fingerprint orientation field reconstruction by weighted discrete cosine transform orientation field represents the topological structure of the interleaved ridge and valley flows in fingerprint images although a number of methods have been proposed for orientation estimation reliable computation of orientation field is still a challenging problem due to the poor quality of some fingerprints this paper proposes a method to reconstruct fingerprint orientation field by weighted discrete cosine transform dct first the dct functions are used to build the basis atoms for linear representation of orientation field then the dct basis atoms of low and high orders are combined with the weights determined by singularity measurements for orientation reconstruction the weighted dct model is further extended for partial fingerprints to gradually and iteratively reconstruct the orientations in noisy or missing parts of fingerprints the proposed method can perform well in smoothing out the noise while maintaining the orientation details in singular regions extensive experiments have been done to compare the proposed method with some existing methods on nist and fvc fingerprint databases in terms of the reconstruction accuracy of orientation field fingerprint indexing performance and fingerprint recognition accuracy experimental results illustrate the effectiveness of the proposed method in reconstructing orientation fields especially for poor quality and partial fingerprints

image filtering edge detection and edge tracing using fuzzy reasoning we characterize the problem of detecting edges in images as a fuzzy reasoning problem the edge detection problem is divided into three stages filtering detection and tracing images are filtered by applying fuzzy reasoning based on local pixel characteristics to control the degree of gaussian smoothing filtered images are then subjected to a simple edge detection algorithm which evaluates the edge fuzzy membership value for each pixel based on local image characteristics finally pixels having high edge membership are traced and assembled into structures again using fuzzy reasoning to guide the tracing process the filtering detection and tracing algorithms are tested on several test images comparison is made with a standard edge detection technique

the hermite transform applications it is demonstrated how the hermite transform can be used for image coding and analysis hierarchical coding structures based on increasingly specified basic patterns i e general 2 d patterns general 1 d patterns and specific 1 d patterns such as edges and corners are presented in the image coding application the relation with existing pyramid coders is described a new coding scheme based on local one dimensional image approximations is introduced in the image analysis application the relation between the hermite transform and existing line edge detection schemes is described it is shown that by concentrating on more specific patterns the coding efficiency can be increased since fewer coefficients have to be coded meanwhile sufficient descriptive power can be maintained for approximating the most interesting features in natural images

complete discrete 2 d gabor transforms by neural networks for image analysis and compression a three layered neural network is described for transforming two dimensional discrete signals into generalized nonorthogonal 2 d gabor representations for image analysis segmentation and compression these transforms are conjoint spatial spectral representations which provide a complete image description in terms of locally windowed 2 d spectral coordinates embedded within global 2 d spatial coordinates in the present neural network approach based on interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights the network finds coefficients for complete conjoint 2 d gabor transforms without restrictive conditions in wavelet expansions based on a biologically inspired log polar ensemble of dilations rotations and translations of a single underlying 2 d gabor wavelet template image compression is illustrated with ratios up to 20 1 also demonstrated is image segmentation based on the clustering of coefficients in the complete 2 d gabor transform

local intensity variation analysis for iris recognition as all emerging biometric for human identification iris recognition has received increasing attention in recent years this paper makes an attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image in our framework a set of one dimensional 1d intensity signals is constructed to contain the most important local variations of the original 2d iris image gaussian hermite moments of such intensity signals reflect to a large extent their various spatial modes and are used as distinguishing features a resulting high dimensional feature vector is mapped into a low dimensional subspace using fisher linear discriminant and then the nearest center classifier based on cosine similarity measure is adopted for classification extensive experimental results show that the proposed method is effective and encouraging c 2004 pattern recognition society published by elsevier ltd all rights reserved

iris recognition based on bidimensional empirical mode decomposition and fractal dimension as the demand for information security increases more attention is being paid to biometrics based automated personal identification one of the most promising current biometric techniques is based on the human iris this paper attempts to detect shape information from the iris by analyzing local intensity variations of an iris image the methodology involves extraction of iris features using bidimensional empirical mode decomposition bemd and fractal dimension after the preprocessing procedure the normalized effective iris image is decomposed into 2d intrinsic mode function imf components at different spatial frequencies by bidimensional empirical mode decomposition then the texture features of each intrinsic mode function image are obtained via the differential box counting method to evaluate the efficacy of the proposed approach three different similarity measures used in recognition are adopted the experimental results using the casia and ice iris databases show that the schema presented achieves promising results for iris recognition

