is developed the result is a theoretical construction for developing dense stereo correspondence algorithms which use chromatic information the efficacy of using chromatic information via this construction is tested by implementing single and multi resolution versions of a stereo correspondence algorithm that uses simulated annealing as a means of solving the optimization problem results demonstrate that the use of chromatic information can significantly improve the performance of dense stereo correspondence

progressively weighted affine adaptive correlation matching for quasi dense 3d reconstruction correlation matching has been widely accepted as a rudimentary similarity measure to obtain dense 3d reconstruction from a stereo pair in particular given a large overlapping area between images with minimal scale differences the correlation results followed by a geometrically constrained global optimisation delivers adequately dense and accurate reconstruction results in order to achieve greater reliability however correlation matching should correctly account for the geometrical distortion introduced by the different viewing angles of the stereo or multi view sensors conventional adaptive least squares correlation alsc matching addresses this by modifying the shape of a matching window iteratively assuming that the distortion can be approximated by an affine transform nevertheless since an image captured from different viewing angle is often not practically identical due to scene occlusions the matching confidence normally deteriorates subsequently it affects the density of the reconstruction results from alsc based stereo region growing algorithms to address this we propose an advanced alsc matching method that can progressively update matching weight for each pixel in an aggregating window using a relaxation labelling technique the experimental results show that the proposed method can improve matching performance which consequently enhances the quality of stereo reconstruction also the results demonstrate its ability to refine a scale invariant conjugate point pair to an affine and scale invariant point pair

adaptive wavelet thresholding for image denoising and compression the first part of this paper proposes an adaptive data driven threshold for image denoising via wavelet soft thresholding the threshold is derived in a bayesian framework and the prior used on the wavelet coefficients is the generalized gaussian distribution ggd widely used in image processing applications the proposed threshold is simple and closed form and it is adaptive to each subband because it depends on data driven estimates of the parameters experimental results show that the proposed method called bayesshrink is typically within 5 of the mse of the best soft thresholding benchmark with the image assumed known it also outperforms sureshrink donoho and johnstone 1994 1995 donoho 1995 most of the time the second part of the paper attempts to further validate claims that lossy compression can be used for denoising the bayesshrink threshold can aid in the parameter selection of a coder designed with the intention of denoising and thus achieving simultaneous denoising and compression specifically the zero zone in the quantization step of compression is analogous to the threshold value in the thresholding function the remaining coder design parameters are chosen based on a criterion derived from rissanen s minimum description length mdl principle experiments show that this compression method does indeed remove noise significantly especially for large noise power however it introduces quantization noise and should be used only if bitrate were an additional concern to denoising

efficient stereoscopic ranging via stochastic sampling of match quality we present an efficient method that computes dense stereo correspondences by stochastically sampling match quality values nonexhaustive sampling facilitates the use of quality metrics that take unique values at noninteger disparities depth estimates are iteratively refined with a stochastic cooperative search by perturbing the estimates sampling match quality and reweighting and aggregating the perturbations the approach gains significant efficiencies when applied to video where initial estimates are seeded using information from the previous pair in a novel application of the z buffering algorithm this significantly reduces the number of search iterations required we present a quantitative accuracy evaluation wherein the proposed method outperforms a microcanonical annealing approach by barnard and a cooperative approach by zitnick and kanade while using fewer match quality evaluations than either the approach is shown to have more attractive memory usage and scaling than alternatives based on exhaustive sampling

matching 3 d anatomical surfaces with non rigid deformations using octree splines presents a new method for determining the minimal nonrigid deformation between two 3d surfaces such as those which describe anatomical structures in 3d medical images although the authors match surfaces they represent the deformation as a volumetric transformation their method performs a least squares minimization of the distance between the two surfaces of interest to quickly and accurately compute distances between points on the two surfaces the authors use a precomputed distance map represented using an octree spline whose resolution increases near the surface to quickly and robustly compute the deformation the authors use a second octree spline to model the deformation function the coarsest level of the deformation encodes the global e g affine transformation between the two surfaces while finer levels encode smooth local displacements which bring the two surfaces into closer registration the authors present experimental results on both synthetic and real 3d surfaces

