synchronization and directed percolation in coupled map lattices we study a synchronization mechanism based on one way coupling of all or nothing type applied to coupled map lattices with several different local rules by analyzing the metric and the topological distance between the two systems we found two different regimes a strong chaos phase in which the transition has a directed percolation character and a weak chaos phase in which the synchronization transition occurs abruptly we are able to derive some analytical approximations for the location of the transition point and the critical properties of the system we propose to use the characteristics of this transition as indicators of the spatial propagation of chaoticity

adaptive support weight approach for correspondence search we present a new window based method for correspondence search using varying support weights we adjust the support weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity our method outperforms other local methods on standard stereo benchmarks

alpha trimmed means and their relationship to median filters suppose that x is a finite set of n numbers the trimmed mean of x is obtained by sorting x into ascending order removing trimming a fixed fraction alpha 0 leq alpha leq 0 5 from the high and low ends of the sorted set and computing the average of the remaining values when applied to a sliding window of length lw the trimming process is called trim filtering for 0 5 the trimmed mean of a set is the median of the set and the filtering operation is called median filtering repeated application of a median filter to the output of a previous median filter of the same length lw eventually produces a signal which is invariant to median filtering this final signal is called the root signal this paper explains the relationship between trimmed means and median filters derives a simple straightforward and fast algorithm for applying a median filter and provides a new explanation of the convergence of repeated median filtering to the root signal the latter result incorporates an approach which permits generalization of the associated concepts to a larger class of index map filters

a robust real time ellipse detector abstract in this paper we present an improved ellipse detector that may be used in real time face detection in this algorithm we first extract edges from the image using a robust edge detector which is then followed by a rule based method finally we decompose the parameter space of the hough transform to achieve computational efficiency our extensive experimental results show indeed that the proposed detector is capable of detecting ellipse features with an excellent accuracy under various image conditions at a high speed 10 frames per second on a pentium iii 500 mhz pc

scale space and edge detection using anisotropic diffusion a new definition of scale space is suggested and a class of algorithms used to realize a diffusion process is introduced the diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing it is shown that the no new maxima should be generated at coarse scales property of conventional scale space is preserved as the region boundaries in the approach remain sharp a high quality edge detector which successfully exploits global information is obtained experimental results are shown on a number of images parallel hardware implementations are made feasible because the algorithm involves elementary local operations replicated over the image

a fast approximation of the bilateral filter using a signal processing approach the bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges it has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics and fast versions have been proposed unfortunately little is known about the accuracy of such accelerations in this paper we propose a new signal processing analysis of the bilateral filter which complements the recent studies that analyzed it as a pde or as a robust statistical estimator the key to our analysis is to express the filter in a higher dimensional space where the signal intensity is added to the original domain dimensions importantly this signal processing perspective allows us to develop a novel bilateral filtering acceleration using downsampling in space and intensity this affords a principled expression of accuracy in terms of bandwidth and sampling the bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities this allows us to derive criteria for downsampling the key operations and achieving important acceleration of the bilateral filter we show that for the same running time our method is more accurate than previous acceleration techniques typically we are able to process a 2 megapixel image using our acceleration technique in less than a second and have the result be visually similar to the exact computation that takes several tens of minutes the acceleration is most effective with large spatial kernels furthermore this approach extends naturally to color images and cross bilateral filtering

adaptive fuzzy filtering for artifact reduction in compressed images and videos a fuzzy filter adaptive to both sample s activity and the relative position between samples is proposed to reduce the artifacts in compressed multidimensional signals for jpeg images the fuzzy spatial filter is based on the directional characteristics of ringing artifacts along the strong edges for compressed video sequences the motion compensated spatiotemporal filter mcstf is applied to intraframe and interframe pixels to deal with both spatial and temporal artifacts a new metric which considers the tracking characteristic of human eyes is proposed to evaluate the flickering artifacts simulations on compressed images and videos show improvement in artifact reduction of the proposed adaptive fuzzy filter over other conventional spatial or temporal filtering approaches

artistic edge and corner enhancing smoothing two important visual properties of paintings and painting like images are the absence of texture details and the increased sharpness of edges as compared to photographic images painting like artistic effects can be achieved from photographic images by filters that smooth out texture details while preserving or enhancing edges and corners however not all edge preserving smoothers are suitable for this purpose we present a simple nonlinear local operator that generalizes both the well known kuwahara filter and the more general class of filters known in the literature as ldquocriterion and value filter structure rdquo this class of operators suffers from intrinsic theoretical limitations which give rise to a dramatic instability in presence of noise especially on shadowed areas such limitations are discussed in the paper and overcome by the proposed operator a large variety of experimental results shows that the output of the proposed operator is visually similar to a painting comparisons with existing techniques on a large set of natural images highlight conditions on which traditional edge preserving smoothers fail whereas our approach produces good results in particular unlike many other well established approaches the proposed operator is robust to degradations of the input image such as blurring and noise contamination

