computationally efficient sine wave synthesis for acoustic waveform processing methods and apparatus for reducing discontinuities between frames of sinusoidally modeled acoustic waveforms such as speech which occur when sampling at low frame rates a fast fourier transform based overlap add technique is applied to amplitude frequency and phase components of sinusoidal waves after frame to frame sine wave matching has been performed matched sine wave amplitudes and frequencies are linearly interpolated and mid point phase is estimated such that the mid frame sine wave is best fit to the most recent half frame segments of the lagging and leading sine waves synthetic mid frame sine waves are generated using the interpolated amplitude and frequency and estimated phase values synthesized acoustic waveforms of high quality from original source waveforms can be produced in sinusoidal analysis synthesis operations at coding frame rates of 50 hz and lower the methods and devices disclosed herein are particularly useful for computationally efficient coding and synthesis of speech waveforms

a local algorithm for the computation of image velocity via constructive interference of global fourier components a novel fourier based technique for local motion detection from image sequences is proposed in this method the instantaneous velocities of local image points are inferred directly from the global 3d fourier components of the image sequence this is done by selecting those velocities for which the superposition of the corresponding fourier gratings leads to constructive interference at the image point hence image velocities can be assigned locally even though position is computed from the phases and amplitudes of global fourier components spanning the whole image sequence that have been filtered based on the motion constraint equation reducing certain aperture effects typically arising from windowing in other methods regularization is introduced for sequences having smooth flow fields aperture effects and their effect on optic flow regularization are investigated in this context the algorithm is tested on both synthetic and real image sequences and the results are compared to those of other local methods finally we show that other motion features i e motion direction can be computed using the same algorithmic framework without requiring an intermediate representation of local velocity which is an important characteristic of the proposed method

volumetric texture segmentation by discriminant feature selection and multiresolution classification in this paper a multiresolution volumetric texture segmentation m vts algorithm is presented the method extracts textural measurements from the fourier domain of the data via subband filtering using an orientation pyramid wilson and spann 1988 a novel bhattacharyya space based on the bhattacharyya distance is proposed for selecting the most discriminant measurements and producing a compact feature space an oct tree is built of the multivariate features space and a chosen level at a lower spatial resolution is first classified the classified voxel labels are then projected to lower levels of the tree where a boundary refinement procedure is performed with a three dimensional 3 d equivalent of butterfly filters the algorithm was tested with 3 d artificial data and three magnetic resonance imaging sets of human knees with encouraging results the regions segmented from the knees correspond to anatomical structures that can be used as a starting point for other measurements such as cartilage extraction

a generalized wavelet transform for fourier analysis the multiresolution fourier transform and its application to image and audio signal analysis a wavelet transform specifically designed for fourier analysis at multiple scales is described and shown to be capable of providing a local representation which is particularly well suited to segmentation problems it is shown that by an appropriate choice of analysis window and sampling intervals it is possible to obtain a fourier representation which can be computed efficiently and overcomes the limitations of using a fixed scale of window yet by virtue of its symmetry properties allows simple estimation of such fundamental signal parameters as instantaneous frequency and onset time position the transform is applied to the segmentation of both image and audio signals demonstrating its power to deal with signal events which are localized in either time space or frequency feature extraction and segmentation are performed through the introduction of a class of multiresolution markov models whose parameters represent the signal events underlying the segmentation

a combined markov random field and wave packet transform based approach for image segmentation the author formulates a novel segmentation algorithm which combines the use of markov random field models for image modeling with the use of the discrete wavepacket transform for image analysis image segmentations are derived and refined at a sequence of resolution levels using as data selected wave packet transform images or channels the segmentation algorithm is compared with nonmultiresolution markov random field based image segmentation algorithms in the context of synthetic image example problems and found to be both significantly more efficient and effective

