a novel implementation of multilevel fast multipole algorithm for higher order galerkin s method a new approach is proposed to reduce the memory requirements of the multilevel fast multipole algorithm mlfma when applied to the higher order galerkin s method this approach represents higher order basis functions by a set of point sources such that a matrix vector multiply is equivalent to calculating the fields at a number of points from given current sources at these points the mlfma is then applied to calculate the point to point interactions this permits the use of more levels in mlfma than applying mlfma to basis to basis interactions directly and thus reduces the memory requirements significantly

on the estimation of optical flow relations between different approaches and some new results abstract a common basis is suggested for the optical flow estimation approaches of nagel 1983 haralick and lee 1983 and tretiak and pastor 1984 based on a discussion of these approaches an exact solution for the system of partial differential equations proposed by horn and schunck 1981 is given at gray value corners and extrema the insight gained by this solution results in a modification of the oriented smoothness approach of nagel 1983 which thereby becomes considerably simpler in addition the optical flow estimation approach of hildreth 1983 1984 can be shown to represent a kind of special case of this modified oriented smoothness approach in a more direct manner than discussed in nagel 1984

fast surface interpolation using hierarchical basis functions an alternative to multigrid relaxation that is much easier to implement and more generally applicable is presented conjugate gradient descent is used in conjunction with a hierarchical multiresolution set of basis functions the resultant algorithm uses a pyramid to smooth the residual vector before the direction is computed simulation results showing the speed of convergence and its dependence on the choice of interpolator the number of smoothing levels and other factors are presented the relationship of this approach to other multiresolution relaxation and representation schemes is also discussed

a new upper bound on the ml decoding error probability of linear binary block codes in awgn interference performance evaluation of maximum likelihood ml soft decision decoded binary block codes is usually carried out using bounding techniques many tight upper bounds on the error probability of binary codes are based on the so called gallager s first bounding technique gfbt the tangential sphere bound tsb of poltyrev which has been believed for many years to offer the tightest bound developed for binary block codes is an example within the framework of the tsb and gfbt we apply a new method referred to as the added hyper plane ahp technique to the decomposition of the error probability this results in a bound developed upon the application of two stages of the gfbt with two different gallager regions culminating in a tightened upper bound beyond the tsb the proposed bound is simple and only requires the spectrum of the binary code

a bounded distance decoding algorithm for the leech lattice with generalizations an algorithm is given that decodes the leech lattice with not much more than twice the complexity of soft decision decoding of the golay code the algorithm has the same effective minimum distance as maximum likelihood decoding and increases the effective error coefficient by less than a factor or two the algorithm can be recognized as a member of the class of multistage algorithms that are applicable to hierarchical constructions it is readily generalized to lattices that can be expressed in terms of binary code formulas and in particular to construction b lattices

multiresolution moment filters theory and applications we introduce local weighted geometric moments that are computed from an image within a sliding window at multiple scales when the window function satisfies a two scale relation we prove that lower order moments can be computed efficiently at dyadic scales by using a multiresolution wavelet like algorithm we show that b splines are well suited window functions because in addition to being refinable they are positive symmetric separable and very nearly isotropic gaussian shape we present three applications of these multiscale local moments the first is a feature extraction method for detecting and characterizing elongated structures in images the second is a noise reduction method which can be viewed as a multiscale extension of savitzky golay filtering the third is a multiscale optical flow algorithm that uses a local affine model for the motion field extending the lucas kanade optical flow method the results obtained in all cases are promising

multilevel codes based on partitioning following v v ginzburg 1984 a hierarchy of codes is proposed to match the geometric partitioning of a signal set the authors show that coset codes including ungerboeck lattice and binary codes and indeed any codes which rely on partitioning of the signal set are all subclasses of the proposed coding scheme the combination of such codes in a multilevel scheme often leads to reduced complexity in comparison with previously published schemes a variety of decoder structures is discussed

design of steerable wavelets to detect multifold junctions we propose a framework for the detection of junctions in images although the detection of edges and key points is a well examined and described area the multiscale detection of junction centers especially for odd orders poses a challenge in pattern analysis the goal of this paper is to build optimal junction detectors based on 2d steerable wavelets that are polar separable in the fourier domain the approaches we develop are general and can be used for the detection of arbitrary symmetric and asymmetric junctions the backbone of our construction is a multiscale pyramid with a radial wavelet function where the directional components are represented by circular harmonics and encoded in a shaping matrix we are able to detect m fold junctions in different scales and orientations we provide experimental results on both simulated and real data to demonstrate the effectiveness of the algorithm

motion competition a variational approach to piecewise parametric motion segmentation we present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence our model is based on a conditional probability for the spatio temporal image gradient given a particular velocity model and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length exploiting the bayesian framework we derive a cost functional which depends on parametric motion models for each of a set of regions and on the boundary separating these regions the resulting functional can be interpreted as an extension of the mumford shah functional from intensity segmentation to motion segmentation in contrast to most alternative approaches the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional minimizing this functional with respect to its dynamic variables results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion discontinuity set we propose two different representations of this motion boundary an explicit spline based implementation which can be applied to the motion based tracking of a single moving object and an implicit multiphase level set implementation which allows for the segmentation of an arbitrary number of multiply connected moving objects numerical results both for simulated ground truth experiments and for real world sequences demonstrate the capacity of our approach to segment objects based exclusively on their relative motion

tightened upper bounds on the ml decoding error probability of binary linear block codes the performance of maximum likelihood ml decoded binary linear block codes is addressed via the derivation of tightened upper bounds on their decoding error probability the upper bounds on the block and bit error probabilities are valid for any memoryless binary input and output symmetric communication channel and their effectiveness is exemplified for various ensembles of turbo like codes over the additive white gaussian noise awgn channel an expurgation of the distance spectrum of binary linear block codes further tightens the resulting upper bounds

approximate orientation steerability based on angular gaussians junctions are significant features in images with intensity variation that exhibits multiple orientations this makes the detection and characterization of junctions a challenging problem the characterization of junctions would ideally be given by the response of a filter at every orientation this can be achieved by the principle of steerability that enables the decomposition of a filter into a linear combination of basis functions however current steerability approaches suffer from the consequences of the uncertainty principle in order to achieve high resolution in orientation they need a large number of basis filters increasing thus the computational complexity furthermore these functions have usually a wide support which only accentuates the computational burden we propose a novel alternative to current steerability approaches it is based on utilizing a set of polar separable filters with small support to sample orientation information the orientation signature is then obtained by interpolating orientation samples using gaussian functions with small support compared with current steerability techniques our approach achieves a higher orientation resolution with a lower complexity in addition we build a polar pyramid to characterize junctions of arbitrary inherent orientation scales