a universal prior for integers and estimation by minimum description length of the number of bits required to write down the observed data has been reformulated to extend the classical maximum likelihood principle the principle permits estimation of the number of the parameters in statistical models in addition to their values and even of the way the parameters appear in the models i e of the model structures the principle rests on a new way to interpret and construct a universal prior distribution for the integers which makes sense even when the parameter is an individual object truncated realvalued parameters are converted to integers by dividing them by their precision and their prior is determined from the universal prior for the integers by optimizing the precision 1 introduction in this paper we study estimation based upon the principle of minimizing the total number of binary digits required to rewrite the observed data when each observation is given with some precision instead of attempting at an absolutely shortest description which would be futile we look for the optimum relative to a class of parametrically given distributions this minimum description length mdl principle which we introduced in a less comprehensive form in 25 turns out to degenerate to the more familiar maximum likelihood ml principle in case the number of parameters in the models is fixed so that the description length of the parameters themselves can be ignored in another extreme case where the parameters determine the data it similarly degenerates to jaynes s principle of maximum entropy 14 but the main power of the new criterion is that it permits estimates of the entire model its parameters their number and even the way the parameters appear in the model i e the model structure hence there will be no need to supplement the estimated parameters with a separate hypothesis test to decide whether a model is adequately parameterized or perhaps over parameterized

generalised nonlocal image smoothing we propose a discrete variational approach for image smoothing consisting of nonlocal data and smoothness constraints that penalise general dissimilarity measures defined on image patches one of such dissimilarity measures is the weighted l 2 distance between patches in such a case we derive an iterative neighbourhood filter that induces a new similarity measure in the photometric domain it can be regarded as an extended patch similarity measure that evaluates not only the patch similarity of two chosen pixels but also the similarity of their corresponding neighbours this leads to a more robust smoothing process since the pixels selected for averaging are more coherent with the local image structure by slightly modifying the way the similarities are computed we obtain two related filters the nl means filter of buades et al siam multiscale model simul 4 2 490 530 2005b and the nds filter of mrazek et al geometric properties for incomplete data computational imaging and vision vol 31 pp 335 352 springer dordrecht 2006 in fact the proposed approach can be considered as a generalisation of the latter filter to the space of patches we also provide novel insights into relations of the nds filter with diffusion regularisation methods as well as with some recently proposed graph regularisation techniques we evaluate our method for the task of denoising greyscale and colour images degraded with gaussian and salt and pepper noise demonstrating that it compares very well to other more sophisticated approaches

llsure local linear sure based edge preserving image filtering in this paper we propose a novel approach for performing high quality edge preserving image filtering based on a local linear model and using the principle of stein s unbiased risk estimate as an estimator for the mean squared error from the noisy image only we derive a simple explicit image filter which can filter out noise while preserving edges and fine scale details moreover this filter has a fast and exact linear time algorithm whose computational complexity is independent of the filtering kernel size thus it can be applied to real time image processing tasks the experimental results demonstrate the effectiveness of the new filter for various computer vision applications including noise reduction detail smoothing and enhancement high dynamic range compression and flash no flash denoising

fundamental limits of bayesian inference order parameters and phase transitions for road tracking there is a growing interest in formulating vision problems in terms of bayesian inference and in particular the maximum a posteriori map estimator in this paper we consider the special case of detecting roads from aerial images and demonstrate that analysis of this ensemble enables us to determine fundamental bounds on the performance of the map estimate we demonstrate that there is a phase transition at a critical value of the order parameter below this phase transition it is impossible to detect the road by any algorithm we derive closely related order parameters which determine the time and memory complexity of search and the accuracy of the solution using the n search strategy our approach can be applied to other vision problems and we briefly summarize the results when the model uses the wrong prior we comment on how our work relates to studies of the complexity of visual search and the critical behaviour in the computational cost of solving np complete problems

n sift n dimensional scale invariant feature transform we propose the n dimensional scale invariant feature transform n sift method for extracting and matching salient features from scalar images of arbitrary dimensionality and compare this method s performance to other related features the proposed features extend the concepts used for 2 d scalar images in the computer vision sift technique for extracting and matching distinctive scale invariant features we apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors we analyze the performance of a fully automated multimodal medical image matching technique based on these features and successfully apply the technique to determine accurate feature point correspondence between pairs of 3 d mri images and dynamic 3d time ct data

on the impact of anisotropic diffusion on edge detection content aware edge preserving smoothing techniques have gained visibility in recent years however they have had a rather limited impact on the edge detection literature compared to content unaware linear techniques often based on gaussian filters in this work we focus on anisotropic diffusion covering its initial definition by perona and malik and subsequent extensions a visual case study is used to illustrate their features we perform a quantitative evaluation of the performance of the canny method for edge detection when substituting linear gaussian smoothing filters by anisotropic diffusion highlightsreviewing the main trends in anisotropic diffusion identifying the practical scenarios on which anisotropic diffusion fails quantifying the improvement of edge detection when anisitropic diffusion is applied

synchronization of coupled systems with spatiotemporal chaos we argue that the synchronization transition of stochastically coupled cellular automata discovered recently by l g morelli it et al phys rev bf 58 e r8 1998 is generically in the directed percolation universality class in particular this holds numerically for the specific example studied by these authors in contrast to their claim for real valued systems with spatiotemporal chaos such as coupled map lattices we claim that the synchronization transition is generically in the universality class of the kardar parisi zhang equation with a nonlinear growth limiting term

susan a new approach to low level image processing this paper describes a new approach to low level image processing in particular edge and corner detection and structure preserving noise reduction non linear filtering is used to define which parts of the image are closely related to each individual pixel each pixel has associated with it a local image region which is of similar brightness to that pixel the new feature detectors are based on the minimization of this local image region and the noise reduction method uses this region as the smoothing neighbourhood the resulting methods are accurate noise resistant and fast details of the new feature detectors and of the new noise reduction method are described along with test results