minimal perfect hashing a competitive method for indexing internal memory a perfect hash function phf is an injective function that maps keys from a set s to unique values since no collisions occur each key can be retrieved from a hash table with a single probe a minimal perfect hash function mphf is a phf with the smallest possible range that is the hash table size is exactly the number of keys in s mphfs are widely used for memory efficient storage and fast retrieval of items from static sets differently from other hashing schemes mphfs completely avoid the problem of wasted space and wasted time to deal with collisions until recently the amount of space to store an mphf description for practical implementations found in the literature was o logn bits per key and therefore similar to the overhead of space of other hashing schemes recent results on mphfs presented in the literature changed this scenario an mphf can now be described by approximately 2 6 bits per key the objective of this paper is to show that mphfs are after the new recent results a good option to index internal memory when static key sets are involved and both successful and unsuccessful searches are allowed we have shown that mphfs provide the best tradeoff between space usage and lookup time when compared with other open addressing and chaining hash schemes such as linear hashing quadratic hashing double hashing dense hashing cuckoo hashing sparse hashing hopscotch hashing chaining with move to front heuristic and exact fit we considered lookup time for successful and unsuccessful searches in two scenarios i the mphf description fits in the cpu cache and ii the mphf description does not fit entirely in the cpu cache considering lookup time the minimal perfect hashing outperforms the other hashing schemes in the two scenarios and in the first scenario the performance is better even when the compared methods leave more than 80 of the hash table entries free considering space overhead the amount of used space other than the key value pairs the minimal perfect hashing is within a factor of o logn bits lower than the other hashing schemes for both scenarios

action recognition based on overcomplete independent components analysis existing works on action recognition rely on two separate stages 1 designing hand designed features or learning features from video data 2 classifying features using a classifier such as svm or adaboost motivated by two observations 1 independent component analysis ica is capable of encoding intrinsic features underlying video data and 2 videos of different actions can be easily distinguished by their intrinsic features we propose a simple but effective action recognition framework based on the recently proposed overcomplete ica model after a set of overcomplete ica basis functions are learned from the densely sampled 3d patches from training videos for each action a test video is classified as the class whose basis functions can reconstruct the sampled 3d patches from the test video with the smallest reconstruction error the experimental results on five benchmark datasets demonstrate that the proposed approach outperforms several state of the art works

topological microstructure analysis using persistence landscapes phase separation mechanisms can produce a variety of complicated and intricate microstructures which often can be difficult to characterize in a quantitative way in recent years a number of novel topological metrics for microstructures have been proposed which measure essential connectivity information and are based on techniques from algebraic topology such metrics are inherently computable using computational homology provided the microstructures are discretized using a thresholding process however while in many cases the thresholding is straightforward noise and measurement errors can lead to misleading metric values in such situations persistence landscapes have been proposed as a natural topology metric common to all of these approaches is the enormous data reduction which passes from complicated patterns to discrete information it is therefore natural to wonder what type of information is actually retained by the topology in the present paper we demonstrate that averaged persistence landscapes can be used to recover central system information in the cahn hilliard theory of phase separation more precisely we show that topological information of evolving microstructures alone suffices to accurately detect both concentration information and the actual decomposition stage of a data snapshot considering that persistent homology only measures discrete connectivity information regardless of the size of the topological features these results indicate that the system parameters in a phase separation process affect the topology considerably more than anticipated we believe that the methods discussed in this paper could provide a valuable tool for relating experimental data to model simulations

nonlocal hierarchical dictionary learning using wavelets for image denoising exploiting the sparsity within representation models for images is critical for image denoising the best currently available denoising methods take advantage of the sparsity from image self similarity pre learned and fixed representations most of these methods however still have difficulties in tackling high noise levels or noise models other than gaussian in this paper the multiresolution structure and sparsity of wavelets are employed by nonlocal dictionary learning in each decomposition level of the wavelets experimental results show that our proposed method outperforms two state of the art image denoising algorithms on higher noise levels furthermore our approach is more adaptive to the less extensively researched uniform noise

a randomized subdivision algorithm for determining the topology of nodal sets topology is a natural mathematical tool for quantifying complex structures in many applications such as for example in the context of phase field models in materials science the structures of interest arise as sub or superlevel sets of continuous functions i e as nodal domains from a computational point of view any attempt at constructing a truthful representation of the topology of nodal domains has to involve a discretization step and it is natural to wonder whether this step introduces topological artifacts in this paper we present a randomized subdivision algorithm which given a smooth function constructs an adaptive rectangular grid containing the essential information necessary for approximating nodal domains furthermore under mild regularity assumptions the algorithm will also provide a computer assisted proof for the correctness of the approximation by showing that the rectangular grid can be used to construct rectangular complexes which are homotopy equivalent to the nodal domai

sorted random projections for robust rotation invariant texture classification this paper presents a simple novel yet very powerful approach for robust rotation invariant texture classification based on random projection the proposed sorted random projection maintains the strengths of random projection in being computationally efficient and low dimensional with the addition of a straightforward sorting step to introduce rotation invariance at the feature extraction stage a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches the rotation invariant random features are embedded into a bag of words model to perform texture classification allowing us to achieve global rotation invariance the proposed unconventional and novel random features are very robust yet by leveraging the sparse nature of texture images our approach outperforms traditional feature extraction methods which involve careful design and complex steps we report extensive experiments comparing the proposed method to six state of the art methods rp patch lbp wmfs and the methods of lazebnik et al and zhang et al in texture classification on five databases curet brodatz uiuc umd and kth tips our approach leads to significant improvements in classification accuracy producing consistently good results on each database including what we believe to be the best reported results for brodatz umd and kth tips