a comparative study of energy minimization methods for markov random fields with smoothness based priors among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel labeling tasks such as depth or texture computation it has been known for decades that such problems can be elegantly expressed as markov random fields yet the resulting energy minimization problems have been widely viewed as intractable algorithms such as graph cuts and loopy belief propagation lbp have proven to be very powerful for example such methods form the basis for almost all the top performing stereo methods however the trade offs among different energy minimization algorithms are still not well understood in this paper we describe a set of energy minimization benchmarks and use them to compare the solution quality and runtime of several common energy minimization algorithms we investigate three promising methods graph cuts lbp and tree reweighted message passing in addition to the well known older iterated conditional mode icm algorithm our benchmark problems are drawn from published energy functions used for stereo image stitching interactive segmentation and denoising we also provide a general purpose software interface that allows vision researchers to easily switch between optimization methods the benchmarks code images and results are available at http vision middlebury edu mrf

a novel boundary integral formulation for three dimensional analysis of thin acoustic barriers over an impedance plane this article presents a three dimensional formulation for the analysis of acoustic barriers over an impedance plane as infinitely thin structures the barriers are therefore modeled as simple surfaces rather than volumetric structures using this approach the problems caused by near singular integrations and near degenerate systems of equations are averted and mesh generation is made easier a dual boundary element method is used in the analysis involving the simultaneous solution of standard and hypersingular boundary integral equations an optimization procedure is used to speed up the assembling of the system of equations increasing the applicability of the method to a wider range of frequencies

multimodal estimation of discontinuous optical flow using markov random fields the estimation of dense velocity fields from image sequences is basically an ill posed problem primarily because the data only partially constrain the solution it is rendered especially difficult by the presence of motion boundaries and occlusion regions which are not taken into account by standard regularization approaches in this paper the authors present a multimodal approach to the problem of motion estimation in which the computation of visual motion is based on several complementary constraints it is shown that multiple constraints can provide more accurate flow estimation in a wide range of circumstances the theoretical framework relies on bayesian estimation associated with global statistical models namely markov random fields the constraints introduced here aim to address the following issues optical flow estimation while preserving motion boundaries processing of occlusion regions fusion between gradient and feature based motion constraint equations deterministic relaxation algorithms are used to merge information and to provide a solution to the maximum a posteriori estimation of the unknown dense motion field the algorithm is well suited to a multiresolution implementation which brings an appreciable speed up as well as a significant improvement of estimation when large displacements are present in the scene experiments on synthetic and real world image sequences are reported

simulation of sound absorption in 2d thin elements using a coupled bem tbem formulation in the presence of fixed and moving 3d sources abstract modelling sound propagation in complex environments can be a difficult task in realistic applications the boundaries of scatterers present in the propagation domain can be partially absorbing and this must be accounted for in the numerical models this paper addresses the use of a frequency domain dual bem bem tbem formulation to model the propagation of sound generated by fixed and moving point loads in 2 5d configurations in the presence of very thin elements with partially absorbing surfaces the proposed approach is based on the concept of impedance boundary conditions and is applied in conjunction with a dual bem approach thereby allowing the definition of models in which only very compact descriptions of the propagation domain are required since a 2 5d formulation is used 3d responses can be computed as a discrete summation of simpler 2d solutions the formulation of the numerical methods used here bem tbem and dual bem are described together with the strategy devised by the authors to incorporate sound absorption the model is verified against closed form solutions and the calculations indicate that it can provide very accurate results several numerical applications involving fixed and moving 3d sources are described to illustrate the applicability and usefulness of the proposed approaches in the analysis of various scenarios

analysis of multichannel narrow band filters for image texture segmentation a model for texture analysis and segmentation using multiple oriented channel filters is analyzed in the general framework several different arguments are applied leading to the conclusion that the two dimensional gabor filters possess strong optimality properties for this task properties of the multiple channel segmentation approach are analyzed in particular perturbations of textures from an ideal model are found to have important effects on the segmentation that can usually be ameliorated by simply preceding the segmentation process by a logarithmic operation and using a low pass postfilter prior to making region assignments the difficult problems of space variant textures and multiple component textures are also considered local spatial frequency estimation approaches are suggested that use the responses as constraints in estimating the locally emergent texture frequencies complex texture aggregates containing multiple shared frequency components can be analyzed if the textures are distinct and few in number