on the spectrum of distances of a multilevel code decoded by a multistage decoder in this correspondence we are concerned with the error rates contributed by component codes in a multilevel code decoded by a multistage decoder these error rates are required for estimating the performance of the structure but more importantly they can serve as a design tool for achieving high coding gain the latter objective can be reached by approaching a balance among these error rates for evaluating the error rate contributed by a component code it is essential to calculate the corresponding spectrum of euclidean distances a method for computing this spectrum is developed in this correspondence the method is general and applicable to any signal constellation and convolutional as well as block component codes the error coefficient available in the literature for some multilevel codes with an infinite size signal constellation is shown to be significantly different from that of a practical finite constellation note that the complete spectrum is essential when codewords of a component code with hamming distance beyond the minimum distance affect the error rate see e g turbo codes

multiresolution feature extraction and selection for texture segmentation an approach is described for unsupervised segmentation of textured images local texture properties are extracted using local linear transforms that have been optimized for maximal texture discrimination local statistics texture energy measures are estimated at the output of an equivalent filter bank by means of a nonlinear transformation absolute value followed by an iterative gaussian smoothing algorithm this procedure generates a multiresolution sequence of feature planes with a half octave scale progression a feature reduction technique is then applied to the data and is determined by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions this approach provides a good approximation of r a fisher s 1950 multiple linear discriminants and has the advantage of requiring no a priori knowledge this feature reduction methods appears to be an improvement on the commonly used karhunen loeve transform and allows efficient texture segmentation based on simple thresholding

a quad tree approach to image segmentation which combines statistical and spatial information abstract a new approach to the problem of image segmentation is presented by combining a nonparametric classifier based on a clustering algorithm with a quad tree representation of the image the scheme is both simple to implement and performs well giving satisfactory results at signal to noise ratios well below 1 the results of an analysis of the algorithm are borne out by a comprehensive set of tests on gaussian images and synthetic textures which demonstrate its principal features

bounds on the decoding error probability of binary linear codes via their spectra bounds on the error probability of maximum likelihood decoding of a binary linear code are considered the bounds derived use the weight spectrum of the code and they are tighter than the conventional union bound in the case of large noise in the channel the bounds derived are applied to a code with an average spectrum and the result is compared to the random coding exponent the author shows that the bound considered for the binary symmetrical channel case coincides asymptotically with the random coding bound for the case of awgn channel the author shows that berlekamp s 1980 tangential bound can be improved but even this improved bound does not coincide with the random coding bound although it can be very close to it

the design and use of steerable filters the authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters allowing one to adaptively steer a filter to any orientation and to determine analytically the filter output as a function of orientation steerable filters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation the authors show how to design and steer the filters and present examples of their use in the analysis of orientation and phase angularly adaptive filtering edge detection and shape from shading one can also build a self similar steerable pyramid representation the same concepts can be generalized to the design of 3 d steerable filters

maximum likelihood orientation estimation of 1 d patterns in laguerre gauss subspaces a method for measuring the orientation of linear 1 d patterns based on a local expansion with laguerre gauss circular harmonic lg ch functions is presented it lies on the property that the polar separable lg ch functions span the same space as the 2 d cartesian separable hermite gauss 2 d hg functions exploiting the simple steerability of the lg ch functions and the peculiar block linear relationship among the two expansion coefficients sets maximum likelihood ml estimates of orientation and cross section parameters of 1 d patterns are obtained projecting them in a proper subspace of the 2 d hg family it is shown in this paper that the conditional ml solution derived by elimination of the cross section parameters surprisingly yields the same asymptotic accuracy as the ml solution for known cross section parameters the accuracy of the conditional ml estimator is compared to the one of state of art solutions on a theoretical basis and via simulation trials a thorough proof of the key relationship between the lg ch and the 2 d hg expansions is also provided

hierarchical construction of orientation and velocity selective filters as a step towards the early measurement of visual primitives the authors outline design criteria for the extraction of orientation and velocity information and present a variety of tools useful in the construction of simple linear filters a hierarchical parallel processing scheme is used in which nodes compute a weighted sum of inputs from within a small spatio temporal neighborhood the resulting scheme is easily analyzed and provides mechanisms sensitive to narrow ranges of both image velocity and orientation the hierarchical approach in combination with separability in the first levels yields an efficient implementation

on the renyi divergence joint range of relative entropies and a channel coding theorem this paper starts by considering the minimization of the renyi divergence subject to a constraint on the total variation distance based on the solution of this optimization problem the exact locus of the points bigl d q p 1 d q p 2 bigr is determined when p 1 p 2 and q are arbitrary probability measures which are mutually absolutely continuous and the total variation distance between p 1 and p 2 is not below a given value it is further shown that all the points of this convex region are attained by probability measures which are defined on a binary alphabet this characterization yields a geometric interpretation of the minimal chernoff information subject to a constraint on the variational distance this paper also derives an exponential upper bound on the performance of binary linear block codes or code ensembles under maximum likelihood decoding its derivation relies on the gallager bounding technique and it reproduces the shulman feder bound as a special case the bound is expressed in terms of the renyi divergence from the normalized distance spectrum of the code or the average distance spectrum of the ensemble to the binomially distributed distance spectrum of the capacity achieving ensemble of random block codes this exponential bound provides a quantitative measure of the degradation in performance of binary linear block codes or code ensembles as a function of the deviation of their distance spectra from the binomial distribution an efficient use of this bound is considered

multilevel codes theoretical concepts and practical design rules this paper deals with 2 sup l ary transmission using multilevel coding mlc and multistage decoding msd the known result that mlc and msd suffice to approach capacity if the rates at each level are appropriately chosen is reviewed using multiuser information theory it is shown that there is a large space of rate combinations such that mlc and full maximum likelihood decoding mld can approach capacity it is noted that multilevel codes designed according to the traditional balanced distance rule tend to fall in the latter category and therefore require the huge complexity of mld the capacity rule the balanced distances rules and two other rules based on the random coding exponent and cutoff rate are compared and contrasted for practical design simulation results using multilevel binary turbo codes show that capacity can in fact be closely approached at high bandwidth efficiencies moreover topics relevant in practical applications such as signal set labeling dimensionality of the constituent constellation and hard decision decoding are emphasized bit interleaved coded modulation proposed by caire et al see ibid vol 44 p 927 46 1998 is reviewed in the context of mlc finally the combination of signal shaping and coding is discussed significant shaping gains are achievable in practice only if these design rules are taken into account