encoding and synthesis of tonal audio signals using dominant sinusoids and a vector quantized residual tonal signal tonal audio signals can be modeled as a sum of sinusoids with time varying frequencies amplitudes and phases an efficient encoder and synthesizer of tonal audio signals is disclosed the encoder determines time varying frequencies amplitudes and optionally phases for a restricted number of dominant sinusoid components of the tonal audio signal to form a dominant sinusoid parameter sequence these components are removed from the tonal audio signal to form a residual tonal signal the residual tonal signal is encoded using a residual tonal signal encoder rtse in one embodiment the rtse generates a vector quantization codebook vqc and residual codebook sequence rcs the vqc may contain time domain residual waveforms selected from the residual tonal signal synthetic time domain residual waveforms with magnitude spectra related to the residual tonal signal magnitude spectrum encoding vectors or a combination of time domain waveforms and magnitude spectrum encoding vectors the tonal audio signal synthesizer uses a sinusoidal oscillator bank to synthesize a set of dominant sinusoid components from the dominant sinusoid parameter sequence generated during encoding in one embodiment a residual tonal signal is synthesized using a vqc and rcs generated by the rtse during encoding if the vqc includes time domain waveforms an interpolating residual waveform oscillator may be used to synthesize the residual tonal signal the synthesized dominant sinusoids and synthesized residual tonal signal are summed to form the synthesized tonal audio signal

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

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

musical synthesizer combining deterministic and stochastic waveforms a musical sound analyzer and synthesizer uses a model that considers a sound to be composed of two types of elements a deterministic component plus a stochastic component the deterministic component is represented as a series of sinusoids with an amplitude and a frequency function for each sinusoid the stochastic component is represented as a series of magnitude spectral envelopes from this representation sounds can be synthesized that in the absence of modifications can behave as perceptual identities that is they are perceptually equal to the original sound in addition stored representations of sounds can be easily modified in a musical synthesizer to create a wide variety of new sounds

recognition by symmetry derivatives and the generalized structure tensor we suggest a set of complex differential operators that can be used to produce and filter dense orientation tensor fields for feature extraction matching and pattern recognition we present results on the invariance properties of these operators that we call symmetry derivatives these show that in contrast to ordinary derivatives all orders of symmetry derivatives of gaussians yield a remarkable invariance they are obtained by replacing the original differential polynomial with the same polynomial but using ordinary coordinates x and y corresponding to partial derivatives moreover the symmetry derivatives of gaussians are closed under the convolution operator and they are invariant to the fourier transform the equivalent of the structure tensor representing and extracting orientations of curve patterns had previously been shown to hold in harmonic coordinates in a nearly identical manner as a result positions orientations and certainties of intricate patterns e g spirals crosses parabolic shapes can be modeled by use of symmetry derivatives of gaussians with greater analytical precision as well as computational efficiency since gaussians and their derivatives are utilized extensively in image processing the revealed properties have practical consequences for local orientation based feature extraction the usefulness of these results is demonstrated by two applications 1 tracking cross markers in long image sequences from vehicle crash tests and 2 alignment of noisy fingerprints

video orbits of the projective group a simple approach to featureless estimation of parameters we present direct featureless methods for estimating the eight parameters of an exact projective homographic coordinate transformation to register pairs of images together with the application of seamlessly combining a plurality of images of the same scene resulting in a single image or new image sequence of greater resolution or spatial extent the approach is exact for two cases of static scenes 1 images taken from the same location of an arbitrary three dimensional 3 d scene with a camera that is free to pan tilt rotate about its optical axis and zoom or 2 images of a flat scene taken from arbitrary locations the featureless projective approach generalizes interframe camera motion estimation methods that have previously used a camera model which lacks the degrees of freedom to exactly characterize such phenomena as camera pan and tilt and or which have relied upon finding points of correspondence between the image frames the featureless projective approach which operates directly on the image pixels is shown to be superior in accuracy and the ability to enhance the resolution the proposed methods work well on image data collected from both good quality and poor quality video under a wide variety of conditions sunny cloudy day night these new fully automatic methods are also shown to be robust to deviations from the assumptions of static scene and no parallax