a two stage algorithm for discontinuity preserving surface reconstruction a two stage algorithm for visual surface reconstruction from scattered data while preserving discontinuities is presented the first stage consists of a robust local approximation algorithm the moving least median of squares mlms of error to clean the data and create a grid from the original scattered data points this process is discontinuity preserving the second stage introduces a weighted bicubic spline wbs as a surface descriptor the wbs has a factor in the regularizing term that adapts the behavior of the spline across discontinuities the weighted bicubic approximating spline can approximate data with step discontinuities with no discernible distortion in the approximating surface the combination of robust surface fitting and wbss removes outliers and reduces gaussian noise either stage by itself would not effectively remove both kinds of noise experimental results with the two stage algorithm are presented

speeding up the bilateral filter a joint acceleration way computational complexity of the brute force implementation of the bilateral filter bf depends on its filter kernel size to achieve the constant time bf whose complexity is irrelevant to the kernel size many techniques have been proposed such as 2d box filtering dimension promotion and shiftability property although each of the above techniques suffers from accuracy and efficiency problems previous algorithm designers were used to take only one of them to assemble fast implementations due to the hardness of combining them together hence no joint exploitation of these techniques has been proposed to construct a new cutting edge implementation that solves these problems jointly employing five techniques kernel truncation best n term approximation as well as previous 2d box filtering dimension promotion and shiftability property we propose a unified framework to transform bf with arbitrary spatial and range kernels into a set of 3d box filters that can be computed in linear time to the best of our knowledge our algorithm is the first method that can integrate all these acceleration techniques and therefore can draw upon one another s strong point to overcome deficiencies the strength of our method has been corroborated by several carefully designed experiments in particular the filtering accuracy is significantly improved without sacrificing the efficiency at running time

accurate junction detection and characterization in natural images accurate junction detection and characterization are of primary importance for several aspects of scene analysis including depth recovery and motion analysis in this work we introduce a generic junction analysis scheme the first asset of the proposed procedure is an automatic criterion for the detection of junctions permitting to deal with textured parts in which no detection is expected second the method yields a characterization of l y and x junctions including a precise computation of their type localization and scale contrary to classical approaches scale characterization does not rely on the linear scale space first an a contrario approach is used to compute the meaningfulness of a junction this approach relies on a statistical modeling of suitably normalized gray level gradients then exclusion principles between junctions permit their precise characterization we give implementation details for this procedure and evaluate its efficiency through various experiments

fast domain decomposition for global image smoothing edge preserving smoothing eps can be formulated as minimizing an objective function that consists of data and regularization terms at the price of high computational cost this global eps approach is more robust and versatile than a local one that typically has a form of weighted averaging in this paper we introduce an efficient decomposition based method for global eps that minimizes the objective function of l 2 data and possibly non smooth and non convex regularization terms in linear time different from previous decomposition based methods which require solving a large linear system our approach solves an equivalent constrained optimization problem resulting in a sequence of 1 d sub problems this enables applying fast linear time solver for weighted least squares and l 1 smoothing problems an alternating direction method of multipliers algorithm is adopted to guarantee fast convergence our method is fully parallelizable and its runtime is even comparable to the state of the art local eps approaches we also propose a family of fast majorization minimization algorithms that minimize an objective with non convex regularization terms experimental results demonstrate the effectiveness and flexibility of our approach in a range of image processing and computational photography applications

on the equivalence of soft wavelet shrinkage total variation diffusion total variation regularization and sides soft wavelet shrinkage total variation tv diffusion tv regularization and a dynamical system called sides are four useful techniques for discontinuity preserving denoising of signals and images in this paper we investigate under which circumstances these methods are equivalent in the one dimensional case first we prove that haar wavelet shrinkage on a single scale is equivalent to a single step of space discrete tv diffusion or regularization of two pixel pairs in the translationally invariant case we show that applying cycle spinning to haar wavelet shrinkage on a single scale can be regarded as an absolutely stable explicit discretization of tv diffusion we prove that space discrete tv diffusion and tv regularization are identical and that they are also equivalent to the sides system when a specific force function is chosen afterwards we show that wavelet shrinkage on multiple scales can be regarded as a single step diffusion filtering or regularization of the laplacian pyramid of the signal we analyze possibilities to avoid gibbs like artifacts for multiscale haar wavelet shrinkage by scaling the thresholds finally we present experiments where hybrid methods are designed that combine the advantages of wavelets and pde variational approaches these methods are based on iterated shift invariant wavelet shrinkage at multiple scales with scaled thresholds

multipatch unbiased distance non local adaptive means with wavelet shrinkage many existing non local means nlm methods either use euclidean distance to measure the similarity between patches or compute weight omega ij only once and keep it unchanged during the subsequent denoising iterations or use only the structure information of the denoised image to update weight omega ij these may lead to the limited denoising performance to address these issues this paper proposes the non local adaptive means nlam for image denoising nlam treats weight omega ij as an optimization variable and iteratively updates its value we then introduce three unbiased distances namely pixel pixel patch patch and coupled unbiased distances these unbiased distances are more robust to measure the image pixel patch similarity than euclidean distance using the coupled unbiased distance we propose the unbiased distance non local adaptive means ud nlam because ud nlam uses only a single patch size to compute weight omega ij we introduce multipatch ud nlam mud nlam to adapt different noise levels to further improve denoising performance we then propose a new denoising method called mud nlam with wavelet shrinkage mud nlam ws experimental results show that the proposed nlam ud nlam and mud nlam outperform existing nlm methods and mud nlam ws achieves a better performance than the state of the art denoising methods