a morphological affine and galilean invariant scale space for movies we study a model of multiscale analysis or scale space applied to movies this model comes from a thorough formalization that has been done in the theory of scale space of static image this formulation has led one to associate with each multiscale analysis a partial differential equation pde we examine the case of movies and insist on the motion aspects more precisely it has been proved by alvarez guichard lions and morel 1993 that there exists a unique affine and morphological and galilean invariant scale space for movies the amg model this model is described by a partial differential equation we focus on terms appearing in that equation we show that this model provides a reliable definition of an optical multiscale acceleration at the practical level scale is interpreted as a way of characterizing reliable trajectories as we prove by experiments the amg model is a riddle for decimating spurious trajectories due to any kinds of nonadditive impurities and noise simple discrete formulae are given to implement the model

a modified k means algorithm for circular invariant clustering several important pattern recognition applications are based on feature vector extraction and vector clustering directional patterns are commonly represented by rotation variant vectors f sub d formed from features uniformly extracted in m directions it is often desirable that pattern recognition algorithms are invariant under pattern rotation this paper introduces a distance measure and a k means based algorithm namely circular k means ck means to cluster vectors containing directional information such as f sub d in a circular shift invariant manner a circular shift of f sub d corresponds to pattern rotation thus the algorithm is rotation invariant an efficient fourier domain representation of the proposed measure is presented to reduce computational complexity a split and merge approach smck means suited to the proposed ck means technique is proposed to reduce the possibility of converging at local minima and to estimate the correct number of clusters experiments performed for textural images illustrate the superior performance of the proposed algorithm for clustering directional vectors f sub d compared to the alternative approach that uses the original k means and rotation invariant feature vectors transformed from f sub d

retinal vessel centerline extraction using multiscale matched filters confidence and edge measures motivated by the goals of improving detection of low contrast and narrow vessels and eliminating false detections at nonvascular structures a new technique is presented for extracting vessels in retinal images the core of the technique is a new likelihood ratio test that combines matched filter responses confidence measures and vessel boundary measures matched filter responses are derived in scale space to extract vessels of widely varying widths a vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile vessel boundary measures and associated confidences are computed at potential vessel boundaries combined these responses form a six dimensional measurement vector at each pixel a training technique is used to develop a mapping of this vector to a likelihood ratio that measures the vesselness at each pixel results comparing this vesselness measure to matched filters alone and to measures based on the hessian of intensities show substantial improvements both qualitatively and quantitatively the hessian can be used in place of the matched filter to obtain similar but less substantial improvements or to steer the matched filter by preselecting kernel orientations finally the new vesselness likelihood ratio is embedded into a vessel tracing framework resulting in an efficient and effective vessel centerline extraction algorithm

curvilinear structure analysis by ranking the orientation responses of path operators the analysis of thin curvilinear objects in 3d images is a complex and challenging task in this article we introduce a new non linear operator called rorpo ranking the orientation responses of path operators inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis rorpo is built upon the notion of path operator from mathematical morphology this operator unlike most operators commonly used for 3d curvilinear structure analysis is discrete non linear and non local from this new operator two main curvilinear structure characteristics can be estimated an intensity feature that can be assimilated to a quantitative measure of curvilinearity and a directional feature providing a quantitative measure of the structure s orientation we provide a full description of the structural and algorithmic details for computing these two features from rorpo and we discuss computational issues we experimentally assess rorpo by comparison with three of the most popular curvilinear structure analysis filters namely frangi vesselness optimally oriented flux and hybrid diffusion with continuous switch in particular we show that our method provides up to 8 percent more true positive and 50 percent less false positives than the next best method on synthetic and real 3d images

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

scale affine invariant interest point detectors in this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations our scale and affine invariant detectors are based on the following recent results 1 interest points extracted with the harris detector can be adapted to affine transformations and give repeatable results geometrically stable 2 the characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives the laplacian 3 the affine shape of a point neighborhood is estimated based on the second moment matrix our scale invariant detector computes a multi scale representation for the harris interest point detector and then selects points at which a local measure the laplacian is maximal over scales this provides a set of distinctive points which are invariant to scale rotation and translation as well as robust to illumination changes and limited changes of viewpoint the characteristic scale determines a scale invariant region for each point we extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood an iterative algorithm modifies location scale and neighborhood of each point and converges to affine invariant points this method can deal with significant affine transformations including large scale changes the characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point we present a comparative evaluation of different detectors and show that our approach provides better results than existing methods the performance of our detector is also confirmed by excellent matching resultss the image is described by a set of scale affine invariant descriptors computed on the regions associated with our points

wavelet descriptors for multiresolution recognition of handprinted characters we present a novel set of shape descriptors that represents a digitized pattern in a concise way and that is particularly well suited for the recognition of handprinted characters the descriptor set is derived from the wavelet transform of a pattern s contour the approach is closely related to feature extraction methods by fourier series expansion the motivation to use an orthonormal wavelet basis rather than the fourier basis is that wavelet coefficients provide localized frequency information and that wavelets allow us to decompose a function into a multiresolution hierarchy of localized frequency bands we describe a character recognition system that relies upon wavelet descriptors to simultaneously analyze character shape at multiple levels of resolution the system was trained and tested on a large database of more than 6000 samples of handprinted alphanumeric characters the results show that wavelet descriptors are an efficient representation that can provide for reliable recognition in problems with large input variability