optical flow using spatiotemporal filters a model is presented consonant with current views regarding the neurophysiology and psychophysics of motion perception that combines the outputs of a set of spatiotemporal motion energy filters to estimate image velocity a parallel implementation computes a distributed representation of image velocity a measure of image flow uncertainty is formulated preliminary results indicate that this uncertainty measure may be used to recognize ambiguity due to the aperture problem the model appears to deal with the aperture problem as well as the human visual system since it extracts the correct velocity for some patterns that have large differences in contrast at different spatial orientations

improved upper bounds on the ml decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum the ensemble performance of parallel and serial concatenated turbo codes is considered where the ensemble is generated by a uniform choice of the interleaver and of the component codes taken from the set of time varying recursive systematic convolutional codes following the derivation of the input output weight enumeration functions of the ensembles of random parallel and serial concatenated turbo codes the tangential sphere upper bound is employed to provide improved upper bounds on the block and bit error probabilities of these ensembles of codes for the binary input additive white gaussian noise awgn channel based on coherent detection of equi energy antipodal signals and maximum likelihood decoding the influence of the interleaver length and the memory length of the component codes is investigated the improved bounding technique proposed here is compared to the conventional union bound and to a alternative bounding technique by duman and salehi 1998 which incorporates modified gallager bounds the advantage of the derived bounds is demonstrated for a variety of parallel and serial concatenated coding schemes with either fixed or random recursive systematic convolutional component codes and it is especially pronounced in the region exceeding the cutoff rate where the performance of turbo codes is most appealing these upper bounds are also compared to simulation results of the iterative decoding algorithm

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

channel coding with multilevel phase signals a coding technique is described which improves error performance of synchronous data links without sacrificing data rate or requiring more bandwidth this is achieved by channel coding with expanded sets of multilevel phase signals in a manner which increases free euclidean distance soft maximum likelihood ml decoding using the viterbi algorithm is assumed following a discussion of channel capacity simple hand designed trellis codes are presented for 8 phase shift keying psk and 16 quadrature amplitude shift keying qask modulation these simple codes achieve coding gains in the order of 3 4 db it is then shown that the codes can be interpreted as binary convolutional codes with a mapping of coded bits into channel signals which we call mapping by set partitioning based on a new distance measure between binary code sequences which efficiently lower bounds the euclidean distance between the corresponding channel signal sequences a search procedure for more powerful codes is developed codes with coding gains up to 6 db are obtained for a variety of multilevel phase modulation schemes simulation results are presented and an example of carrier phase tracking is discussed

steerable wedge filters for local orientation analysis steerable filters have been used to analyze local orientation patterns in imagery such filters are typically based on directional derivatives whose symmetry produces orientation responses that are periodic with period spl pi independent of image structure we present a more general set of steerable filters that alleviate this problem

coset codes i introduction and geometrical classification practically all known good constructive coding techniques for bandlimited channels including lattice codes and various trellis coded modulation schemes can be characterized as coset codes a coset code is defined by a lattice partition lambda lambda and by a binary encoder c that selects a sequence of cosets of the lattice lambda the fundamental coding gain of a coset code as well as other important parameters such as the error coefficient the decoding complexity and the constellation expansion factor are purely geometric parameters determined by c lambda lambda the known types of coset codes as well as a number of new classes that systematize and generalize known codes are classified and compared in terms of these parameters

practical signal dependent noise parameter estimation from a single noisy image the additive white gaussian noise is widely assumed in many image processing algorithms however in the real world the noise from actual cameras is better modeled as signal dependent noise sdn in this paper we focus on the sdn model and propose an algorithm to automatically estimate its parameters from a single noisy image the proposed algorithm identifies the noise level function of signal dependent noise assuming the generalized signal dependent noise model and is also applicable to the poisson gaussian noise model the accuracy is achieved by improved estimation of local mean and local noise variance from the selected low rank patches we evaluate the proposed algorithm with both synthetic and real noisy images experiments demonstrate that the proposed estimation algorithm outperforms the state of the art methods

a global interpolator with low sample rate for multilevel fast multipole algorithm a new improved version of a global interpolator utilizing trigonometric polynomials is presented for the high frequency multilevel fast multipole algorithm the number of required points to sample the outgoing and incoming field patterns is low almost half in some levels compared with the earlier published versions compared with local interpolators based on lagrange interpolating polynomials the proposed technique performs even more favorably and reduces the number of sample points by a factor of eight the numerical examples demonstrate that the interpolator allows full numerical accuracy control during the aggregation and disaggregation phases regardless of the number of the levels in the octree

design and implementation of multisteerable matched filters image analysis problems such as feature tracking edge detection image enhancement or texture analysis require the detection of multi oriented patterns which can appear at arbitrary orientations direct rotated matched filtering for feature detection is computationally expensive but can be sped up with steerable filters so far steerable filter approaches were limited to only one direction many important low level image features are however characterized by more than a single orientation we therefore present here a framework for efficiently detecting specific multi oriented patterns with arbitrary orientations in grayscale images the core idea is to construct multisteerable filters by appropriate combinations of single steerable filters we exploit that steerable filters are closed under addition and multiplication this allows to derive a design guide for multisteerable filters by means of multivariate polynomials furthermore we describe an efficient implementation scheme and discuss the use of weighting functions to reduce angular oscillations applications in camera calibration junction analysis of images from plant roots and the discrimination of l t and x junctions demonstrate the potential of this approach

analysis of superimposed oriented patterns estimation of local orientation in images may be posed as the problem of finding the minimum gray level variance axis in a local neighborhood in bivariate images the solution is given by the eigenvector corresponding to the smaller eigenvalue of a 2times2 tensor for an ideal single orientation the tensor is rank deficient i e the smaller eigenvalue vanishes a large minimal eigenvalue signals the presence of more than one local orientation what may be caused by non opaque additive or opaque occluding objects crossings bifurcations or corners we describe a framework for estimating such superimposed orientations our analysis is based on the eigensystem analysis of suitably extended tensors for both additive and occluding superpositions unlike in the single orientation case the eigensystem analysis does not directly yield the orientations rather it provides so called mixed orientation parameters mops we therefore show how to decompose the mops into the individual orientations we also show how to use tensor invariants to increase efficiency and derive a new feature for describing local neighborhoods which is invariant to rigid transformations applications are e g in texture analysis directional filtering and interpolation feature extraction for corners and crossings tracking and signal separation