patch based near optimal image denoising in this paper we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising insights from that study are used here to derive a high performance practical denoising algorithm we propose a patch based wiener filter that exploits patch redundancy for image denoising our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters we describe how these parameters can be accurately estimated directly from the input noisy image our denoising approach designed for near optimal performance in the mean squared error sense has a sound statistical foundation that is analyzed in detail the performance of our approach is experimentally verified on a variety of images and noise levels the results presented here demonstrate that our proposed method is on par or exceeding the current state of the art both visually and quantitatively

on a cross diffusion system arising in image denoising abstract we study a generalization of a cross diffusion problem deduced from a nonlinear complex variable diffusion model for signal and image denoising we prove the existence of weak solutions of the time independent problem with fidelity terms under mild conditions on the data problem then we show that this translates on the well posedness of a quasi steady state approximation of the evolution problem and also prove the existence of weak solutions of the latter under more restrictive hypothesis we finally perform some numerical simulations for image denoising comparing the performance of the cross diffusion model to other well known denoising methods

junction detection for linear structures based on hessian correlation and shape information junctions have been demonstrated to be important features in many visual tasks such as image registration matching and segmentation as they can provide reliable local information this paper presents a method for detecting junctions in 2d images with linear structures as well as providing the number of branches and branch orientations the candidate junction points are selected through a new measurement which combines hessian information and correlation matrix then the locations of the junction centers are refined and the branches of the junctions are found using the intensity information of a stick shaped window at a number of orientations and the correlation value between the intensity of a local region and a gaussian shaped multi scale stick template the multi scale template is used here to detect the structures with various widths we present the results of our algorithm on images of different types and compare our algorithm with three other methods the results have shown that the proposed approach can detect junctions more accurately

using canny s criteria to derive a recursively implemented optimal edge detector a highly efficient recursive algorithm for edge detection is presented using canny s design 1 we show that a solution to his precise formulation of detection and localization for an infinite extent filter leads to an optimal operator in one dimension which can be efficiently implemented by two recursive filters moving in opposite directions in addition to the noise truncature immunity which results the recursive nature of the filtering operations leads with sequential machines to a substantial saving in computational effort five multiplications and five additions for one pixel independent of the size of the neighborhood the extension to the two dimensional case is considered and the resulting filtering structures are implemented as two dimensional recursive filters hence the filter size can be varied by simply changing the value of one parameter without affecting the time execution of the algorithm performance measures of this new edge detector are given and compared to canny s filters various experimental results are shown

on the midrange estimator an error criterion for the design of an estimator based on extreme order statistics namely the midrange is defined this approach is taken to overcome the fact that the variance of the midrange estimate is strongly dependent on the input distribution moreover under this performance criterion nonparametric characteristics for the design of these estimators are found

junctions detection classification and reconstruction junctions are important features for image analysis and form a critical aspect of image understanding tasks such as object recognition we present a unified approach to detecting classifying and reconstructing junctions in images our main contribution is a modeling of the junction which is complex enough to handle all these issues and yet simple enough to admit an effective dynamic programming solution we use a template deformation framework along with a gradient criterium to detect radial partitions of the template we use the minimum description length principle to obtain the optimal number of partitions that best describes the junction the kona detector presented by parida et al 1997 is an implementation of this model we demonstrate the stability and robustness of the detector by analyzing its behavior in the presence of noise using synthetic controlled apparatus we also present a qualitative study of its behavior on real images

junction detection and grouping with probabilistic edge models and bayesian a in this paper we propose and integrate two bayesian methods one of them for junction detection and the other one for junction grouping our junction detection method relies on a probabilistic edge model and a log likelihood test our junction grouping method relies on 6nding connecting paths between pairs of junctions path searching is performed by applying a bayesian a algorithm such algorithm uses both an intensity and geometric model for de6ning the rewards of a partial path and prunes those paths with low rewards we have extended such a pruning with an additional rule which favors the stability of longer paths against shorter ones we have tested experimentally the e ciency and robustness of the methods in an indoor image sequence 2002 pattern recognition society published by elsevier science ltd all rights reserved

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

complete synchronization of boolean networks we examine complete synchronization of two deterministic boolean networks bns coupled unidirectionally in the drive response configuration a necessary and sufficient criterion is presented in terms of algebraic representations of bns as a consequence we show that complete synchronization can occur only between two conditionally identical bns when the transition matrix of the drive network is nonsingular two examples are worked out to illustrate the obtained results