the empirical mode decomposition and the hilbert spectrum for nonlinear and non stationary time series analysis a new method for analysing nonlinear and non stationary data has been developed the key part of the method is the empirical mode decomposition method with which any complicated data set can be dec

the sure let approach to image denoising we propose a new approach to image denoising based on the image domain minimization of an estimate of the mean squared error stein s unbiased risk estimate sure unlike most existing denoising algorithms using the sure makes it needless to hypothesize a statistical model for the noiseless image a key point of our approach is that although the nonlinear processing is performed in a transformed domain typically an undecimated discrete wavelet transform but we also address nonorthonormal transforms this minimization is performed in the image domain indeed we demonstrate that when the transform is a ldquotightrdquo frame an undecimated wavelet transform using orthonormal filters separate subband minimization yields substantially worse results in order for our approach to be viable we add another principle that the denoising process can be expressed as a linear combination of elementary denoising processes linear expansion of thresholds let armed with the sure and let principles we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient quite remarkably the very competitive results obtained by performing a simple threshold image domain sure optimized on the undecimated haar wavelet coefficients show that the sure let principle has a huge potential

feature vector field and feature matching in this paper we propose a feature vector field for images which is built by the inner products and exterior products of image gradients the feature vector field effectively represents image edges and feature points including corners and edge points with big curvature using the feature vector field some novel descriptors are constructed for point matching and curve matching the descriptors are all invariant to image euclidean transformations and linear intensity changes the experiments show that the descriptors also have a good adaptability to small image affine transformation jpeg compression and nonlinear changes of intensity

reduced length checking sequences here the method proposed by ural wu and zhang 1997 for constructing minimal length checking sequences based on distinguishing sequences is improved the improvement is based on optimizations of the state recognition sequences and their use in constructing test segments it is shown that the proposed improvement further reduces the length of checking sequences produced from minimal completely specified and deterministic finite state machines

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

on minimizing the lengths of checking sequences a general model for constructing minimal length checking sequences employing a distinguishing sequence is proposed the model is based on characteristics of checking sequences and a set of state recognition sequences some existing methods are shown to be special cases of the proposed model and are proven to construct checking sequences the minimality of the resulting checking sequences is discussed and a heuristic algorithm for the construction of minimal length checking sequences is given

palmprint classification using principal lines this paper proposes a novel algorithm for the automatic classification of low resolution palmprints first the principal lines of the palm are defined using their position and thickness then a set of directional line detectors is devised after that we use these directional line detectors to extract the principal lines in terms of their characteristics and their definitions in two steps the potential beginnings line initials of the principal lines are extracted and then based on these line initials a recursive process is applied to extract the principal lines in their entirety finally palmprints are classified into six categories according to the number of the principal lines and the number of their intersections the proportions of these six categories 1 6 in our database containing 13 800 samples are 0 36 1 23 2 83 11 81 78 12 and 5 65 respectively the proposed algorithm has been shown to classify palmprints with an accuracy of 96 03

test generation for multiple state table faults in finite state machines a test generation procedure to detect multiple state table faults in finite state machines is proposed the importance of multiple state table faults and their advantages as test generation objectives to avoid the need for checking experiments are considered the proposed procedure is based on a new method for implicit enumeration of large numbers of multiple faults by using incompletely specified faulty machines experimental results are presented to demonstrate the effectiveness of implicit fault enumeration in detecting large numbers of multiple faults and in guaranteeing detection of all the faults or all the faults up to a specific multiplicity

training less color object recognition for autonomous robotics abstract this paper looks at the challenge of object recognition from the perspective of achieving the final goals of practical real world autonomous robot vision applications that of identifying a target of interest in the robot s field of view and properly localizing its position in preparation for the higher level goal of tracking and navigation a unified framework is introduced that combines a multidimensional feature histogram approach with a multiscale pyramid approach for training less color object recognition and localization with direct application to autonomous robotic agents this framework addresses the high computational cost associated with multidimensional processing by deriving the multidimensional laplacian feature histogram pyramid a novel approach to a unified multidimensional multiscale histogram representation furthermore a taylor series formulation is employed to combine the multiscale levels of the multidimensional laplacian feature histogram pyramid into one efficient multidimensional multiscale laplacian taylor feature histogram for rapid object recognition and localization the paper describes the criteria for target detection and localization by autonomous robots and how this newly developed framework fits these needs comparative results demonstrate the robustness of this recognition framework to noise and localization of target objects in cluttered scenes

image denoising using scale mixtures of gaussians in the wavelet domain we describe a method for removing noise from digital images based on a statistical model of the coefficients of an overcomplete multiscale oriented basis neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables a gaussian vector and a hidden positive scalar multiplier the latter modulates the local variance of the coefficients in the neighborhood and is thus able to account for the empirically observed correlation between the coefficient amplitudes under this model the bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable we demonstrate through simulations with images contaminated by additive white gaussian noise that the performance of this method substantially surpasses that of previously published methods both visually and in terms of mean squared error