hierarchical image segmentation by multi dimensional clustering and orientation adaptive boundary refinement in this paper we present a new multi dimensional segmentation algorithm we propose an orientation adaptive boundary estimation process embedded in a multiresolution pyramidal structure that allows the use of different clustering procedures without spatial connectivity constraints the presence of noise in the feature space mainly produced by modeling errors causes a class overlap which can be reduced in a multiresolution pyramid at the coarsest resolution level the separation between the different classes is increased and the within class variance reduced thus at this level the classes can be obtained with different multi dimensional clustering algorithms without connectivity constraints small and scattered classes as well as isolated class labels are reassigned to their neighborhood by a process which guarantees the spatial connectivity the resolution is then increased by projecting down the class labels at each level the borders are improved by reassigning the boundary pixels to their spatially closest class however the class uncertainty astride the borders has first to be reduced and we propose to do this by means of orientation adaptive butterfly shaped filters this refinement process further eliminates spatially misclassified pixels produced by the unconstrained clustering experimental results show that similarly accurate boundaries are obtained with different clustering algorithms for various test images

the bandcount increment scenario ii interior structures bifurcation structures in the two dimensional parameter spaces formed by chaotic attractors alone are still far away from being understood completely in a series of three papers we investigate th

bounding multiple gaussians uncertainty with application to object tracking this paper proves the uncertainty bound for the multiple gaussian functions termed multiple gaussians uncertainty mgu which significantly generalizes the uncertainty principle for the single gaussian function first as a theoretical contribution we prove that the momentum velocity and position for the sum of multiple gaussians wave function are theoretically bounded second as for a practical application we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework by integrating mgu with a given object tracker we demonstrate that uncertainty principle can provide remarkable robustness in tracking extensive experiments are done to show that the proposed mgu can significantly help base trackers overcome the object drifting and reach state of the art results

a cellular coevolutionary algorithm for image segmentation clustering is inherently a difficult problem both with respect to the definition of adequate models as well as to the optimization of the models we present a model for the cluster problem that does not need knowledge about the number of clusters a priori this property is among others useful in the image segmentation domain which we especially address further we propose a cellular coevolutionary algorithm for the optimization of the model within this scheme multiple agents are placed in a regular two dimensional 2 d grid representing the image which imposes neighboring relations on them the agents cooperatively consider pixel migration from one agent to the other in order to improve the homogeneity of the ensemble of the image regions they represent if the union of the regions of neighboring agents is homogeneous then the agents form alliances on the other hand if an agent discovers a deviant subject it isolates the subject in the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms the efficiency can easily be improved by exploiting the intrinsic parallelism of the proposed method

a theoretical framework for convex regularizers in pde based computation of image motion many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy typically these energy functionals consist of two terms a data term which requires e g that a brightness constancy assumption holds and a regularizer that encourages global or piecewise smoothness of the flow field in this paper we present a systematic classification of rotation invariant convex regularizers by exploring their connection to diffusion filters for multichannel images this taxonomy provides a unifying framework for data driven and flow driven isotropic and anisotropic as well as spatial and spatio temporal regularizers while some of these techniques are classic methods from the literature others are derived here for the first time we prove that all these methods are well posed they posses a unique solution that depends in a continuous way on the initial data an interesting structural relation between isotropic and anisotropic flow driven regularizers is identified and a design criterion is proposed for constructing anisotropic flow driven regularizers in a simple and direct way from isotropic ones its use is illustrated by several examples

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

mir an approach to robust clustering application to range image segmentation this paper describes an unsupervised region merging technique based on a novel robust statistical test the merging decision is derived from the mutual inlier ratio mir of adjacent regions this ratio is computed using robust regression techniques and a novel method to estimate the robust scale of the gaussian distribution a discrimination value to recognize identical gaussian distributions with the mir is derived theoretically as a function of the sizes of the compared sets the presented method to test distributions is compared with the established kolmogorov smirnov test and implemented into a segmentation algorithm for planar range images the iterative region growing technique is evaluated using an established framework for range image segmentation comparison involving 60 real range images the evaluation incorporates a comparison with four state of the art algorithms and gives an experimental demonstration of the need for robust methods capable of handling noisy data in real applications