a new method for linear feature and junction enhancement in 2d images based on morphological operation oriented anisotropic gaussian function and hessian information abstract feature enhancement is an important preprocessing step in many image processing tasks it is the process of adjusting image intensities so that the enhanced results are more suitable for analysis good enhancement results for linear structures such as vessels or neurites can be used as inputs for segmentation and other operations in this paper a novel linear feature enhancement filter an adaptive multi scale morpho gaussian filter which can enhance and smooth linear features is proposed based on morphological operation anisotropic gaussian function and hessian information this filter can enhance and smooth along the local orientation of the linear structures and the hessian measurement is used to further enhance the linear features we utilize the hessian matrix to calculate the orientation information for our directional morphological operation and the oriented anisotropic gaussian smoothing we also propose a novel method for junction enhancement which can solve the problem of junction suppression we decompose the junctions and enhance along each linear structure within a junction region we present the test results of our algorithm on images of different types and compare our method with three existing methods the experimental results show that the proposed approach can achieve better results

control of cellular automata we study the problem of master slave synchronization and control of totalistic cellular automata the synchronization mechanism is that of setting a fraction of sites of the slave system equal to those of the master one pinching synchronization the synchronization observable is the distance between the two configurations we present three control strategies that exploit local information the number of nonzero first order boolean derivatives in order to choose the sites to be synchronized when no local information is used we speak of simple pinching synchronization we find the critical properties of control and discuss the best control strategy compared with simple synchronization

investigation of the global dynamics of cellular automata using boolean derivatives global dynamics of a non linear cellular automaton ca is in general irregular asymmetric and unpredictable as opposed to that of a linear ca which is highly systematic and tractable in this paper efforts have been made to systematize non linear ca evolutions in the light of boolean derivatives and jacobian matrices a few new theorems on hamming distance between boolean functions as well as on jacobian matrices of cellular automata are proposed and proved moreover a classification of boolean functions based on the nature of deviation from linearity has been suggested with a view to grouping them together to classes subclasses such that the members of a class subclass satisfy certain similar properties next an error vector which cannot be captured by the jacobian matrix is identified and systematically classified this leads us to the concept of modified jacobian matrix whereby a quasi affine representation of a non linear cellular automaton is introduced

synchronization and maximum lyapunov exponents of cellular automata we study the synchronization of totalistic one dimensional cellular automata ca the ca with a nonzero synchronization threshold exhibit complex nonperiodic space time patterns and vice versa this synchronization transition is related to directed percolation we also study the maximum lyapunov exponent for ca defined analogous to continuous dynamical systems as the exponential rate of expansion of the linear map induced by the evolution rule of ca constructed with the aid of the boolean derivatives the synchronization threshold is strongly correlated to the maximum lyapunov exponent and we propose approximate relations between these quantities the value of this threshold can be used to parametrize the space time complexity of ca

outlier robust corner preserving methods for reconstructing noisy images the ability to remove a large amount of noise and the ability to preserve most structure are desirable properties of an image smoother unfortunately they usually seem to be at odds with each other one can only improve one property at the cost of the other by combining m smoothing and least squares trimming the tm smoother is introduced as a means to unify corner preserving properties and outlier robustness to identify edge and corner preserving properties a new theory based on differential geometry is developed further robustness concepts are transferred to image processing in two examples the tm smoother outperforms other corner preserving smoothers a software package containing both the tm and the m smoother can be downloaded from the internet

contrast invariant interest point detection by zero norm log filter the laplacian of gaussian log filter is widely used in interest point detection however low contrast image structures though stable and significant are often submerged by the high contrast ones in the response image of the log filter and hence are difficult to be detected to solve this problem we derive a generalized log filter and propose a zero norm log filter the response of the zero norm log filter is proportional to the weighted number of bright dark pixels in a local region which makes this filter be invariant to the image contrast based on the zero norm log filter we develop an interest point detector to extract local structures from images compared with the contrast dependent detectors such as the popular scale invariant feature transform detector the proposed detector is robust to illumination changes and abrupt variations of images experiments on benchmark databases demonstrate the superior performance of the proposed zero norm log detector in terms of the repeatability and matching score of the detected points as well as the image recognition rate under different conditions

a short time beltrami kernel for smoothing images and manifolds we introduce a short time kernel for the beltrami image enhancing flow the flow is implemented by convolving the image with a space dependent kernel in a similar fashion to the solution of the heat equation by a convolution with a gaussian kernel the kernel is appropriate for smoothing regular flat 2 d images for smoothing images painted on manifolds and for simultaneously smoothing images and the manifolds they are painted on the kernel combines the geometry of the image and that of the manifold into one metric tensor thus enabling a natural unified approach for the manipulation of both additionally the derivation of the kernel gives a better geometrical understanding of the beltrami flow and shows that the bilateral filter is a euclidean approximation of it on a practical level the use of the kernel allows arbitrarily large time steps as opposed to the existing explicit numerical schemes for the beltrami flow in addition the kernel works with equal ease on regular 2 d images and on images painted on parametric or triangulated manifolds we demonstrate the denoising properties of the kernel by applying it to various types of images and manifolds

two bayesian methods for junction classification we propose two bayesian methods for junction classification which evolve from the kona method a region based method and an edge based method our region based method computes a one dimensional 1 d profile where wedges are mapped to intervals with homogeneous intensity these intervals are found through a growing and merging algorithm driven by a greedy rule on the other hand our edge based method computes a different profile which maps wedge limits to peaks of contrast and these peaks are found through thresholding followed by nonmaximum suppression experimental results show that both methods are more robust and efficient than the kona method and also that the edge based method outperforms the region based one

moment preserving corner detection abstract a new corner detection method based on the principle of preserving gray and mass moments is proposed for each circular region in a digital image bilevel thresholding based on gray moment preserving is used first to transform the blurred subimage in the region into a binary picture two approximate boundary points on the circumference of the thresholded circular region are obtained next by simple traversal an approximate corner point is estimated from the geometric relation between the centroid and the three vertices of the triangular lamina formed by the corner pattern within the circular region these points are then used as an initial guess for solving a system of nonlinear equations that are constructed by preserving the area and the three mass moments of the partial object lamina within the circular region more accurate corner boundary points can finally be determined from which not only the location but also the orientation as well as the angle of the corner can be computed experimental results show the effectiveness of the proposed method

patch based video denoising with optical flow estimation a novel image sequence denoising algorithm is presented the proposed approach takes advantage of the self similarity and redundancy of adjacent frames the algorithm is inspired by fusion algorithms and as the number of frames increases it tends to a pure temporal average the use of motion compensation by regularized optical flow methods permits robust patch comparison in a spatiotemporal volume the use of principal component analysis ensures the correct preservation of fine texture and details an extensive comparison with the state of the art methods illustrates the superior performance of the proposed approach with improved texture and detail reconstruction

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

ror rejection of outliers by rotations we address the problem of rejecting false matches of points between two perspective views the two views are taken from two arbitrary unknown positions and orientations we present an algorithm for identification of the false matches between the views the algorithm exploits the possibility of rotating one of the images to achieve some common behavior of the correct matches those matches that deviate from this common behavior turn out to be false matches our algorithm does not in any way use the image characteristics of the matched features in particular it avoids problems that cause the false matches in the first place the algorithm works even in cases where the percentage of false matches is as high as 85 percent the algorithm may be run as a post processing step on output from any point matching algorithm use of the algorithm may significantly improve the ratio of correct matches to incorrect matches we present the algorithm identify the conditions under which it works and present results of the test

fast bilateral filtering for denoising large 3d images a fast implementation of bilateral filtering is presented which is based on an optimal expansion of the filter kernel into a sum of factorized terms these terms are computed by minimizing the expansion error in the mean square error sense this leads to a simple and elegant solution in terms of eigenvectors of a square matrix in this way the bilateral filter is applied through computing a few gaussian convolutions for which very efficient algorithms are readily available moreover the expansion functions are optimized for the histogram of the input image leading to improved accuracy it is shown that this further optimization it made possible by removing the commonly deployed constrain of shiftability of the basis functions experimental validation is carried out in the context of digital rock imaging results on large 3d images of rock samples show the superiority of the proposed method with respect to other fast approximations of bilateral filtering

a robust approach for object matching and classification using partial dominant orientation descriptor this paper introduces a novel approach to measure the correspondence between objects and exploit it for object and image classification tasks using the proposed partial dominant orientation descriptor pdod in particular the object is represented by a set of stable and informative key locations sampled using difference of gaussian the proposed pdod at each extracted key location takes into account the position and partially computes the dominant orientation of other key locations relative to it thus offering a global distinctive and discriminative characterization this allows us to learn features that are largely invariant to common image transformations including changes in object colors and textures the correspondence in between two objects is performed by finding for each key location in one object the key location in the other object that has the most similar descriptor object classification proceeds by assigning the most relevant category that has maximally similar stored prototype objects to the query object using k nearest neighbors algorithm with adaptive object distance for efficiency we further investigate pdod for image classification by developing powerful image representations based on the popular bag of words model the extensive experiments demonstrate that the proposed approach greatly improves the matching and classification results while achieving the state of the art performances on several challenging benchmark datasets the obtained results suggest also broader applicability to other classification modalities we propose a novel descriptor for complex object matching and classification the proposed descriptor combines both local and global properties of interest points the proposed descriptor is highly distinctive and insensitive to common image transformations adaptive object distance is proposed to measure the similarity between objects we compare our approach with many existing methods and we achieve the state of the art performances on several benchmarks

noise estimation from digital step model signal this paper addresses the noise estimation in the digital domain and proposes a noise estimator based on the step signal model it is efficient for any distribution of noise because it does not rely only on the smallest amplitudes in the signal or image the proposed approach uses polarized directional derivatives and a nonlinear combination of these derivatives to estimate the noise distribution e g gaussian poisson speckle etc the moments of this measured distribution can be computed and are also calculated theoretically on the basis of noise distribution models the 1d performances are detailed and as this paper is mostly dedicated to image processing a 2d extension is proposed the 2d performances for several noise distributions and noise models are presented and are compared with selected other methods

scaling behavior in probabilistic neuronal cellular automata we study a neural network model of interacting stochastic discrete two state cellular automata on a regular lattice the system is externally tuned to a critical point which varies with the degree of stochasticity or the effective temperature there are avalanches of neuronal activity namely spatially and temporally contiguous sites of activity a detailed numerical study of these activity avalanches is presented and single joint and marginal probability distributions are computed at the critical point we find that the scaling exponents for the variables are in good agreement with a mean field theory

self similarity and points of interest in this work we present a new approach to interest point detection different types of features in images are detected by using a common computational concept the proposed approach considers the total variability of local regions the total sum of squares computed on the intensity values of a local circular region is divided into three components between circumferences sum of squares between radii sum of squares and the remainder these three components normalized by the total sum of squares represent three new saliency measures namely radial tangential and residual the saliency measures are computed for regions with different radii and scale spaces are built in this way local extrema in scale space of each of the saliency measures are located they represent features with complementary image properties blob like features corner like features and highly textured points results obtained on image sets of different object classes and image sets under different types of photometric and geometric transformations show high robustness of the method to intraclass variations as well as to different photometric transformations and moderate geometric transformations and compare favorably with the results obtained by the leading interest point detectors from the literature the proposed approach gives a rich set of highly distinctive local regions that can be used for object recognition and image matching