automatic parameter selection for denoising algorithms using a no reference measure of image content across the field of inverse problems in image and video processing nearly all algorithms have various parameters which need to be set in order to yield good results in practice usually the choice of such parameters is made empirically with trial and error if no ground truth reference is available some analytical methods such as cross validation and stein s unbiased risk estimate sure have been successfully used to set such parameters however these methods tend to be strongly reliant on restrictive assumptions on the noise and also computationally heavy in this paper we propose a no reference metric q which is based upon singular value decomposition of local image gradient matrix and provides a quantitative measure of true image content i e sharpness and contrast as manifested in visually salient geometric features such as edges in the presence of noise and other disturbances this measure 1 is easy to compute 2 reacts reasonably to both blur and random noise and 3 works well even when the noise is not gaussian the proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms ample simulated and real data experiments support our claims furthermore tests using the tid2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions

generalized tangential sphere bound on the ml decoding error probability of linear binary block codes in awgn interference the error probability of maximum likelihood ml soft decision decoded binary block codes rarely accepts nice closed forms in addition for long codes ml decoding becomes prohibitively complex nevertheless bounds on the performance of ml decoded systems provide insight into the effect of system parameters on the overall system performance as well as a measure of goodness of the suboptimum decoding methods used in practice using the so called gallager s first bounding technique involving a so called gallager region and within the framework of tangential sphere bound tsb of poltyrev we develop a general bound referred to as the generalized tsb gtsb the gallager region is chosen to be a general hyper surface of revolution hsr which is optimized to tighten the bound the search for the optimal gallager region is a classical problem dating back to gallager s thesis in the early 1960s for the random coding case gallager provided the optimal solution in a closed form while for the nonrandom case the problem has been an active area of research in information theory for many years we prove that for a sphere code the optimal hsr within the proposed gtsb is a hyper cone this will climax to the tsb of poltyrev one of the tightest bounds ever developed for binary block codes and therefore terminates the search for a better gallager region in the groundwork of the gtsb

concatenated multilevel block coded modulation encoding and decoding schemes for concatenated multilevel block codes are presented by one of these structures a real coding gain of 5 6 7 4 db for the bit error range of 10 sup 6 to 10 sup 9 is achieved for transmission through the additive white gaussian noise channel also a rather large asymptotic coding gain is obtained the new coding schemes have very low decoding complexity and increased coding gain in comparison with the conventional block and trellis coded modulation structures a few design rules for concatenated single and multilevel block codes with large coding gain are also provided

coset codes ii binary lattices and related codes for pt i see ibid vol 34 no 5 p 1123 51 1988 the family of barnes wall lattices including d sub 4 and e sub 8 of lengths n 2 sup n and their principal sublattices which are useful in constructing coset codes are generated by iteration of a simple construction called the squaring construction the closely related reed muller codes are generated by the same construction the principal properties of these codes and lattices are consequences of the general properties of iterated squaring constructions which also exhibit the interrelationships between codes and lattices of different lengths an extension called the cubing construction generates good codes and lattices of lengths n 3 2 sup n including the golay code and leech lattice with the use of special bases for 8 space another related construction generates the nordstrom robinson code and an analogous 16 dimensional nonlattice packing these constructions are represented by trellis diagrams that display their structure and interrelationships and that lead to efficient maximum likelihood decoding algorithms

the error probability of m ary psk block coded modulation schemes a tight upper bound on the decoding error probability is derived for block coded modulation structures where an m ary phase shift keying m psk signal constellation is employed this bound called a tangential sphere bound is tight for very low as well as for high signal to noise ratios snrs berlekamp s tangential union bound previously derived for binary codes can be derived for an m psk block coded modulation structure as well however it is proven that our tangential sphere bound is tighter than berlekamp s 1980 tangential bound for particular schemes it is shown that for low snrs our bound is considerably tighter than the tangential bound as one of the examples a multistage decoder is considered

sphere packing bounds revisited for moderate block lengths the main reference of this paper is the sphere packing bound of 1967 sp67 derived by shannon gallager and berlekamp it offers a lower bound on the decoding error probability over a very large variety of channels if it has failed so far to provide any usable material in practical implementation of telecommunication systems it is due to an original focus on asymptotic results making it inapplicable for moderate code lengths and to the difficulty of the involved methods which makes the derivation of sp67 quite hermetic and uninspiring for further research the purpose of this paper is two fold 1 to stir up some renewed interest in the topic on which shannon concluded his career in information theory thanks to a qualitative rather than technical review of the derivation of sp67 introduced by a review of the simpler sphere packing bound derived by shannon in 1959 2 to prove the practical interest of sp67 by extending its field of application to continuous output channels and particularly the additive white gaussian noise awgn channel used with any particular modulation scheme and by improving its lower bound for the moderate code length case so that it becomes the best lower bound for most iteratively decodable codes turbo codes low density parity check ldpc codes repeat accumulate ra codes etc of usual lengths

3 d motion estimation understanding and prediction from noisy image sequences this paper presents an approach to understanding general 3 d motion of a rigid body from image sequences based on dynamics a locally constant angular momentum lcam model is introduced the model is local in the sense that it is applied to a limited number of image frames at a time specifically the model constrains the motion over a local frame subsequence to be a superposition of precession and translation thus the instantaneous rotation axis of the object is allowed to change through the subsequence the trajectory of the rotation center is approximated by a vector polynomial the parameters of the model evolve in time so that they can adapt to long term changes in motion characteristics the nature and parameters of short term motion can be estimated continuously with the goal of understanding motion through the image sequence the estimation algorithm presented in this paper is linear i e the algorithm consists of solving simultaneous linear equations based on the assumption that the motion is smooth object positions and motion in the near future can be predicted and short missing subsequences can be recovered noise smoothing is achieved by overdetermination and a leastsquares criterion the framework is flexible in the sense that it allows both overdetermination in number of feature points and the number of image frames

universal bound on the performance of lattice codes we present a lower bound on the probability of symbol error for maximum likelihood decoding of lattices and lattice codes on a gaussian channel the bound is tight for error probabilities and signal to noise ratios of practical interest as opposed to most existing bounds that become tight asymptotically for high signal to noise ratios the bound is also universal it provides a limit on the highest possible coding gain that may be achieved at specific symbol error probabilities using any lattice or lattice code in n dimensions in particular it is shown that the effective coding gains of the densest known lattices are much lower than their nominal coding gains the asymptotic as n spl rarr spl infin behavior of the new bound is shown to coincide with the shannon 1948 limit for gaussian channels