on detection of multi band chaotic attractors in this work we present two numerical methods for the detection of the number of bands of a multi band chaotic attractor the first method is more efficient but can be applied only for dynamical systems with a continuous system function whereas the second one is applicable for dynamical systems with a discontinuous system function as well using the developed methods we investigate a one dimensional piecewise linear map and report for both cases of a continuous and a discontinuous system functions some new bifurcation scenarios involving multi band chaotic attractors

segmentation of textured images and gestalt organization using spatial spatial frequency representations the generic issue of clustering grouping is addressed recent research both in computer and human vision suggests the use of joint spatial spatial frequency s sf representations the spectrogram the difference of gaussians representation the gabor representation and the wigner distribution are discussed and compared it is noted that the wigner distribution gives superior joint resolution experimental results in the area of texture segmentation and gestalt grouping using the wigner distribution are presented proving the feasibility of using s sf representations for low level early preattentive vision

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

region competition unifying snakes region growing and bayes mdl for multiband image segmentation we present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition this algorithm is derived by minimizing a generalized bayes minimum description length mdl criterion using the variational principle the algorithm is guaranteed to converge to a local minimum and combines aspects of snakes balloons and region growing the classic snakes balloons and region growing algorithms can be directly derived from our approach we provide theoretical analysis of region competition including accuracy of boundary location criteria for initial conditions and the relationship to edge detection using filters it is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images color images and texture images the novel color model allows us to eliminate intensity gradients and shadows thereby obtaining segmentation based on the albedos of objects it also helps detect highlight regions

finite prolate spheroidal sequences and their applications i generation and properties the finite prolate spheroidal sequences are those finite sequences which have extremal energy concentrations both in space and spatial frequency the first part of the paper is devoted to a study of the eigenvalue problem defining the sequences this reveals those fundamental properties of the sequences which are relevant in image processing applications and shows how they can be generated efficiently a new version of the sampling theorem is also demonstrated

optimal generating kernels for image pyramids by piecewise fitting a novel class of generating kernels for image pyramids is introduced when these kernels are convolved with intensity functions of images continuous piecewise surfaces composed of polynomial tensor products are fitted to the intensity functions the fittings are optimal in the sense that the mean square error between them and the original intensity functions is minimized two members of the class are introduced and symmetry normalization unimodality and equal contribution properties are proved these kernels possess attractive properties such as small window size fast inverse transformation and minimum error experiments show that they compare favorably with existing ones in terms of mean square error

on the optimality of solutions of the max product belief propagation algorithm in arbitrary graphs graphical models such as bayesian networks and markov random fields mrfs represent statistical dependencies of variables by a graph the max product belief propagation algorithm is a local message passing algorithm on this graph that is known to converge to a unique fixed point when the graph is a tree furthermore when the graph is a tree the assignment based on the fixed point yields the most probable values of the unobserved variables given the observed ones good empirical performance has been obtained by running the max product algorithm or the equivalent min sum algorithm on graphs with loops for applications including the decoding of turbo codes except for two simple graphs cycle codes and single loop graphs there has been little theoretical understanding of the max product algorithm on graphs with loops here we prove a result on the fixed points of max product on a graph with arbitrary topology and with arbitrary probability distributions discrete or continuous valued nodes we show that the assignment based on a fixed point is a neighborhood maximum of the posterior probability the posterior probability of the max product assignment is guaranteed to be greater than all other assignments in a particular large region around that assignment the region includes all assignments that differ from the max product assignment in any subset of nodes that form no more than a single loop in the graph in some graphs this neighborhood is exponentially large we illustrate the analysis with examples

inverse transform narrow band broad band sound synthesis an additive sound synthesis process for generating complex realistic sounds is realized in a computationally efficient manner in accordance with one aspect of the invention polyphony is efficiently achieved by dosing the energy of a given partial between separate transform sums corresponding to different channels in accordance with another aspect of the invention noise 87 is injected by randomly perturbing the phase of the sound either on a per partial basis or on a transform sum basis in the latter instance the phase is perturbed in different regions of the spectrum to a degree determined by the amount of energy present in the respective regions of the spectrum in accordance with yet another aspect of the invention a transform sum 83 representing a sound is processed in the transform domain to achieve with great economy effects achievable only at much greater expense outside the transform domain other transforms besides the fourier transform may be used to advantage for example use of the hartley transform produces comparable results but allows transforms to be computed at approximately twice the speed as the fourier transform