edge preserving image denoising with a closed form solution this paper addresses the problem of image denoising which is still a valid challenge at the crossing of functional analysis and statistics we herein propose a novel pixel based algorithm which formulates the image denoising problem as the maximum a posterior map estimation problem using markov random fields mrfs such an map estimation problem is equivalent to a maximum likelihood ml estimation constrained on spatial homogeneity and is np hard in discrete domain to make it tractable we convert it to a continuous label assignment problem based on a gaussian mrf model and then obtain a closed form globally optimal solution since the gaussian mrfs tend to over smooth images and blur edges our algorithm incorporates the pre estimated image edge information into the energy function construction and therefore better preserves the image structures in the algorithm patch similarity based pairwise interaction is also involved to better preserve image details and make the algorithm more robust to noise based on the theoretical analysis on the deviation caused by the discretization from obtained continuous global optimum to discrete output we demonstrate the guaranteed optimal property of our algorithm both quantitative and qualitative comparative experimental results are given to demonstrate the better performance of our algorithm over several existing state of the art related algorithms

interpreting line drawings of curved objects in this thesis we study the problem of interpreting line drawings of scenes composed or opaque regular solid objects bounded by piecewise smooth surfaces with no markings or texture on them it is assumed that the line drawing has been formed by orthographic projection of such a scene under general viewpoint that the line drawing is error free and that there are no lines due to shadows or specularities our definition implicitly excludes laminae wires and the apices of cones a major component of the interpretation of line drawings is line labelling by line labelling we mean a classification of each image curve as corresponding to either a depth or orientation discontinuity in the scene and b further subclassification of each kind of discontinuity for a depth discontinuity we determine whether it is a limb a locus of points on the surface where the line of sight is tangent to the surface or an occluding edge a tangent plane discontinuity of the surface for an orientation discontinuity we determine whether it corresponds to a convex or concave edge this thesis presents the first mathematically rigorous scheme for labelling line drawings of the class of scenes described by analysing the projection of the neighborhoods of different kinds of points on a piecewise smooth surface we are able to catalog all local labelling possibilities for the different types of junctions in a line drawing an algorithm is developed which utilizes this catalog to determine all legal labellings of the line drawing a local minimum complexity rule at each vertex select those labellings which correspond to the minimum number of faces meeting at the vertex is used in order to prune highly counter intuitive interpretations the labelling scheme was implemented and tested on a number of line drawings the labellings obtained are few and by and large in accordance with human interpretations

image categorization graph edit distance edge direction histogram this paper presents a novel algorithm for computing graph edit distance ged in image categorization this algorithm is purely structural i e it needs only connectivity structure of the graph and does not draw on node or edge attributes there are two major contributions 1 introducing edge direction histogram edh to characterize shape features of images it is shown that ged can be employed as distance of edhs this algorithm is completely independent on cost function which is difficult to be defined exactly 2 computing distance of edhs with earth mover distance emd which takes neighborhood bins into account so as to compute distance of edhs correctly a set of experiments demonstrate that the newly presented algorithm is available for classifying and clustering images and is immune to the planar rotation of images compared with ged from spectral seriation our algorithm can capture the structure change of graphs better and consume 12 79 time used by the former one the average classification rate is 5 and average clustering rate is 25 higher than the spectral seriation method

the failure of the artin rees property for the jacobson radical in prime noetherian rings abstract we provide an example of a prime noetherian ring whose jacobson radical fails to satisfy the artin rees ar property on either side the ring constructed is a finite module over its noetherian centre this settles a longstanding question

recognize objects with three kinds of information in landmarks abstract object recognition is one of the most important tasks in computer vision domain and it has been extensively applied in many areas during the last three decades in this paper a method is proposed to recognize objects in shape spaces in which landmarks have three kinds of information i e landmarks position angle and feature vector by comparing angle and feature vector a landmark in an image can correspond with a few number of landmarks in another image then the pixel s positions of landmarks from two images are used to calculate the procrustean distance in a shape space if the value of the procrustean distance is smaller than a predefined threshold the two sets of landmarks will be considered to match each other applying the proposed method two dimensional objects including occluded objects can be recognized quickly

optimal matching problem in detection and recognition performance evaluation abstract this paper proposes a principle of one to one correspondence in performance evaluation of a general class of detection and recognition algorithms such a correspondence between ground truth entities and algorithm declared entities is essential in accurately computing objective performance measures such as the detection recognition and false alarm rates we mathematically define the correspondence by formulating a combinatorial optimal matching problem in addition to evaluating detection performance this methodology is also capable of evaluating recognition performance our study shows that the proposed principle for detection performance evaluation is simple general and mathematically sound the derived performance evaluation technique is widely applicable precise consistent and efficient

topology induced phase transitions in totalistic cellular automata abstract notwithstanding numerous papers are dedicated to the intriguing dynamics of cellular automata cas which are mathematical models in which the time space and state domain are discrete in most of them the discussion is confined to the sensitivity of a ca s dynamical properties to the imposed initial conditions since the dynamics of a ca relies on both the states of its spatial entities and the interconnections between them to which we refer as a ca s topology it is striking that the influence of the latter on the evolved dynamics has received only minor attention to fill this gap we investigate in this paper how a ca s topology affects its dynamical properties by relying on lyapunov exponents we demonstrate the existence of so called topology induced phase transitions and topological bifurcation points which lead us to discriminate between intrinsically complex cas on the one hand and topology mediated complex cas on the other hand

focus of attention from local color symmetries in this paper a continuous valued measure for local color symmetry is introduced the new algorithm is an extension of the successful gray value based symmetry map proposed by reisfeld et al the use of color facilitates the detection of focus points fps on objects that are difficult to detect using gray value contrast only the detection of fps is aimed at guiding the attention of an object recognition system therefore fps have to fulfill three major requirements stability distinctiveness and usability the proposed algorithm is evaluated for these criteria and compared with the gray value based symmetry measure and two other methods from the literature stability is tested against noise object rotation and variations of lighting as a measure for the distinctiveness of fps the principal components of fp centered windows are compared with those of windows at randomly chosen points on a large database of natural images finally usability is evaluated in the context of an object recognition task

local noise weighted filtering for emphysema scoring of low dose ct images computed tomography ct has become the new reference standard for quantification of emphysema the most popular measure of emphysema derived from ct is the pixel index pi which expresses the fraction of the lung volume with abnormally low intensity values as pi is calculated from a single fixed threshold on intensity this measure is strongly influenced by noise this effect shows up clearly when comparing the pi score of a high dose scan to the pi score of a low dose i e noisy scan of the same subject in this paper the noise variance nova filter is presented a general framework for iterative nonlinear filtering which uses an estimate of the spatially dependent noise variance in an image the nova filter iteratively estimates the local image noise and filters the image for the specific purpose of emphysema quantification of low dose ct images a dedicated noniterative nova filter is constructed by using prior knowledge of the data to obtain a good estimate of the spatially dependent noise in an image the performance of the nova filter is assessed by comparing characteristics of pairs of high dose and low dose scans the compared characteristics are the pi scores for different thresholds and the size distributions of emphysema bullae after filtering the pi scores of high dose and low dose images agree to within 2 3 points the reproducibility of the high dose bullae size distribution is also strongly improved nova filtering of a ct image of typically 400 spl times 512 spl times 512 voxels takes only a couple of minutes which makes it suitable for routine use in clinical practice

local variance controlled forward and backward diffusion for image enhancement and noise reduction in order to improve signal to noise ratio snr and contrast to noise ratio this paper introduces a local variance controlled forward and backward lvcfab diffusion algorithm for edge enhancement and noise reduction in our algorithm an alternative fab diffusion algorithm is proposed the results for the alternative fab algorithm show better algorithm behavior than other existing diffusion fab approaches furthermore two distinct discontinuity measures and the alternative fab diffusion are incorporated into a lvcfab diffusion algorithm where the joint use of the two measures leads to a complementary effect for preserving edge features in digital images this lvc mechanism adaptively modifies the degree of diffusion at any image location and is dependent on both local gradient and inhomogeneity qualitative experiments based on general digital images and magnetic resonance images show significant improvements when the lvcfab diffusion algorithm is used versus the existing anisotropic diffusion and the previous fab diffusion algorithms for enhancing edge features and improving image contrast quantitative analyses based on peak snr confirm the superiority of the proposed lvcfab diffusion algorithm

efficient nonlocal means for denoising of textural patterns this paper contributes two novel techniques in the context of image restoration by nonlocal filtering first we introduce an efficient implementation of the nonlocal means filter based on arranging the data in a cluster tree the structuring of data allows for a fast and accurate preselection of similar patches in contrast to previous approaches the preselection is based on the same distance measure as used by the filter itself it allows for large speedups especially when the search for similar patches covers the whole image domain i e when the filter is truly nonlocal however also in the windowed version of the filter the cluster tree approach compares favorably to previous techniques in respect of quality versus computational cost second we suggest an iterative version of the filter that is derived from a variational principle and is designed to yield nontrivial steady states it reveals to be particularly useful in order to restore regular textured patterns

geometrically invariant watermarking using feature points this paper presents a new approach for watermarking of digital images providing robustness to geometrical distortions the weaknesses of classical watermarking methods to geometrical distortions are outlined first geometrical distortions can be decomposed into two classes global transformations such as rotations and translations and local transformations such as the stirmark attack an overview of existing self synchronizing schemes is then presented theses schemes can use periodical properties of the mark invariant properties of transforms template insertion or information provided by the original image to counter geometrical distortions thereafter a new class of watermarking schemes using the image content is presented we propose an embedding and detection scheme where the mark is bound with a content descriptor defined by salient points three different types of feature points are studied and their robustness to geometrical transformations is evaluated to develop an enhanced detector the embedding of the signature is done by extracting feature points of the image and performing a delaunay tessellation on the set of points the mark is embedded using a classical additive scheme inside each triangle of the tessellation the detection is done using correlation properties on the different triangles the performance of the presented scheme is evaluated after jpeg compression geometrical attack and transformations results show that the fact that the scheme is robust to these different manipulations finally in our concluding remarks we analyze the different perspectives of such content based watermarking scheme