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

image representations using multiscale differential operators differential operators have been widely used for multiscale geometric descriptions of images the efficient computation of these differential operators is always desirable moreover it has not been clear whether such representations are invertible for certain applications it is usually required that such representations should be invertible so that one can facilitate the processing of information in the transform domain and then recover it in this paper such problems are studied we consider multiscale differential representations of images using different types of operators such as the directional derivative operators and laplacian operators in particular we provide a general approach to represent images by their multiscale and multidirectional derivative components for practical implementation efficient pyramid like algorithms are derived using the spline technique for both the the composition and reconstruction of images it is shown that using these representations various meaningful geometric information of images can be extracted at multiple scales therefore these representations can be used for edge based image processing purposes furthermore the intrinsic relationships of the proposed representations with the compact wavelet models and some classical multiscale approaches are also elucidated in the paper

two view line matching algorithm based on context and appearance in low textured images a novel approach for line detection and matching is proposed aimed at achieving good performance with low textured scenes under uncontrolled illumination conditions line detection is performed by means of phase based edge detector over gaussian scale space followed by a multi scale fusion stage which has been proven to be profitable in minimizing the number of fragmented and overlapped segments line matching is performed by an iterative process that uses structural information collected through the use of different line neighborhoods making the set of matched lines grow robustly at each iteration results show that this approach is suitable to deal with low textured scenes and also robust under a wide variety of image transformations highlightsa novel approach for line detection and matching is proposed lines are detected through a fusion process over the gaussian scale space lines are matched based on their appearance geometric properties and neighborhoods our proposal minimizes the number of fragmented and overlapped segments our proposal is suitable for low textured scenes under uncontrolled illumination

consistency analysis on orientation features for fast and accurate palmprint identification a typical palmprint identification system identifies a query palmprint by matching it with the templates in the database and comparing the similarity score with a pre defined threshold however continual database expansion would make conventional brute force search method inefficient for large scale palmprint identification in this paper we propose a consistent orientation pattern cop hashing method to enforce fast search using the orientation and response features extracted by steerable filter we first give an analysis on the consistency of orientation features and then introduce a method to construct cop using the consistent features because the cop is very stable across the samples of the same subject the cop hashing method can find the target template quickly and thus can lead to early termination of the searching process experiments have been carried out on several real and synthetic databases and the results show that the cop hashing method can accelerate the identification process by more than an order of magnitude and can achieve comparable identification accuracy while compared with the state of the art approaches

noise analysis of a new singularity index we analyze the noise sensitivity of a new singularity index that was designed to detect impulse singularities in signals of arbitrary dimensionality while rejecting step like singularities see muralidhar ieee signal process lett vol 20 no 1 pp 7 10 2013 and muralidhar proc ieee int conf image process 2012 for example the index responds strongly to curvilinear masses ridges in images while weakly to jump discontinuities edges we analyze the detection power of the index in the presence of noise our analysis is geared towards answering the following questions a in the presence of noise only what is the probability of falsely detecting an impulse given a threshold b given an impulse submerged in noise what is the probability of detecting it given a threshold and c since the index is designed to be edge suppressing what is the probability of incorrectly detecting an edge submerged in noise given a threshold we compare the detection power of the index with that of a nominal impulse detector the second derivative operator simulations and example applications in 1 d and 2 d reveal the efficacy of the new singularity index for correctly detecting impulses submerged in noise while suppressing edges a software version of the 2 d singularity index can be downloaded from http live ece utexas edu research singularityindex singularityindexcode zip

topology in dynamics differential equations and data abstract this special issue is devoted to showcasing recent uses of topological methods in the study of dynamical behavior and the analysis of both numerical and experimental data the twelve original research papers span a wide spectrum of results from abstract index theories over homology and persistence based data analysis techniques to computer assisted proof techniques based on topological fixed point arguments

lapb locally adaptive patch based wavelet domain edge preserving image denoising abstract image denoising is one of the most diversified research areas in the field of image processing and computer vision it is highly desirable for a denoising technique to preserve important image features such as edges corners and other sharp structures of an image after denoising wavelet transforms show excellent proficiency in providing efficient edge preserving image denoising due to their capability of suppressing noisy signals from an image this paper presents a novel edge preserving image denoising technique based on wavelet transforms the multi level decomposition of the noisy image is carried out to transform the data into the wavelet domain a locally adaptive patch based lapb thresholding scheme is used to effectively reduce noise while preserving relevant features of the original image experimental results on benchmark test images demonstrate that the proposed method achieves competitive denoising performance in comparison to various state of the art algorithms

optimizing the length of checking sequences a checking sequence generated from a finite state machine is a test sequence that is guaranteed to lead to a failure if the system under test is faulty and has no more states than the specification the problem of generating a checking sequence for a finite state machine m is simplified if m has a distinguishing sequence an input sequence d with the property that the output sequence produced by m in response to d is different for the different states of m previous work has shown that where a distinguishing sequence is known an efficient checking sequence can be produced from the elements of a set a of sequences that verify the distinguishing sequence used and the elements of a set spl gamma of subsequences that test the individual transitions by following each transition t by the distinguishing sequence that verifies the final state of t in this previous work a is a predefined set and spl gamma is defined in terms of a the checking sequence is produced by connecting the elements of spl gamma and a to form a single sequence using a predefined acyclic set e sub c of transitions an optimization algorithm is used in order to produce the shortest such checking sequence that can be generated on the basis of the given a and e sub c however this previous work did not state how the sets a and e sub c should be chosen this paper investigates the problem of finding appropriate a and e sub c to be used in checking sequence generation we show how a set a may be chosen so that it minimizes the sum of the lengths of the sequences to be combined further we show that the optimization step in the checking sequence generation algorithm may be adapted so that it generates the optimal e sub c experiments are used to evaluate the proposed method

subband coding of images subband coding has become quite popular for the source encoding of speech this paper presents a simple yet efficient extension of this concept to the source coding of images we specify the constraints for a set of two dimensional quadrature mirror filters qmf s for a particular frequency domain partition and show that these constraints are satisfied by a separable combination of one dimensional qmf s bits are then optimally allocated among the subbands to minimize the mean squared error for dpcm coding of the subbands also an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask optimum quantization is employed with quantizers matched to the laplacian distribution subband coded images are presented along with their signal to noise ratios snr s the snr performance of the subband coder is compared to that of the adaptive discrete cosine transform dct vector quantization and differential vector quantization for bit rates of 0 67 1 0 and 2 0 bits per pixel for 256 256 monochrome images the adaptive subband coder has the best snr performance

noise reduction procedures for gravity gradiometer data abstractnoise suppression of airborne gravity gradiometer data is a crucial part of the data processing stream we considered two approaches to removing noise kriging and directional filtering kriging is an estimation procedure for the interpolation of spatial data the estimator is calculated from the data variogram which characterizes the noise level and correlation length of the measurements directional filtering uses a user defined operator that is oriented to preferentially smooth the data along the strike but it leaves short wavelength components in the cross strike direction for definition of the trend edges both methods were applied to a recently collected offshore gravity gradient survey the kriging and directional filtering results revealed a similar level of smoothness but the main difference between them was the extra smoothing along the strike for the directionally filtered data because kriging is a data driven procedure it provides an objective estimate of the data noise level and

rigorous cubical approximation and persistent homology of continuous functions abstract the interaction between discrete and continuous mathematics lies at the heart of many fundamental problems in applied mathematics and computational sciences in this paper we discuss the problem of discretizing vector valued functions defined on finite dimensional euclidean spaces in such a way that the discretization error is bounded by a pre specified small constant while the approximation scheme has a number of potential applications we consider its usefulness in the context of computational homology more precisely we demonstrate that our approximation procedure can be used to rigorously compute the persistent homology of the original continuous function on a compact domain up to small explicitly known and verified errors in contrast to other work in this area our approach requires minimal smoothness assumptions on the underlying function

estimation of fractal dimension of images using a fixed mass approach a method from the field of chaotic dynamics is applied for the estimation of fractal dimension of images the method is compared with other well known algorithms on a set of computer generated images of known fractal dimension the results confirm the superiority of the method in terms of accuracy dynamic range and computational time 1999 published by elsevier science b v all rights reserved

a least squares framework for component analysis over the last century component analysis ca methods such as principal component analysis pca linear discriminant analysis lda canonical correlation analysis cca locality preserving projections lpp and spectral clustering sc have been extensively used as a feature extraction step for modeling classification visualization and clustering ca techniques are appealing because many can be formulated as eigen problems offering great potential for learning linear and nonlinear representations of data in closed form however the eigen formulation often conceals important analytic and computational drawbacks of ca techniques such as solving generalized eigen problems with rank deficient matrices e g small sample size problem lacking intuitive interpretation of normalization factors and understanding commonalities and differences between ca methods this paper proposes a unified least squares framework to formulate many ca methods we show how pca lda cca lpp sc and its kernel and regularized extensions correspond to a particular instance of least squares weighted kernel reduced rank regression ls wkrrr the ls wkrrr formulation of ca methods has several benefits 1 provides a clean connection between many ca techniques and an intuitive framework to understand normalization factors 2 yields efficient numerical schemes to solve ca techniques 3 overcomes the small sample size problem 4 provides a framework to easily extend ca methods we derive weighted generalizations of pca lda sc and cca and several new ca techniques

asymmetric pulse modeling for fri sampling we consider sampling and reconstruction of finite rate of innovation fri signals such as a train of pulses where the pulses have varying degrees of asymmetry we address the problem of asymmetry modeling starting from a given symmetric prototype we show that among the class of unitary operators that are linear and invariant to translation and scale the fractional hilbert frh operator is unique for parametrically modeling pulse asymmetry the frh operator is obtained by a trigonometric interpolation between the standard hilbert and identity operators where the interpolation weights are determined by the degree of asymmetry the frh operators are also steerable which allows for estimation of the asymmetry factors in addition to the delays and amplitudes using the high resolution spectral estimation techniques that are used for solving standard fri problems we also develop the discrete counterpart using discrete frh operators and show that all the desirable properties carry over smoothly to the discrete setting as well we derive closed form expressions for the cramer rao bounds and hammersley chapman robbins bound on the variances of the estimators for continuous and discrete parameters respectively experimental results show that the proposed estimators have variances that meet the lower bounds we demonstrate an application of the proposed discrete frh methodology on real electrocardiogram ecg signals in the presence of noise specifically we show how the asymmetry of qrs complexes in various channels of an ecg signal could be modeled accurately

combining weighted linear project analysis with orientation diffusion for fingerprint orientation field reconstruction this paper presents a novel algorithm for reconstructing the fingerprint orientation field fof the basic idea of the algorithm is to reconstruct the fof by combining weighted linear project analysis with orientation diffusion we first compute the weight values of point gradients according to the similarity of point orientations in the second procedure the qualities of blocks are assessed by the coherence of point orientations and then the block orientation with high quality are estimated by the weighted linear projection analysis based on the vector set of point gradients the fingerprint blocks that will be reconstructed is selected by the priority of them in the third procedure in the end the fof is reconstructed by orientation diffusion based on the reconstruction priorities of blocks to validate the performance the proposed method has been applied to fingerprint enhancement fingerprint singularity detection fingerprint minutiae extraction and fingerprint matching using the fvc2000 and fvc2004 databases the experiments show that the proposed method is more accurate and more reliable and it is more robust against noise

k local hyperplane distance nearest neighbor classifier oriented local discriminant analysis k local hyperplane distance nearest neighbor hknn classifier is an improved k nearest neighbor knn algorithm that has been successfully applied to pattern classification this paper embeds the decision rule of hknn classifier into the discriminant analysis model to develop a new feature extractor the obtained feature extractor is called k local hyperplane distance nearest neighbor classifier oriented local discriminant analysis holda in which a regularization item is imposed on the original hknn algorithm to obtain a more reliable distance metric based on this distance metric the homo class and hetero class local scatters are characterized in holda by maximizing the ratio of the hetero class local scatter to the homo class local scatter we obtain a subspace which is suitable for feature extraction and classification in general this paper provides a framework for building a feature extractor from the decision rule of a classifier by this means the feature extractor and classifier can be seamlessly integrated experimental results on four databases demonstrate that the integrated pattern recognition system is effective

invariant pattern recognition by moment fourier descriptor abstract this paper proposes a new shape descriptor called moment fourier descriptor which can describe a complex object composed by a set of closed regions this descriptor is shown to be independent of object s translation rotation and scaling the essential advantage of moment fourier descriptor is that it can be used to recognize more complex patterns than the traditional fourier descriptors simulations are given to show the high performance of the proposed approach

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

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

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

steerable wavelet frames based on the riesz transform we consider an extension of the 1 d concept of analytical wavelet to n d which is by construction compatible with rotations this extension called a monogenic wavelet yields a decomposition of the wavelet coefficients into amplitude phase and phase direction the monogenic wavelet is based on the hypercomplex monogenic signal which is defined using riesz transforms and perfectly isotropic wavelets frames employing the new concept of clifford frames we can show that the monogenic wavelet generates a wavelet frame furthermore this approach yields wavelet frames that are steerable with respect to direction applications to descreening and contrast enhancement illustrate the versatility of this approach to image analysis and reconstruction

spatially adaptive estimation via fitted local likelihood techniques this paper offers a new technique for spatially adaptive estimation the local likelihood is exploited for nonparametric modeling of observations and estimated signals the approach is based on the assumption of a local homogeneity of the signal for every point there exists a neighborhood in which the signal can be well approximated by a constant the fitted local likelihood statistics are used for selection of an adaptive size and shape of this neighborhood the algorithm is developed for a quite general class of observations subject to the exponential distribution the estimated signal can be uni and multivariable we demonstrate a good performance of the new algorithm for image denoising and compare the new method versus the intersection of confidence interval ici technique that also exploits a selection of an adaptive neighborhood for estimation

wavelet based fingerprint image retrieval this paper presents a novel approach for personal identification based on a wavelet based fingerprint retrieval system which encompasses three image retrieval tasks namely feature extraction similarity measurement and feature indexing we propose the use of different types of wavelets for representing and describing the textural information presented in fingerprint images in a compact way for that purpose the feature vectors used to characterize the fingerprints are obtained by computing the mean and the standard deviation of the decomposed images in the wavelet domain these feature vectors are used both to retrieve the most similar fingerprints given a query image and their indexation is used to reduce the search spaces of candidate images the different types of wavelets used in our study include gabor wavelets tree structured wavelet decomposition using both orthogonal and bi orthogonal filter banks as well as the steerable wavelets to evaluate the retrieval accuracy of the proposed approach a total number of eight different data sets were considered we also took into account different combinations of the above wavelets with six similarity measures the results show that the gabor wavelets combined with the square chord similarity measure achieves the best retrieval effectiveness