processing of acoustic waveforms a sinusoidal model for acoustic waveforms is applied to develop a new analysis synthesis technique which characterizes a waveform by the amplitudes frequencies and phases of component sine waves these parameters are estimated from a short time fourier transform rapid changes in the highly resolved spectral components are tracked using the concept of birth and death of the underlying sine waves the component values are interpolated from one frame to the next to yield a representation that is applied to a sine wave generator the resulting synthetic waveform preserves the general waveform shape and is perceptually indistinguishable from the original furthermore in the presence of noise the perceptual characteristics of the waveform as well as the noise are maintained the method and devices disclosed herein are particularly useful in speech coding time scale modification frequency scale modification and pitch modification

the bandcount increment scenario i basic structures bifurcation structures in two dimensional parameter spaces formed only by chaotic attractors are still far away from being understood completely in a series of three papers we investigate the chaotic domain without periodic inclusions for a map which is considered by many authors as some kind of one dimensional canonical form for discontinuous maps in the first part we report a novel bifurcation scenario formed by crises bifurcations which includes multi band chaotic attractors with arbitrary high bandcounts and determines the basic structure of the chaotic domain

a computational framework and an algorithm for the measurement of visual the robust measurement of visual motion from digitized image sequences has been an important but difficult problem in computer vision this paper describes a hierarchical computational framework for the determination of dense displacement fields from a pair of images and an algorithm consist ent with that framework our framework is based on the separation of the image intensity information as well as the process of measuring motion according to scale the large scale intensity information is first used to obtain rough estimates of image motion which are then refined by using intensity information at smaller scales the estimates are in the form of displacement or velocity vectors for pixels and are accompanied by a direction dependent confidence measure a smoothness constraint is employed to propagate the measurements with high confidence to their neigboring areas where the confidences are low at all levels the computations are pixel parallel uniform across the image and based on information from a small neighborhood of a pixel for our algorithm the local displacement vectors are determind by mini mizing the sum of squared differences ssd of intensities the confidence measures are derived from the shape of the ssd surface and the smoothness constraint is cast in the form of energy minimization results of applying our algorithm to pairs of real images are included in addition to our own

texture segmentation using gaussian markov random fields and neural oscillator networks we propose an image segmentation method based on texture analysis our method is composed of two parts the first part determines a novel set of texture features derived from a gaussian markov random fields gmrf model unlike a gmrf based approach our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori the second part is a 2d array of locally excitatory globally inhibitory oscillator networks legion after being filtered for noise suppression features are used to determine the local couplings in the network when legion runs the oscillators corresponding to the same texture tend to synchronize whereas different texture regions tend to correspond to distinct phases in simulations a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks we provide results on real texture images to demonstrate the performance of our method

uncertainty relations and time frequency distributions for unsharp observables this paper deals with a new framework in analyzing the formal mathematical correspondence between quantum mechanics and time frequency representations of a signal it is also shown that joint time frequency distributions have a close link with heisenberg uncertainty relations if the observables are taken as fuzzy entities this result contradicts the arguments of cohen ieee proc 77 7 941 1989 regarding the time frequency distributions and the uncertainty relation it is postulated that these mechanisms will be of crucial importance in highly fragmented computation structures such as neural networks as they may exhibit a strong mutual interaction between data and operator

sharp inequalities for geometric fourier transform and associated ambiguity function abstract the geometric fourier transform defined using clifford algebra plays an increasingly active role in modern data analysis in particular for color image processing however due to the non commutativity it is hard to obtain many important analytic properties in this paper we prove several important sharp inequalities including the hausdorff young inequality and its converse pitt s inequality and lieb s inequality for clifford ambiguity functions with these inequalities several uncertainty principles with optimal constants are derived for the geometric fourier transform most results in this paper are new even in the easier case i e the quaternion setting the method developed here also works for more general geometric fourier transforms

determining optical flow optical flow cannot be computed locally since only one independent measurement is available from the image sequence at a point while the flow velocity has two components a second constraint is needed a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image an iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences the algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time it is also insensitive to quantization of brightness levels and additive noise examples are included where the assumption of smoothness is violated at singular points or along lines in the image

finite prolate spheroidal sequences and their applications ii image feature description and segmentation for pt i see ibid vol 9 no 6 p 787 1987 the problem of uncertainty in image feature description is discussed and it is shown how finite prolate spheroidal sequences can be used in the construction of feature descriptions that combine spatial and frequency domain locality in an optimal way methods of constructing such optimal feature sets which are suitable for graphical implementation are described and some generalizations of the quadtree concept are presented these methods are illustrated by examples from image processing applications including feature extraction and texture description the problem of image segmentation is discussed and the importance of scale invariance in overcoming the limitations imposed by uncertainty is demonstrated a novel texture segmentation algorithm that is based on a combination of the new feature description and multiresolution techniques is described and shown to give accurate segmentations on a range of synthetic and natural textures

frame representations for texture segmentation we introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2 d envelope detection we describe and compare two algorithms for envelope detection based on 1 the hilbert transform and 2 zero crossings we present criteria for filter selection and discuss quantitatively their effect on feature extraction the performance of our method is demonstrated experimentally on samples of both natural and synthetic textures

circular mellin features for texture segmentation texture is an important cue in region based segmentation of images in this paper we provide insight into the development of a new set of distortion invariant texture operators these circular mellin operators are invariant to both scale and orientation of the target and represent the spectral decomposition of the image scene in the polar log coordinate system coupled with the unique shift invariance property of the correlator architecture we show that these circular mellin operators can be used for rotation and scale invariant feature extraction we note that while these feature extractors have a functional form that is similar to the gabor operators they have distortion invariant characteristics unlike the gabor functions that make them more suitable for texture segmentation a detailed analytical description of these operators and segmentation results to highlight their salient properties are presented

computation of component image velocity from local phase information we present a technique for the computation of 2d component velocity from image sequences initially the image sequence is represented by a family of spatiotemporal velocity tuned linear filters component velocity computed from spatiotemporal responses of identically tuned filters is expressed in terms of the local first order behavior of surfaces of constant phase justification for this definition is discussed from the perspectives of both 2d image translation and deviations from translation that are typical in perspective projections of 3d scenes the resulting technique is predominantly linear efficient and suitable for parallel processing moreover it is local in space time robust with respect to noise and permits multiple estimates within a single neighborhood promising quantiative results are reported from experiments with realistic image sequences including cases with sizeable perspective deformation

the uncertainty principle in image processing the uncertainty principle is recognized as one of the fundamental results in signal processing its role in inference is however less well known outside of quantum mechanics it is the aim of this paper to provide a unified approach to the problem of uncertainty in image processing it is shown that uncertainty can be derived from the fundamental constraints on the process of vision the requirements for class defining operations which are both shift invariant and insensitive to changes in illumination it is thus shown that uncertainty plays a key role in the language of vision since it affects the choice of both the alphabet the elementary signals and the syntax the inferential structure of vision the report is concluded with a number of practical illustrations of these ideas taken from such image processing tasks as enhancement data compression and segmentation

the hermite transform theory the author introduces a scheme for the local processing of visual information called the hermite transform the problem is addressed from the point of view of image coding and therefore the scheme is presented as an analysis resynthesis system the objectives of the present work however are not restricted to coding the analysis part is designed so that it can also serve applications in the area of computer vision indeed derivatives of gaussians which have found widespread application in feature detection over the past few years play a central role in the hermite analysis it is also argued that the proposed processing scheme is in close agreement with current insight into the image processing that is carried out by the human visual system in particular it is demonstrated that the hermite transform is in better agreement with human visual modeling than gabor expansions

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

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

methods for generating the voiced portion of speech signals the pitch estimation method is improved sub integer resolution pitch values are estimated in making the initial pitch estimate the sub integer pitch values are preferably estimated by interpolating intermediate variables between integer values pitch regions are used to reduce the amount of computation required in making the initial pitch estimate pitch dependent resolution is used in making the initial pitch estimate with higher resolution being used for smaller values of pitch the accuracy of the voiced unvoiced decision is improved by making the decision dependent on the energy of the current segment relative to the energy of recent prior segments if the relative energy is low the current segment favors an unvoiced decision if high it favors a voiced decision voiced harmonics are generated using a hybrid approach some voiced harmonics are generated in the time domain whereas the remaining harmonics are generated in the frequency domain this preserves much of the computational savings of the frequency domain approach while at the same time improving speech quality voiced harmonics generated in the frequency domain are generated with higher frequency accuracy the harmonics are frequency scaled transformed into the time domain with a discrete fourier transform interpolated and then time scaled

texture segmentation using fractal dimension this paper deals with the problem of recognizing and segmenting textures in images for this purpose the authors employ a technique based on the fractal dimension fd and the multi fractal concept six fd features are based on the original image the above average high gray level image the below average low gray level image the horizontally smoothed image the vertically smoothed image and the multi fractal dimension of order two a modified box counting approach is proposed to estimate the fd in combination with feature smoothing in order to reduce spurious regions to segment a scene into the desired number of classes an unsupervised k means like clustering approach is used mosaics of various natural textures from the brodatz album as well as microphotographs of thin sections of natural rocks are considered and the segmentation results to show the efficiency of the technique supervised techniques such as minimum distance and k nearest neighbor classification are also considered the results are compared with other techniques

origins of illusory percepts in digital images abstract here we show the relation between illusory percepts and statistical regularities across scales and orientations to this aim the performance of a computational model for the partitioning of statistical regularities is analyzed on several tasks such as long range boundary completion phase induced contour detection as well as shape and size illusions the system for the automatically learned partitioning of statistical regularities in 2d images is based on a sophisticated band pass filtering operation with fixed scale and orientation sensitivity experimental results are provided to illustrate this analysis on several examples i kanizsa type subjective figures ii phase induced subjective contours iii the zollner illusion and iv the muller lyer illusion

codimension three bifurcations explanation of the complex one two and three dimensional bifurcation structures in nonsmooth maps many physical and engineering systems exhibit cascades of periodic attractors arranged in period increment and period adding sequences as a parameter is varied such systems have been found to yield piecewise smooth maps and in some cases the obtained map is discontinuous by investigating the normal form of such maps we have detected a type of codimension three bifurcation which serves as the organizing center of periodic and aperiodic dynamics in the parameter space the results will help in understanding the occurrence and structure of such cascades observed in many nonsmooth systems in science and engineering

on the concept of attractor this note proposes a definition for the concept of attractor based on the probable asymptotic behavior of orbits the definition is sufficiently broad so that every smooth compact dynamical system has at least one attractor

the bandcount increment scenario iii deformed structures bifurcation structures in two dimensional parameter spaces formed by chaotic attractors alone are still a long way from being understood completely in a series of three papers we investigated the chaotic domain without periodic inclusions for a map which is considered by many authors as some kind of one dimensional canonical form for discontinuous maps in part i the basic structures in the chaotic region are explained by the bandcount increment scenario in part ii fine self similar substructures nested into the bandcount increment scenario are explained by the bandcount adding and doubling scenarios nested into each other ad infinitum hereby we fixed in both previous parts one of the parameters to a non generic value and studied the remaining two dimensional parameter subspace in this part iii finally we investigated the structures under variation of this third parameter remarkably this step is the most important with respect to practical applications since it cannot be expected that these operate exactly at the previously investigated specific value

relaxation using models from quantum mechanics abstract we make an analogy between a single pixel and its first order neighbours in an image and single particle models from quantum physics the analogy is created by identifying the observed grey levels and the noise variance with the potential function and the energy of the physical system the approach is used to set up novel and strongly data dependent local weights in relaxation schemes both image restoration and segmentation are considered the new methods competed favourably with the besag icm and rosenfeld relaxation on several simulated and real images