myocardial motion estimation from medical images using the monogenic signal we present a method for the analysis of heart motion from medical images the algorithm exploits monogenic signal theory recently introduced as an n dimensional generalization of the analytic signal the displacement is computed locally by assuming the conservation of the monogenic phase over time a local affine displacement model is considered to account for typical heart motions as contraction expansion and shear a coarse to fine b spline scheme allows a robust and effective computation of the model s parameters and a pyramidal refinement scheme helps to handle large motions robustness against noise is increased by replacing the standard point wise computation of the monogenic orientation with a robust least squares orientation estimate given its general formulation the algorithm is well suited for images from different modalities in particular for those cases where time variant changes of local intensity invalidate the standard brightness constancy assumption this paper evaluates the method s feasibility on two emblematic cases cardiac tagged magnetic resonance and cardiac ultrasound in order to quantify the performance of the proposed method we made use of realistic synthetic sequences from both modalities for which the benchmark motion is known a comparison is presented with state of the art methods for cardiac motion analysis on the data considered these conventional approaches are outperformed by the proposed algorithm a recent global optical flow estimation algorithm based on the monogenic curvature tensor is also considered in the comparison with respect to the latter the proposed framework provides along with higher accuracy superior robustness to noise and a considerably shorter computation time

a point adaptive grouping scheme of mlfma for electrically large scattering simulation a gaussian point adaptive grouping scheme for the multilevel fast multipole algorithm mlfma has been proposed in this communication to significantly reduce the heavy memory cost resulted from using the basis functions defined on large patches without sacrificing the accuracy of the numerical solutions the grouping process in the mlfma has been considered as a single objective optimization problem and is solved by using the clustering algorithm for gaussian quadrature points on each patch meanwhile the constraint of the addition theorem for mlfma is still satisfied as a result the presented scheme is able to acquire an optimal number of multipoles for each basis function compared to the conventional octree grouping scheme or other grouping schemes used in the past the method in this communication is advantageous in terms of the memory efficiency and the solution accuracy numerical examples have been given to demonstrate the validity and effectiveness of the proposed scheme

correlation feedback technique in optical flow determination we present a new algorithm to determine optical flow that utilizes a correlation feedback technique several experiments are presented to demonstrate that our method performs generally better than some standard correlation and gradient based methods in terms of accuracy

space time coding techniques with bit interleaved coded modulations for mimo block fading channels the space time bit interleaved coded modulation st bicm is an efficient technique to obtain high diversity and coding gain on a block fading multiple input multiple output mimo channel its maximum likelihood ml performance is computed under ideal interleaving conditions which enables a global optimization taking into account channel coding thanks to a diversity upper bound derived from the singleton bound an appropriate choice of the time dimension of the space time coding is possible which maximizes diversity while minimizing complexity based on the analysis an optimized interleaver and a set of linear precoders called dispersive nucleo algebraic dna precoders are proposed the proposed precoders have good performance with respect to the state of the art and exist for any number of transmit antennas and any time dimension with turbo codes they exhibit a frame error rate which does not increase with frame length

a mixed potential mlfma for higher order moment methods with application to the generalized method of moments the application of the multilevel fast multipole algorithm mlfma to higher order moment method discretizations is a continuing open problem herein we present a point based mixed potential variant of the mlfma algorithm that exhibits an mlfma tree of arbitrary height with no restriction related to basis function support size efficient nearfield precomputation and that maintains favorable scaling for any mixture of low and high order bases the flexibility of the algorithm is also leveraged to accelerate the precomputation of algebraic preconditioners we demonstrate the method through application to the generalized method of moments gmm a recently introduced moment method discretization capable of combining both low and high order bases and geometries in the same simulation however the method may be used to accelerate other higher order moment methods as well

fast computation of the difference of low pass transform this paper defines the difference of low pass dolp transform and describes a fast algorithm for its computation the dolp is a reversible transform which converts an image into a set of bandpass images a dolp transform is shown to require o n2 multiplies and produce o n log n samples from an n sample image when gaussian low pass filters are used the result is a set of images which have been convolved with difference of gaussian dog filters from an exponential set of sizes a fast computation technique based on resampling is described and shown to reduce the dolp transform complexity to o n log n multiplies and o n storage locations a second technique cascaded convolution with expansion is then defined and also shown to reduce the computational cost to o n log n multiplies combining these two techniques yields an algorithm for a dolp transform that requires o n storage cells and requires o n multiplies

robust photometric invariant features from the color tensor luminance based features are widely used as low level input for computer vision applications even when color data is available the extension of feature detection to the color domain prevents information loss due to isoluminance and allows us to exploit the photometric information to fully exploit the extra information in the color data the vector nature of color data has to be taken into account and a sound framework is needed to combine feature and photometric invariance theory in this paper we focus on the structure tensor or color tensor which adequately handles the vector nature of color images further we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features we circumvent the drawback of unstable photometric invariants by deriving an uncertainty measure to accompany the photometric invariant derivatives the uncertainty is incorporated in the color tensor hereby allowing the computation of robust photometric invariant features the combination of the photometric invariance theory and tensor based features allows for detection of a variety of features such as photometric invariant edges corners optical flow and curvature the proposed features are tested for noise characteristics and robustness to photometric changes experiments show that the proposed features are robust to scene incidental events and that the proposed uncertainty measure improves the applicability of full invariants

performance of sphere decoding of block codes a sphere decoder searches for the closest lattice point within a certain search radius the search radius provides a tradeoff between performance and complexity we focus on analyzing the performance of sphere decoding of linear block codes we analyze the performance of soft decision sphere decoding on awgn channels and a variety of modulation schemes a hard decision sphere decoder is a bounded distance decoder with the corresponding decoding radius we analyze the performance of hard decision sphere decoding on binary and q ary symmetric channels an upper bound on the performance of maximum likelihood decoding of linear codes defined over fq e g reed solomon codes and transmitted over q ary symmetric channels is derived and used in the analysis we then discuss sphere decoding of general block codes or lattices with arbitrary modulation schemes the tradeoff between the performance and complexity of a sphere decoder is then discussed

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

time domain analysis of electromagnetic scattering problems by using integral equation method with space delayed temporal basis functions this communication presents a new numerical method for the efficient solution of marching on in time mot based time domain integral equations tdie for electromagnetic scattering from perfect electrically conducting pec object with large smooth surfaces a set of space delayed temporal basis functions is proposed for the temporal discretization and the curvilinear rao wilson glisson crwg basis functions for the spatial discretization the space delayed temporal basis functions are firstly extended to the time domain electric field integral equations tdefie then easily to the time domain combined field integral equations tdcfie numerical results demonstrate that the new method is stable and effective for the transient scattering problems from pec objects with large smooth surfaces and a single plane wave incidence a significant reduction of the number of spatial unknowns is achieved by using the space delayed temporal basis functions

multilevel fast multipole method for higher order discretizations the multi level fast multipole method mlfmm for a higher order ho discretization is demonstrate do n high frequency hf problems illustrating for the first time how an efficient mlfmm for ho can be achieved even for very large groups applying several novel ideas beneficial to both lower order and higher order discretizations results from a low memory high speed mlfmm implementation of a ho hierarchical discretization are shown these results challenge the general view that the benefits of ho and hf mlfmm cannot be combined

lucas kanade meets horn schunck combining local and global optic flow methods differential methods belong to the most widely used techniques for optic flow computation in image sequences they can be classified into local methods such as the lucas kanade technique or bigun s structure tensor method and into global methods such as the horn schunck approach and its extensions often local methods are more robust under noise while global techniques yield dense flow fields the goal of this paper is to contribute to a better understanding and the design of novel differential methods in four ways i we juxtapose the role of smoothing regularisation processes that are required in local and global differential methods for optic flow computation ii this discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches it yields dense flow fields that are robust against noise iii spatiotemporal and nonlinear extensions as well as multiresolution frameworks are presented for this hybrid method iv we propose a simple confidence measure for optic flow methods that minimise energy functionals it allows to sparsify a dense flow field gradually depending on the reliability required for the resulting flow comparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure

robust optical flow computation based on least median of squares regression an optical flow estimation technique is presented which is based on the least median of squares lmeds robust regression algorithm enabling more accurate flow estimates to be computed in the vicinity of motion discontinuities the flow is computed in a blockwise fashion using an affine model through the use of overlapping blocks coupled with a block shifting strategy redundancy is introduced into the computation of the flow this eliminates blocking effects common in most other techniques based on blockwise processing and also allows flow to be accurately computed in regions containing three distinct motions a multiresolution version of the technique is also presented again based on lmeds regression which enables image sequences containing large motions to be effectively handled an extensive set of quantitative comparisons with a wide range of previously published methods are carried out using synthetic realistic computer generated images of natural scenes with known flow and natural images both angular and absolute flow errors are calculated for those sequences with known optical flow displaced frame difference error used extensively in video compression is used for those natural scenes with unknown flow in all of the sequences tested a comparison with those methods that result in a dense flow field greater than 80 spatial coverage show that the lmeds technique produces the least error irrespective of the error measure used

analysis of multiple orientations estimation of local orientations in multivariate signals is an important problem in image processing and computer vision this general problem formulation also covers optical flow estimation which can be regarded as orientation estimation in space time volumes modelling a signal using only a single orientation however is often too restrictive since occlusions and transparencies occur frequently thus necessitating the modelling and analysis of multiple orientations we therefore develop a unifying mathematical model for multiple orientations beyond describing an arbitrary number of orientations in scalar and vector valued image data such as color image sequences it allows the unified treatment of additively and occludingly superimposed oriented structures as well as of combinations of these based on this model we describe estimation schemes for an arbitrary number of additively or occludingly superimposed orientations in images we confirm the performance of our framework on both synthetic and real image data

design of steerable filters for feature detection using canny like criteria we propose a general approach for the design of 2d feature detectors from a class of steerable functions based on the optimization of a canny like criterion in contrast with previous computational designs our approach is truly 2d and provides filters that have closed form expressions it also yields operators that have a better orientation selectivity than the classical gradient or hessian based detectors we illustrate the method with the design of operators for edge and ridge detection we present some experimental results that demonstrate the performance improvement of these new feature detectors we propose computationally efficient local optimization algorithms for the estimation of feature orientation we also introduce the notion of shape adaptable feature detection and use it for the detection of image corners

coding for a coherent ds cdma system employing an mmse receiver in a rayleigh fading channel this paper analyzes the performance of a convolutionally coded code division multiple access system which employs a linear minimum mean square error mmse receiver for interference suppression a flat rayleigh fading channel is considered where convolutional encoding and interleaving are employed in order to combat the effects of the fading theoretical results are derived for the average bit error probability of the mmse receiver where the optimum tap weights for the adaptive filter are determined by the solution of the wiener hopf equations simulation results showing the average bit error rate of the mmse receiver are also presented which incorporate the effects of recursive least squares adaptation channel estimation using pilot symbol assisted modulation and finite interleaving results show that the mmse receiver with coding can provide a substantial gain over the matched filter receiver in a rapidly varying rayleigh fading channel the results also reiterate the fact that lower rate codes are not necessarily the best choice when used with the mmse receiver

a nonparametric riemannian framework on tensor field with application to foreground segmentation background modeling on tensor field has recently been proposed for foreground detection tasks taking into account the riemannian structure of the tensor manifold recent research has focused on developing parametric methods on the tensor domain e g mixture of gaussians gmm however in some scenarios simple parametric models do not accurately explain the physical processes kernel density estimators kdes have been successful to model on euclidean sample spaces the nonparametric nature of complex time varying and non static backgrounds founded on a mathematically rigorous kde paradigm on general riemannian manifolds recently proposed in the literature we define a kde specifically to operate on the tensor manifold in order to nonparametrically reformulate the existing tensor based algorithms we present a mathematically sound framework for nonparametric modeling on tensor field to foreground detection we endow the tensor manifold with two well founded riemannian metrics i e affine invariant and log euclidean theoretical aspects are presented and the metrics are compared experimentally by inducing a space with a null curvature the log euclidean metric considerably simplifies the scheme from a practical point of view while maintaining the mathematical soundness and the excellent segmentation performance theoretic analysis and experimental results demonstrate the promise and effectiveness of this framework

techniques of bounding the probability of decoding error for block coded modulation structures two techniques for upper bounding the average probability of decoding error in coded modulation structures are presented the first bound which is applicable to the additive white gaussian noise awgn channel is tighter than the well known union bound and the minimum distance bound especially for low signal to noise ratio it is shown that for the leech lattice this upper bound is very close to a sphere lower bound for the second upper bound which is applicable to any memoryless channel not necessarily awgn a method of random coset coding is presented for the awgn channel a tighter upper bound is obtained by employing the method of random coset coding for calculating the average spectrum of distances of the code which is required for the computation of the first upper bound

a shearlet approach to edge analysis and detection it is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges in this paper we propose a novel approach based on the shearlet transform a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges indeed unlike traditional wavelets shearlets are theoretically optimal in representing images with edges and in particular have the ability to fully capture directional and other geometrical features numerical examples demonstrate that the shearlet approach is highly effective at detecting both the location and orientation of edges and outperforms methods based on wavelets as well as other standard methods furthermore the shearlet approach is useful to design simple and effective algorithms for the detection of corners and junctions

an improved sphere packing bound for finite length codes over symmetric memoryless channels this paper derives an improved sphere packing isp bound for finite length error correcting codes whose transmission takes place over symmetric memoryless channels and the codes are decoded with an arbitrary list decoder we first review classical results i e the 1959 sphere packing sp59 bound of shannon for the gaussian channel and the 1967 sphere packing sp67 bound of shannon et al for discrete memoryless channels an improvement on the sp67 bound as suggested by valembois and fossorier is also discussed these concepts are used for the derivation of a new lower bound on the error probability of list decoding referred to as the isp bound which is uniformly tighter than the sp67 bound and its improved version the isp bound is applicable to symmetric memoryless channels and some of its applications are presented its tightness under maximum likelihood ml decoding is studied by comparing the isp bound to previously reported upper and lower bounds on the ml decoding error probability and also to computer simulations of iteratively decoded turbo like codes this paper also presents a technique which performs the entire calculation of the sp59 bound in the logarithmic domain thus facilitating the exact calculation of this bound for moderate to large block lengths without the need for the asymptotic approximations provided by shannon

mlace mlfma combined with reduced basis method for efficient wideband electromagnetic scattering from metallic targets in this paper a fast interpolation algorithm multilevel accelerated cartesian expansion mlace multilevel fast multipole algorithm mlfma reduced basis method rbm is proposed for the first time by leveraging the benefits of the mlace the classical mlfma and the rbm furthermore a novel nonblind sampling method nbsm is proposed to approximate an orthogonal subspace for the mlace mlfma rbm by integrating the empirical interpolation method eim and the error based compensation method ebcm into the nbsm we first efficiently establish a robust and precision controllable orthogonal subspace next with the accurate orthogonal subspace the mlace mlfma rbm can simplify the iterative solution process of the mlace mlfma or the mlfma into n text g matrix vector multiplications mvms and the solution of a reduced n text g dimensional linear equation n text g is the number of orthonormal vectors note that only the mvms between the far region impedance matrix and the orthogonal vector bases are calculated in comparison with the classical mlace mlfma and the mlace mlfma rbm based on different sampling methods for wideband electromagnetic em responses our proposed algorithms require less memory and cpu time and integrate higher flexibility the numerical results are provided to demonstrate the accuracy efficiency and flexibility

finding trajectories of feature points in a monocular image sequence identifying the same physical point in more than one image the correspondence problem is vital in motion analysis most research for establishing correspondence uses only two frames of a sequence to solve this problem by using a sequence of frames it is possible to exploit the fact that due to inertia the motion of an object cannot change instantaneously by using smoothness of motion it is possible to solve the correspondence problem for arbitrary motion of several nonrigid objects in a scene we formulate the correspondence problem as an optimization problem and propose an iterative algorithm to find trajectories of points in a monocular image sequence a modified form of this algorithm is useful in case of occlusion also we demonstrate the efficacy of this approach considering synthetic laboratory and real scenes

a mixed form fast multipole algorithm the fast multipole algorithm manifests in two very different forms at low frequencies and at mid frequencies each can operate in their respective regimes but are not tenable in the other regimes the paper reports on a way to factorize the green s function for fast algorithm using a mixed form the low frequency fast multipole algorithm lf fma will be used at low frequencies or the long wavelength regime and the multilevel fast multipole algorithm mlfma will be used for the mid frequencies or the shorter wavelength regime for object modeling where both long wavelength and wave physics are important we propose a mixed form fast multipole algorithm mf fma this algorithm has no low frequency break down and it can work seamlessly from static where circuit physics is important to dynamic where wave physics is important

n folded symmetries by complex moments in gabor space and their application to unsupervised texture segmentation complex moments of the gabor power spectrum yield estimates of the n folded symmetry of the local image content at different frequency scales that is they allow to detect linear rectangular hexagonal triangular and so on structures with very fine to very coarse resolutions results from experiments on the unsupervised segmentation of real textures indicate their importance for image processing applications real geometric moments computed in gabor space also provide for very powerful texture features but lack the clear geometrical interpretation of complex moments

multilevel codes and multistage decoding h imai and s hirakawa have proposed 1977 a multilevel coding method based on binary block codes that admits a staged decoding procedure the author extends the coding method to coset codes and shows how to calculate minimum squared distance and path multiplicity in terms of the norms and multiplicities of the different cosets the multilevel structure allows the redundancy in the coset selection procedure to be allocated efficiently among the different levels it also allows the use of suboptimal multistage decoding procedures that have performance complexity advantages over maximum likelihood decoding

flux based anisotropic diffusion applied to enhancement of 3 d angiogram we present a new approach to anisotropic diffusion based on a multidirectional diffusion flux the diffusion flux is decomposed in an orthogonal basis effectively enabling enhancement of contours as well as diffusion along the contours to this end we have selected a three dimensional basis that depicts the directions of principal curvature and has an interesting interpretation in the context of the vessels the diffusion function associated to each vector of the basis depends on the first order derivative of the intensity in this direction instead of the traditional norm of the smoothed gradient accordingly we present the results of a restoration of computed tomography data of the liver

template free wavelet based detection of local symmetries our goal is to detect and group different kinds of local symmetries in images in a scale and rotation invariant way we propose an efficient wavelet based method to determine the order of local symmetry at each location our algorithm relies on circular harmonic wavelets which are used to generate steerable wavelet channels corresponding to different symmetry orders to give a measure of local symmetry we use the f test to examine the distribution of the energy across different channels we provide experimental results on synthetic images biological micrographs and electron microscopy images to demonstrate the performance of the algorithm

higher order hierarchical legendre basis functions for electromagnetic modeling this paper presents a new hierarchical basis of arbitrary order for integral equations solved with the method of moments mom the basis is derived from orthogonal legendre polynomials which are modified to impose continuity of vector quantities between neighboring elements while maintaining most of their desirable features expressions are presented for wire surface and volume elements but emphasis is given to the surface elements in this case the new hierarchical basis leads to a near orthogonal expansion of the unknown surface current and implicitly an orthogonal expansion of the surface charge in addition all higher order terms in the expansion have two vanishing moments in contrast to existing formulations these properties allow the use of very high order basis functions without introducing ill conditioning of the resulting mom matrix numerical results confirm that the condition number of the mom matrix obtained with this new basis is much lower than existing higher order interpolatory and hierarchical basis functions as a consequence of the excellent condition numbers we demonstrate that even very high order mom systems e g tenth order can be solved efficiently with an iterative solver in relatively few iterations

nonconformal discretization of electric current volume integral equation with higher order hierarchical vector basis functions a nonconformal discretization of the electric current volume integral equation jvie is presented for the electromagnetic scattering analysis of inhomogeneous dielectric objects this jvie is based on higher order geometrical and current modeling where curved tetrahedral elements and higher order hierarchical vector hohv basis functions are adopted in order to implement the nonconformal discretization the half face based hohv basis function is introduced to address the nonconformal volumetric elements compared to the conventional low order conformal nonconformal vie our nonconformal hohv based jvie has the advantage of improving the computational efficiency and accuracy compared to the previous vie discretized by other higher order functions our nonconformal jvie is based on the higher order geometric modeling and the hohv bases in order to improve the efficiency the basis expansion and recombination technique is introduced to significantly accelerate the matrix filling moreover the flexibility of basis order selection is further enhanced by the mixed order schemes numerical results are given to demonstrate its accuracy efficiency and flexibility

a diagonalized multilevel fast multipole method with spherical harmonics expansion of the k space integrals diagonalization of the fast multipole method fmm for the helmholtz equation is usually achieved by expanding the multipole representation in propagating plane waves the resulting k space integral over the ewald sphere is numerically evaluated storing the k space quadrature samples of the method of moments mom basis functions constitutes a large portion of the overall memory requirements of the resulting algorithm for solving the integral equations of scattering and radiation problems in this paper it is proposed to expand the k space representation of the basis functions by spherical harmonics in order to reduce the sampling redundancy introduced by numerical quadrature rules aggregations plane wave translations and disaggregations in the realized multilevel fast multipole method mlfmm are carried out using the k space samples of a numerical quadrature rule however the incoming plane waves on the finest mlfmm level are expanded in spherical harmonics again thus due to the orthonormality of spherical harmonics the testing integrals for the individual testing functions are simplified into series over products of spherical harmonics expansion coefficients overall the resulting mlfmm can save a considerable amount of memory without compromising accuracy and numerical speed

efficient steerable circular harmonic functions two dimensional 2d filtering and pattern matching applications often involve processing of images using a set sf which includes several transformed e g rotated scaled warped versions of the same 2d function f depending on the application f may correspond to a filter kernel or a pattern in general image processing using all functions in sf poses a computational burden steerability offers a solution to the problem specifically each function in sf can be exactly expressed or closely approximated by a linear combination of a few basis or steering functions in such cases processing images by employing steering functions and obtaining linear combinations of the associated responses may be preferable over utilizing all functions in sf however employing the steering functions is still a computationally expensive process this paper presents a method to significantly improve the computational efficiency of circular harmonic functions which are the steering functions when rotation is the transformation of interest the method is presented mainly in the context of image filtering yet 2d pattern matching is also discussed the advantages of the proposed technique in relation to other techniques are presented and its efficiency is demonstrated in terms of execution time and number of operations an extension of the technique to more general cases where sf includes rotated but also otherwise transformed versions of f is also discussed highlightsvery efficient circular harmonic function chf steerable filter technique presented chf implementation several times faster than direct convolution and fft conditionally technique provides exact results as direct convolution comparisons presented for pattern matching orientation analysis and denoising

optic flow in harmony most variational optic flow approaches just consist of three constituents a data term a smoothness term and a smoothness weight in this paper we present an approach that harmonises these three components we start by developing an advanced data term that is robust under outliers and varying illumination conditions this is achieved by using constraint normalisation and an hsv colour representation with higher order constancy assumptions and a separate robust penalisation our novel anisotropic smoothness is designed to work complementary to the data term to this end it incorporates directional information from the data constraints to enable a filling in of information solely in the direction where the data term gives no information yielding an optimal complementary smoothing behaviour this strategy is applied in the spatial as well as in the spatio temporal domain finally we propose a simple method for automatically determining the optimal smoothness weight this method bases on a novel concept that we call optimal prediction principle opp it states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence the benefits of our optic flow in harmony ofh approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used middlebury optic flow benchmark

a flexible new technique for camera calibration we propose a flexible technique to easily calibrate a camera it only requires the camera to observe a planar pattern shown at a few at least two different orientations either the camera or the planar pattern can be freely moved the motion need not be known radial lens distortion is modeled the proposed procedure consists of a closed form solution followed by a nonlinear refinement based on the maximum likelihood criterion both computer simulation and real data have been used to test the proposed technique and very good results have been obtained compared with classical techniques which use expensive equipment such as two or three orthogonal planes the proposed technique is easy to use and flexible it advances 3d computer vision one more step from laboratory environments to real world use

multidimensional orientation estimation with applications to texture analysis and optical flow the problem of detection of orientation in finite dimensional euclidean spaces is solved in the least squares sense the theory is developed for the case when such orientation computations are necessary at all local neighborhoods of the n dimensional euclidean space detection of orientation is shown to correspond to fitting an axis or a plane to the fourier transform of an n dimensional structure the solution of this problem is related to the solution of a well known matrix eigenvalue problem the computations can be performed in the spatial domain without actually doing a fourier transformation along with the orientation estimate a certainty measure based on the error of the fit is proposed two applications in image analysis are considered texture segmentation and optical flow the theory is verified by experiments which confirm accurate orientation estimates and reliable certainty measures in the presence of noise the comparative results indicate that the theory produces algorithms computing robust texture features as well as optical flow

anisotropic morphological filters with spatially variant structuring elements based on image dependent gradient fields this paper deals with the theory and applications of spatially variant discrete mathematical morphology we review and formalize the definition of spatially variant dilation erosion and opening closing for binary and gray level images using exclusively the structuring function without resorting to complement this theoretical framework allows to build morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant direction of the structures in the image the shape and orientation of the structuring element at each pixel are extracted from the image under study the orientation is given by means of a diffusion process of the average square gradient field which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant edges of the objects the proposed filters are used on binary and gray level images for enhancement of anisotropic features such as coherent flow like structures results of spatially variant erosions dilations and openings closings based filters prove the validity of this theoretical sound and novel approach