surfaces with occlusions from layered stereo we propose a new binocular stereo algorithm that estimates scene structure as a collection of smooth surface patches the disparities within each patch are modeled by a continuous valued spline while the extent of each patch is represented via a pixelwise partitioning of the images disparities and extents are alternately estimated in an iterative energy minimization framework experimental results demonstrate that for scenes consisting of smooth surfaces the proposed algorithm significantly improves upon the state of the art

edge preserving image denoising via optimal color space projection denoising of color images can be done on each color component independently recent work has shown that exploiting strong correlation between high frequency content of different color components can improve the denoising performance we show that for typical color images high correlation also means similarity and propose to exploit this strong intercolor dependency using an optimal luminance color difference space projection experimental results confirm that performing denoising on the projected color components yields superior denoising performance both in peak signal to noise ratio and visual quality sense compared to that of existing solutions we also develop a novel approach to estimate directly from the noisy image data the image and noise statistics which are required to determine the optimal projection

sure let multichannel image denoising interscale orthonormal wavelet thresholding we propose a vector matrix extension of our denoising algorithm initially developed for grayscale images in order to efficiently process multichannel e g color images this work follows our recently published sure let approach where the denoising algorithm is parameterized as a linear expansion of thresholds let and optimized using stein s unbiased risk estimate sure the proposed wavelet thresholding function is pointwise and depends on the coefficients of same location in the other channels as well as on their parents in the coarser wavelet subband a nonredundant orthonormal wavelet transform is first applied to the noisy data followed by the subband dependent vector valued thresholding of individual multichannel wavelet coefficients which are finally brought back to the image domain by inverse wavelet transform extensive comparisons with the state of the art multiresolution image denoising algorithms indicate that despite being nonredundant our algorithm matches the quality of the best redundant approaches while maintaining a high computational efficiency and a low cpu memory consumption an online java demo illustrates these assertions

joint reconstruction of multiview compressed images distributed representation of correlated multiview images is an important problem that arises in vision sensor networks this paper concentrates on the joint reconstruction problem where the distributively compressed images are decoded together in order to take benefit from the image correlation we consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions e g jpeg with a balanced rate distribution among different cameras a central decoder first estimates the inter view image correlation from the independently compressed data the joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total variation tv smooth images which comply with the estimated correlation model at the same time we add constraints that force the reconstructed images to be as close as possible to their compressed versions we show through experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality for a given target bit rate in addition the decoding performance of our algorithm compares advantageously to state of the art distributed coding schemes based on motion learning and on the discover algorithm

shape and the stereo correspondence problem we examine the implications of shape on the process of finding dense correspondence and half occlusions for a stereo pair of images the desired property of the disparity map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities to zeroth order piecewise continuity becomes piecewise constancy using this approximation we first discuss an approach for dealing with such a fronto parallel shapeless world and the problems involved therein we then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity in particular we emphasize the following geometric fact a horizontally slanted surface i e having depth variation in the direction of the separation of the two cameras will appear horizontally stretched in one image as compared to the other image thus while corresponding two images n pixels on a scanline in one image may correspond to a different number of pixels m in the other image this leads to three important modifications to existing stereo algorithms a due to unequal sampling existing intensity matching metrics must be modified b unequal numbers of pixels in the two images must be allowed to correspond to each other and c the uniqueness constraint which is often used for detecting occlusions must be changed to an interval uniqueness constraint we also discuss the asymmetry between vertical and horizontal slant and the central role of non horizontal edges in the context of vertical slant using experiments we discuss cases where existing algorithms fail and how the incorporation of these new constraints provides correct results

a dual boundary element formulation for sound propagation around barriers over an impedance plane in this paper the dual boundary element method is formulated and applied to study outdoor sound propagation where acoustic barriers are modelled as non thickness bodies that may have different impedance conditions at each side standard and hypersingular integral equations are applied on each side of the barrier the infinite impedance plane is incorporated into the green function in both equations avoiding the discretization of the ground numerical results are presented validating the formulation and the use of non thickness barriers

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed