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

noncommutative differential geometry and new models of gauge theory the noncommutative differential geometry of the algebra c v mn c of smooth mn c valued functions on a manifold v is investigated for n 2 the analog of maxwell s theory is constructed and interpreted as a field theory on v it describes a u n yang mills field minimally coupled to a set of fields with values in the adjoint representation that interact among themselves through a quartic polynomial potential the euclidean action which is positive vanishes on exactly two distinct gauge orbits which are interpreted as two vacua of the theory in one of the corresponding vacuum sectors the su n part of the yang mills field is massive for the case n 2 analogies with the standard model of electroweak theory are pointed out finally a brief description is provided of what happens if one starts from the analog of a general yang mills theory instead of maxwell s theory which is a particular case

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

representable e theory for c0 x algebras let x be a locally compact space and let a and b be c0 x algebras we define the notion of an asymptotic c0 x morphism from a to b and construct representable e theory groups re x a b these are the universal groups on the category of separable c0 x algebras that are c0 x stable c0 x homotopy invariant and half exact if a is rkk x nuclear these groups are naturally isomorphic to kasparov s representable kk theory groups rkk x a b applications and examples are also discussed let x be a locally compact space a c algebra a is called a c0 x algebra if it is equipped with a nondegenerate action of c0 x as commuting multipliers of a these algebras have many important applications since they can be represented as the sections of a noncommutative bundle of c algebras over x kw95 ew97 in particular any continuous trace c algebra with spectrum x is a c0 x algebra rw98 there is a substantial amount of literature on c0 x algebras see the general treatments in bla96 nil96 in 1988 kasparov kas88 defined representable kk theory groups rkk x a b which extend his bivariant kk theory groups kk a b where x and the representable topo logical k theory rk 0 x of segal seg70 these groups and their equivariant versions have found many applications in geometry topology and index theory most notably in kasparov s proof of the novikov conjecture for closed discrete subgroups of finite component lie groups kas88 however these groups are difficult to compute sincethey have six term exact sequences only for split exact sequences of c0 x algebras bau98 in 1989 connes and higson ch89 defined e theory groups e a b based on asymptotic morphisms from a to b an asymptotic morphism t a b is a one parameter family of maps which satisfy the properties of a homomorphism in the limit as t there is a natural notion of homotopy for asymptotic morphisms for which a composition product is well defined if a is separable the functor b 7 e a b is the universal functor on the category of separable c algebras b which is stable homotopy invariant and half exact thus there are six term exact sequences in both variables for any exact sequence of separable c algebras there is a natural transformation kk a b e a b which is an isomorphism if a is k nuclear in this paper we extend these constructions to the category of separable c0 x algebras by defining a notion of asymptotic c0 x morphism between two c0 x algebras a and b there is a corresponding notion of c0 x homotopy for an asymptotic c0 x morphism for which composition is well defined this leads us to define representable e theory groups re x a b if x is second countable then these groups have a composition product re x a b re x b c re x a c and we prove that these are the universal such groups on the category of separable c0 x algebras that are c0 x stable c0 x homotopy invariant and half exact we also prove that there is a natural transformation

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

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

submanifolds and quotient manifolds in noncommutative geometry we define and study noncommutative generalizations of submanifolds and quotient manifolds using the derivation based differential calculus introduced by m dubois violette and p michor we give examples to illustrate these definitions

noncommutative differential geometry of matrix algebras the noncommutative differential geometry of the algebra mn c of complex n n matrices is investigated the role of the algebra of differential forms is played by the graded differential algebra c sl n c mn c mn c sl n c sl n c acting by inner derivations on mn c a canonical symplectic structure is exhibited for mn c for which the poisson bracket is to within a factor i the commutator also a canonical riemannian structure is described for mn c finally the analog of the maxwell potential is constructed and it is pointed out that there is a potential with a vanishing curvature that is not a pure gauge

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

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

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

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

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

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

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

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

invariant noncommutative connections in this paper we classify invariant noncommutative connections in the framework of the algebra of endomorphisms of a complex vector bundle it has been proven previously that this noncommutative algebra generalizes in a natural way the ordinary geometry of connections we use explicitly some geometric constructions usually introduced to classify ordinary invariant connections and we expand them using algebraic objects coming from the noncommutative setting the main result is that the classification can be performed using a reduced algebra an associated differential calculus and a module over this algebra

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

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

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

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

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

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

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