local shading analysis local analysis of image shading in the absence of prior knowledge about the viewed scene may be used to provide information about the scene the following has been proved every image point has the same image intensity and first and second derivatives as the image of some point on a lambertian surface with principal curvatures of equal magnitude further if the principal curvatures are assumed to be equal there is a unique combination of image formation parameters up to a mirror reversal that will produce a particular set of image intensity and first and second derivatives a solution for the unique combination of surface orientation etc is presented this solution has been extended to natural imagery by using general position and regional constraints to obtain estimates of the following surface orientation at each image point the qualitative type of the surface i e whether the surface is planar cylindrical convex concave or saddle the illuminant direction within a region algorithms to recover illuminant direction and estimate surface orientation have been evaluated on both natural and synthesized images and have been found to produce useful information about the scene

linear shape from shading in many situations the reflectance function of a surface is approximately linear and there is an effielent closed form solution to the shape from shading problem when boundary conditions e g edges singular points are not available good estimates of shape may still be extracted by using the assumption of general viewing position an improved method for estimating the illuminant direction is also presented

testing finite state machines state identification and verification we study the complexity of two fundamental problems in the testing of finite state machines 1 distinguishing sequences state identification we show that it is pspace complete to determine whether a finite state machine has a preset distinguishing sequence there are machines that have distinguishing sequences but only of exponential length we give a polynomial time algorithm that determines whether a finite state machine has an adaptive distinguishing sequence the previous classical algorithms take exponential time furthermore if there is an adaptive distinguishing sequence then we give an efficient algorithm that constructs such a sequence of length at most n n spl minus 1 2 which is the best possible where n is the number of states 2 unique input output sequences state verification it is pspace complete to determine whether a state of a machine has a unique input output sequence there are machines whose states have unique input output sequences but only of exponential length

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

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

signal denoising and ultrasonic flaw detection via overcomplete and sparse representations sparse signal representations from overcomplete dictionaries are the most recent technique in the signal processing community applications of this technique extend into many fields in this paper this technique is utilized to cope with ultrasonic flaw detection and noise suppression problem in particular a noisy ultrasonic signal is decomposed into sparse representations using a sparse bayesian learning algorithm and an overcomplete dictionary customized from a gabor dictionary by incorporating some a priori information of the transducer used nonlinear postprocessing including thresholding and pruning is then applied to the decomposed coefficients to reduce the noise contribution and extract the flaw information because of the high compact essence of sparse representations flaw echoes are packed into a few significant coefficients and noise energy is likely scattered all over the dictionary atoms generating insignificant coefficients this property greatly increases the efficiency of the pruning and thresholding operations and is extremely useful for detecting flaw echoes embedded in background noise the performance of the proposed approach is verified experimentally and compared with the wavelet transform signal processor experimental results to detect ultrasonic flaw echoes contaminated by white gaussian additive noise or correlated noise are presented in the paper

a differential model of the complex cell the receptive fields of simple cells in the visual cortex can be understood as linear filters these filters can be modeled by gabor functions or gaussian derivatives gabor functions can also be combined in an energy model of the complex cell response this letter proposes an alternative model of the complex cell based on gaussian derivatives it is most important to account for the insensitivity of the complex response to small shifts of the image the new model uses a linear combination of the first few derivative filters at a single position to approximate the first derivative filter at a series of adjacent positions the maximum response over all positions gives a signal that is insensitive to small shifts of the image this model unlike previous approaches is based on the scale space theory of visual processing in particular the complex cell is built from filters that respond to the 2d differential structure of the image the computational aspects of the new model are studied in one and two dimensions using the steerability of the gaussian derivatives the response of the model to basic images such as edges and gratings is derived formally the response to natural images is also evaluated using statistical measures of shift insensitivity the neural implementation and predictions of the model are discussed

coreduction homology algorithm for inclusions and persistent homology we present an algorithm for computing the homology of inclusion maps which is based on the idea of coreductions and leads to significant speed improvements over current algorithms it is shown that this algorithm can be extended to compute both persistent homology and an extension of the persistence concept to two sided filtrations in addition to describing the theoretical background we present results of numerical experiments as well as several applications to concrete problems in materials science

accurate image rotation using hermite expansions in this paper we propose an approach for the accurate rotation of a digital image using hermite expansions this exploits the fact that if a 2 d continuous bandlimited hermite expansion is rotated the resulting function can be expressed as a hermite expansion with the same bandlimit furthermore the hermite coefficients of the initial 2 d expansion and the rotated expansion are mapped through an invertible linear relationship two efficient methods to compute the mapping between hermite coefficients during rotation are proposed we also propose a method for connecting the hermite expansion and a discrete image using this method we can obtain the hermite expansion from a discrete image and vice versa combining these techniques we propose new methods for the rotation of discrete images we assess the accuracy of our methods and compare them with an existing fft based method implementing three shears we find that the method proposed here consistently has better accuracy than the fft based method

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