eigenfaces vs fisherfaces recognition using class specific linear projection we develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression taking a pattern classification approach we consider each pixel in an image as a coordinate in a high dimensional space we take advantage of the observation that the images of a particular face under varying illumination but fixed pose lie in a 3d linear subspace of the high dimensional image space if the face is a lambertian surface without shadowing however since faces are not truly lambertian surfaces and do indeed produce self shadowing images will deviate from this linear subspace rather than explicitly modeling this deviation we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation our projection method is based on fisher s linear discriminant and produces well separated classes in a low dimensional subspace even under severe variation in lighting and facial expressions the eigenface technique another method based on linearly projecting the image space to a low dimensional subspace has similar computational requirements yet extensive experimental results demonstrate that the proposed fisherface method has error rates that are lower than those of the eigenface technique for tests on the harvard and yale face databases

a moment based unified approach to image feature detection in this paper a novel model based approach is proposed for generating a set of image feature maps or primal sketches for each type of feature a piecewise smooth parametric model is developed to characterize the local intensity function in an image projections of the intensity profile onto a set of orthogonal zernike moment generating polynomials are used to estimate model parameters and in turn generate the desired feature map a small set of moment based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images one main advantage of using parametric model based techniques is that it is possible to extract complete information i e model parameters about the underlying image feature which is desirable in many high level vision tasks experimental results are included to demonstrate the effectiveness of proposed feature detectors

comparing images using the hausdorff distance the hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa thus this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another efficient algorithms for computing the hausdorff distance between all possible relative positions of a binary image and a model are presented the focus is primarily on the case in which the model is only allowed to translate with respect to the image the techniques are extended to rigid motion the hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived the method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods it is shown that the method extends naturally to the problem of comparing a portion of a model against an image

indication of multiscaling in the volatility return intervals of stock markets a certain trend with the mean interval we generate surrogate records using the schreiber method and find that their cumulative distributions almost collapse to a single curve and moments are almost constant for most ranges of those substantial differences suggest that nonlinear correlations in the original volatility sequence account for the deviations from a single scaling law we also find that the original and surrogate records exhibit slight tendencies for short and long due to the discreteness and finite size effects of the records respectively to avoid as possible those effects for testing the multiscaling behavior we investigate the moments in the range 10100 and find that the exponent from the power law fitting m has a narrow distribution around 0 which depends on m for the 500 stocks the distribution of for the surrogate records are very narrow and centered around 0 this suggests that the return interval distribution exhibits multiscaling behavior due to the nonlinear correlations in the original volatility

shape similarity matching for query by example this paper describes a unified approach for two dimensional 2 d shape matching and similarity ranking of objects by means of a modal representation in particular we propose a new shape similarity metric in the eigenshape space for object image retrieval from a visual database via query by example this differs from prior work which performed point correspondence determination and similarity ranking of shapes in separate steps the proposed method employs selected boundary and or contour points of an object as a coarse to fine shape representation and does not require extraction of connected boundaries or silhouettes it is rotation translation and scale invariant and can handle mild deformations of objects e g due to partial occlusions or pose variations results comparing the unified method with an earlier two step approach using b spline based modal matching and hausdorff distance ranking are presented on retail and museum catalog style still image databases

robust hausdorff distance matching algorithms using pyramidal structures abstract this paper proposes two hausdorff distance hd matching algorithms in which robust hd measures are implemented in pyramidal structures by computer simulations the matching performance of the conventional hd measures and the proposed robust hd matching algorithms using pyramidal structures is compared with real images which are degraded by noise and occlusions

using angular dispersion of gradient direction for detecting edge ribbons edges in a scene generally project to smooth continuous curves nearly everywhere in the image which results in low angular deviation of the intensity gradients in small neighborhoods straddling edges angular deviation is shown to be a measure of snr a theoretical analysis of angular deviation arising due to independent and identically distributed n 0 sigma sup 2 random noise is presented angular deviation thresholds for neighborhood sizes from 3 3 to 11 11 pixels are determined both from this analysis and numerical examples the proposed gradient angular dispersion detection algorithm detects edge elements by comparing the measured angular deviation with values computed for the minimum acceptable snr low values of deviation violate the no edge hypothesis the algorithm is shown to make good use of the limited dynamic range of the imaging system the sensitivity and selectivity of the strategy are both shown to be high

long term memory in extreme returns of financial time series abstract it is well known that while daily price returns of financial markets are uncorrelated their absolute values volatility are long term correlated here we provide evidence that certain subsequences of the returns themselves also exhibit long term memory these subsequences consist of maxima or minima of returns in consecutive time windows of r days our analysis shows that for both stocks and currency exchange rates long term correlations are significant for r 4 we argue that this long term memory which is similar to that observed in volatility clustering sheds further insight on price dynamics that might be used for risk estimation

orthogonal moment operators for subpixel edge detection abstract a new approach to detect step edges with subpixel accuracy is presented the proposed approach is based on a set of orthogonal complex moments of the image known as zernike moments an ideal two dimensional 2d step edge is modeled in terms of four parameters the background gray level the step size the distance of the edge from the center of the mask and the orientation of the edge discrete zernike moments are used to obtain a total of three complex masks to compute all the edge parameters for subpixel detection for pixel level edge detection only two masks one real and one complex are required the theoretical analysis of the influence of noise on the location and the orientation of an edge is presented this analysis reveals that the accuracy of the proposed approach is virtually unaffected by the additive noise the technique is effective in detecting both the pixel level and subpixel level edges experimental results are presented to demonstrate the efficacy of the proposed technique

wave propagation in continuous periodic structures research contributions from southampton 1964 1995 abstract after brief reference to some early studies by other investigators this paper focuses mainly on methods developed at the university of southampton since 1964 to analyze and predict the free and forced wave motion in continuous periodic engineering structures beginning with receptance methods which have been applied to periodic beams and rib skin structures it continues with a method of direct solution of the wave equation this uses floquet s principle and has been applied to beams and quasi one dimensional periodic plates and cylindrical shells sample curves of the propagation and attenuation constants pertaining to these structures are presented a limited discussion of the transfer matrix then follows after which the method of space harmonics is introduced as the method best suited to the prediction of sound radiated from a vibrating periodic structure reviewed next are some theorems and variational principles relating to periodic structures which have been developed at southampton and which form a basis for finding natural frequencies of finite structures or for computing free and forced wave notion by energy methods this has led to the finite element method in its standard and hierarchical forms being used to study wave motion in genuine two dimensional and three dimensional structures examples of this work are shown the method of phased array receptance functions is then introduced as possibly the easiest way of setting up exact equations for the propagation constants of uniform quasi one dimensional periodic structures a summary is finally presented of the limited and early work performed at southampton on simple disordered periodic structures

statistical analysis of the overnight and daytime return we investigate the two components of the total daily return close to close the overnight return close to open and the daytime return open to close as well as the corresponding volatilities of the 2215 new york stock exchange stocks for the 20 year period from 1988 to 2007 the tail distribution of the volatility the long term memory in the sequence and the cross correlation between different returns are analyzed our results suggest that i the two component returns and volatilities have features similar to that of the total return and volatility the tail distribution follows a power law for all volatilities and long term correlations exist in the volatility sequences but not in the return sequences ii the daytime return contributes more to the total return both the tail distribution and the long term memory of the daytime volatility are more similar to that of the total volatility compared to the overnight records in addition the cross correlation between the daytime return and the total return is also stronger iii the two component returns tend to be anticorrelated moreover we find that the cross correlations between the three different returns total overnight and daytime are quite stable over the entire 20 year period

spatially eigen weighted hausdorff distances for human face recognition abstract hausdorff distance is an efficient measure of the similarity of two point sets in this paper we propose a new spatially weighted hausdorff distance measure for human face recognition the weighting function used in the hausdorff distance measure is based on an eigenface which has a large value at locations of importance facial features and can reflect the face structure more effectively two modified hausdorff distances namely spatially eigen weighted hausdorff distance sewhd and spatially eigen weighted doubly hausdorff distance sew2hd are proposed which incorporate the information about the location of important facial features such as eyes mouth and face contour so that distances at those regions will be emphasized experimental results based on a combination of the orl mit and yale face databases show that sew2hd can achieve recognition rates of 83 90 and 92 for the first one the first three and the first five likely matched faces respectively while the corresponding recognition rates of sewhd are 80 83 and 88 respectively

economic fluctuations and statistical physics quantifying extremely rare and less rare events in finance one challenge of economics is that the systems treated by these sciences have no perfect metronome in time and no perfect spatial architecture crystalline or otherwise nonetheless as if by magic out of nothing but randomness one finds remarkably fine tuned processes in time we present an overview of recent research joining practitioners of economic theory and statistical physics to try to better understand puzzles regarding economic fluctuations one of these puzzles is how to describe outliers phenomena that lie outside of patterns of statistical regularity we review evidence consistent with the possibility that such outliers may not exist this possibility is supported by recent analysis of databases containing information about each trade of every stock r 2007 elsevier b v all rights reserved

precision edge contrast and orientation estimation the contrast and orientation estimation accuracy of several edge operators that have been proposed in the literature is examined both for the noiseless case and in the presence of additive gaussian noise the test image is an ideal step edge that has been sampled with a square aperture grid the effects of subpixel translations and rotations of the edge on the performance of the operators are studied it is shown that the effect of subpixel translations of an edge can generate more error than moderate noise levels methods with improved results are presented for sobel angle estimates and the nevatia babu operator and theoretical noise performance evaluations are also provided an edge operator based on two dimensional spatial moments is presented all methods are compared according to worst case and rms error in an ideal noiseless situation and rms error under various noise levels

free vibration of an orthogonally stiffened cylindrical shell part i discrete line simple supports abstract the hierarchical finite element method is used to establish the stiffness and mass matrices of a cylindrically curved rectangular panel some natural frequencies and modes of two such panels each with different boundary conditions are then determined excellent agreement if found between this work and that of other investigators these stiffness and mass matrices are then combined with periodic structure theory to analyze an orthogonally stiffened cylindrical shell this analysis is formulated for a plane wave type of motion which is characterized by different propagation constants in the axial and circumferential directions the governing equations of free vibration are then solved as a matrix eigenvalue problem for the frequencies at which particular waves will propagate results are presented in the form of phase constant surface stacks and clearly show the qualitative effects of varying the major shell parameters b a r a and r h

line based recognition using a multidimensional hausdorff distance a line feature based approach for model based recognition using a four dimensional hausdorff distance is proposed this approach reduces the problem of finding the rotation scaling and translation transformations between a model and an image to the problem of finding a single translation minimizing the hausdorff distance between two sets of points in a four dimensional space the implementation of the proposed algorithm can be naturally extended to higher dimensional spaces to efficiently find correspondences between n dimensional patterns the method performance and sensitivity to segmentation problems are quantitatively characterized using an experimental protocol with simulated data it is shown that the algorithm performs well is robust to occlusion and outliers and that it degrades nicely as the segmentation problems increase experiments with real images are also presented

analysis of wave propagation in a thin composite cylinder with periodic axial and ring stiffeners using periodic structure theory wave propagation characteristics of a thin composite cylinder stiffened by periodically spaced ring frames and axial stringers are investigated by an analytical method using periodic structure theory it is used for calculating propagation constants in axial and circumferential directions of the cylindrical shell subject to a given circumferential mode or axial half wave number the propagation constants corresponding to several different circumferential modes and or half wave numbers are combined to determine the vibrational energy ratios between adjacent basic structural elements of the two dimensional periodic structure vibration analyses to validate the theoretical development have been carried out on sufficiently detailed finite element model of the same dimension and configuration as the stiffened cylinder and very good agreement is obtained between the analytical and the dense finite element results the effects of shell material properties and the length of each periodic element on the wave propagation characteristics are also examined based on the current analytical approach

robust and efficient image alignment based on relative gradient matching in this paper we present a robust image alignment algorithm based on matching of relative gradient maps this algorithm consists of two stages namely a learning based approximate pattern search and an iterative energy minimization procedure for matching relative image gradient the first stage finds some candidate poses of the pattern from the image through a fast nearest neighbor search of the best match of the relative gradient features computed from training database of feature vectors which are obtained from the synthesis of the geometrically transformed template image with the transformation parameters uniformly sampled from a given transformation parameter space subsequently the candidate poses are further verified and refined by matching the relative gradient images through an iterative energy minimization procedure this approach based on the matching of relative gradients is robust against nonuniform illumination variations experimental results on both simulated and real images are shown to demonstrate superior efficiency and robustness of the proposed algorithm over the conventional normalized correlation method

subpixel measurements using a moment based edge operator recent results in precision measurements using computer vision are presented an edge operator based on two dimensional spatial moments is given the operator can be implemented for virtually any size of window and has been shown to locate edges in digitized images to a twentieth of a pixel this accuracy is unaffected by additive or multiplicative changes to the data values the precision is achieved by correcting for many of the deterministic errors caused by nonideal edge profiles using a lookup table to correct the original estimates of edge orientation and location this table is generated using a synthesized edge which is located at various subpixel locations and various orientations the operator is extended to accommodate nonideal edge profiles and rectangularly sampled pixels the technique is applied to the measurement of imaged machined metal parts theoretical and experimental noise analyses show that the operator has relatively small bias in the presence of noise

robust fft based scale invariant image registration with image gradients we present a robust fft based approach to scale invariant image registration our method relies on fft based correlation twice once in the log polar fourier domain to estimate the scaling and rotation and once in the spatial domain to recover the residual translation previous methods based on the same principles are not robust to equip our scheme with robustness and accuracy we introduce modifications which tailor the method to the nature of images first we derive efficient log polar fourier representations by replacing image functions with complex gray level edge maps we show that this representation both captures the structure of salient image features and circumvents problems related to the low pass nature of images interpolation errors border effects and aliasing second to recover the unknown parameters we introduce the normalized gradient correlation we show that using image gradients to perform correlation the errors induced by outliers are mapped to a uniform distribution for which our normalized gradient correlation features robust performance exhaustive experimentation with real images showed that unlike any other fourier based correlation techniques the proposed method was able to estimate translations arbitrary rotations and scale factors up to 6

long memory behavior of returns after intraday financial jumps abstract in this paper characterization of intraday financial jumps and time dynamics of returns after jumps is investigated and will be analytically and empirically shown that intraday jumps are power law distributed with the exponent 1 2 in addition returns after jumps show long memory behavior in the theory of finance it is important to be able to distinguish between jumps and continuous sample path price movements and this can be achieved by introducing a statistical test via calculating sums of products of returns over small period of time in the case of having jump the null hypothesis for normality test is rejected this is based on the idea that returns are composed of mixture of normally distributed and power law distributed data 1 r 1 probability of rejection of null hypothesis is a function of which is equal to one for 1 2 within large intraday sample size m to test this idea empirically we downloaded sp and it represents a power law exponent in 2015 2016 greater than one in 1997 1998 assuming that i i d returns generally follow poisson distribution if the jump is a causal factor high returns after jumps are the effect we show that returns caused by jump decay as power law distribution to test this idea empirically we average over the time dynamics of all days therefore the superposed time dynamics after jump represent a power law which indicates that there is a long memory with a power law distribution of return after jump

quantifying fluctuations in market liquidity analysis of the bid ask spread quantifying the statistical features of the bid ask spread offers the possibility of understanding some aspects of market liquidity using quote data for the 116 most frequently traded stocks on the new york stock exchange over the two year period 1994 1995 we analyze the fluctuations of the average bid ask spread s over a time interval dt we find that s is characterized by a distribution that decays as a power law phs x j x zs with an exponent zs 3 for all 116 stocks analyzed our analysis of the autocorrelation function of s shows long range power law correlations ksstdsst tdl t ms similar to those previously found for the volatility we next examine the relationship between the bid ask spread and the volume q and find that s ln q we find that a similar logarithmic relationship holds between the transaction level bid ask spread and the trade size we then study the relationship between s and other indicators of market liquidity such as the frequency of trades n and the frequency of quote updates u and finds ln n and s ln u lastly we show that the bid ask spread and the volatility are also related logarithmically

improving classifier performance through repeated sampling abstract in traditional pattern recognition the classification decision is based on a single observation of the input in this paper we show that by relaxing this assumption the performance of the classifier can be improved substantially we present a detailed analysis of one particular method for achieving this taking a consensus vote on the classifier s output for repeated samples of the input we prove that this approach always yields a net improvement in recognition accuracy for common distributions of interest upper and lower bounds on the improvement are also discussed under certain conditions it is even possible to beat the bayes error bound associated with the classifier we conclude by presenting results from three sets of experiments examining the effectiveness of the idea

a new adaptive framework for unbiased orientation estimation in textured images this paper focuses on directional texture analysis we propose a new approach for orientation estimation this approach hinges on two classes of convolution masks i e the gradient and the valleyness operators we provide a framework for their optimization regarding bias reduction and noise robustness as the gradient and the valleyness operators are complementary we propose a combination named gv joe this combination consists in using the gradient on inflexion pixels the valleyness on crests and valleys and a linear mixture of both elsewhere we implement an adaptive selection of the size of our operators in order to take into account the variations of the texture scale in the image we apply our approach both on synthetic and natural textures these experiments show that when used separately both classes of operators are more accurate than classical derivative approaches in noisy cases the gv joe implementation improves the robustness of our operators without affecting their accuracy moreover compared to well known orientation estimators it gives the best estimates in the most difficult cases i e for high frequency textures and low snr

scaling and memory of intraday volatility return intervals in stock markets we study the return interval tau between price volatilities that are above a certain threshold q for 31 intraday data sets including the standard and poor s 500 index and the 30 stocks that form the dow jones industrial index for different threshold q the probability density function pq tau scales with the mean interval tau as formula see text similar to that found in daily volatilities since the intraday records have significantly more data points compared to the daily records we could probe for much higher thresholds and still obtain good statistics we find that the scaling function f x is consistent for all 31 intraday data sets in various time resolutions and the function is well approximated by the stretched exponential f x similar to e ax gamma with gamma 0 38 0 05 and a 3 9 0 5 which indicates the existence of correlations we analyze the conditional probability distribution pq tau tau0 for tau following a certain interval tau0 and find pq tau tau0 depends on tau0 which demonstrates memory in intraday return intervals also we find that the mean conditional interval tau tau0 increases with tau0 consistent with the memory found for pq tau tau0 moreover we find that return interval records in addition to having short term correlations as demonstrated by pq tau tau0 have long term correlations with correlation exponents similar to that of volatility records

line segment hausdorff distance on face matching abstract a novel concept of line segment hausdorff distance is proposed in this paper researchers apply hausdorff distance to measure the similarity of two point sets it is extended here to match two sets of line segments the new approach has the advantage to incorporate structural and spatial information to compute the similarity the added information can conceptually provide more and better distinctive capability for recognition this would strengthen and enhance the matching process of similar objects such as faces the proposed technique has been applied online segments generated from the edge maps of faces with encouraging result that supports the concept experimentally the results also implicate that line segments could provide sufficient information for face recognition this might imply a new way for face coding and recognition

comparing face images using the modified hausdorff distance abstract we introduce a novel methodology applicable to face matching and fast screening of large facial databases the proposed shape comparison method operates on edge maps and derives holistic similarity measures yet it does not require solving the point correspondence problem while the use of edge images is important to introduce robustness to changes in illumination the lack of point to point matching delivers speed and tolerance to local non rigid distortions in particular we propose a face similarity measure derived as a variant of the hausdorff distance by introducing the notion of a neighborhood function n and associated penalties p experimental results on a large set of face images demonstrate that our approach produces excellent recognition results even when less than 3 of the original grey scale face image information is stored in the face database gallery these results implicate that the process of face recognition may start at a much earlier stage of visual processing than it was earlier suggested we argue that edge like retinal images of faces are initially screened at a glance without the involvement of high level cognitive functions thus delivering high speed and reducing computational complexity

intravital leukocyte detection using the gradient inverse coefficient of variation the problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest currently methods exist for tracking rolling leukocytes in vivo but these methods rely on manual detection of the cells in this paper we propose a technique for accurately detecting rolling leukocytes based on bayesian classification the classification depends on a feature score the gradient inverse coefficient of variation gicov which serves to discriminate rolling leukocytes from a cluttered environment the leukocyte detection process consists of three sequential steps the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal gicov in the second step starting from each of the ellipses found in the first step a b spline snake is evolved to refine the leukocytes boundaries by maximizing the associated gicov score the third and final step retains only the extracted contours that have a gicov score above the analytically determined threshold experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods the proposed gicov method achieves 78 6 leukocyte detection accuracy with 13 1 false alarm rate

a method to detect and characterize ellipses using the hough transform we describe a new technique for detecting and characterizing ellipsoidal shapes automatically from any type of image this technique is a single pass algorithm which can extract any group of ellipse parameters or characteristics which can be computed from those parameters without having to detect all five parameters for each ellipsoidal shape moreover the method can explicitly incorporate any a priori knowledge the user may have concerning ellipse parameters the method is based on techniques from projective geometry and on the hough transform this technique can significantly reduce interpretation and computation time by automatically extracting only those features or geometric parameters of interest from images and making exact use of a priori information

texture synthesis using the structure tensor this paper proposes a two stage texture synthesis algorithm at the first stage a structure tensor map carrying information about the local orientation is synthesized from the exemplar s data and used at the second stage to constrain the synthesis of the texture keeping in mind that the algorithm should be able to reproduce as faithfully as possible the visual aspect statistics and morphology of the input sample the method is tested on various textures and compared objectively with existing methods highlighting its strength in successfully synthesizing the output texture in many situations where traditional algorithms fail to reproduce the exemplar s patterns the promising results pave the way towards the synthesis of accurately large and multi scale patterns as it is the case for carbon material samples showing laminar structures for example

object matching algorithms using robust hausdorff distance measures a hausdorff distance hd is one of commonly used measures for object matching this work analyzes the conventional hd measures and proposes two robust hd measures based on m estimation and least trimmed square lts which are more efficient than the conventional hd measures by computer simulation the matching performance of the conventional and proposed hd measures is compared with synthetic and real images

detection of composite edges the paper presents a new parametric model based approach to high precision composite edge detection using orthogonal zernike moment based operators it deals with two types of composite edges a generalized step and b pulse staircase edges a 2 d generalized step edge is modeled in terms of five parameters two gradients on two sides of the edge the distance from the center of the candidate pixel the orientation of the edge and the step size at the location of the edge a 2 d pulse staircase edge is modeled in terms of two steps located at two positions within the mask and the edge orientation a pulse edge is formed if the steps are of opposite polarities whereas a staircase edge results from two steps having the same polarity two complex and two real zernike moment based masks are designed to determine parameters of both the 2 d edge models for a given edge model estimated parameter values at a point are used to detect the presence or absence of that type of edge extensive noise analysis is performed to demonstrate the robustness of the proposed operators experimental results with intensity and range images are included to demonstrate the efficacy of the proposed edge detection technique as well as to compare its performance with the geometric moment based step edge detection technique and canny s 1986 edge detector

gradient orientation pattern matching with the hamming distance abstract this paper presents a novel pattern matching technique that is robust to illumination changes and the occlusion problem the technique is based on the matching of gradient orientations in place of traditional image features such as intensities or gradients gradient orientations depend on the texture in an image they are known to be insensitive to changes of image intensities that are often caused by time varying illuminations or the auto gain control agc function of the camera moreover the proposed method employs a voting strategy in the process of matching gradient orientations the method works remarkably well even when a large part of the pattern is occluded with a foreign object consequently the proposed method is robust to both irregular lighting conditions and the occlusion problem

the influence of edge direction on the estimation of edge contrast and orientation abstract this paper concerns the influence of edge direction on the estimation of edge contrast and orientation we present an analytical study of general first order detectors we show that the gradient estimated using radial filters is not affected by edge orientation for non radial filters the gradient may be affected by edge orientation for instance we find that the estimated edge orientation using a non radial filter may be biased even if the image is noise free however there are non radial filters for which gradient is unaffected by edge orientation as in the case of radial filters the properties of these functions are given in this paper the results are illustrated by studying the detectors of canny deriche and shen castan we take into account discretization effects such as subpixel and tesselation of the image plane the quantitative results provide insights into the estimation of gradient by these commonly used detectors taken together the theoretical and experimental results give a clear indication of the effect of the rotation invariance property of an edge detector on its response thus providing a more precise meaning for this property in edge detection

hybrid image matching combining hausdorff distance with normalized gradient matching image matching has been a central problem in computer vision and image processing for decades most of the previous approaches to image matching can be categorized into the intensity based and edge based comparison hausdorff distance has been widely used for comparing point sets or edge maps since it does not require point correspondences in this paper we propose a new image similarity measure combining the hausdorff distance with a normalized gradient consistency score for image matching the normalized gradient consistency score is designed to compare the normalized image gradient fields between two images to alleviate the illumination variation problem in image matching by combining the edge based and intensity based information for image matching we are able to achieve robust image matching under different lighting conditions we show the superior robustness property of the proposed image matching technique through experiments on face recognition under different lighting conditions

an adaptive image euclidean distance the image euclidean distance imed considers the spatial relationship between the pixels of different images and can easily be embedded in existing image recognition algorithms that are based on euclidean distance imed uses the prior knowledge that pixels located near one another have little variance in gray scale values and defines a metric matrix according to the spatial distance between pixels in this paper we propose an adaptive image euclidean distance aimed which considers not only the prior spatial knowledge but also the prior gray level knowledge from images the most important advantage of the proposed aimed over imed is that aimed makes the metric matrix adaptive to the content of the concerned images two ways of using gray level information are proposed one is based on gray level distances and the other is based on cosine dissimilarity of gray levels experiments on two facial databases and a handwritten digital database show that aimed achieves the highest classification accuracy when it is embedded in nearest neighbor classifiers principal component analysis and support vector machines

statistical properties of trading volume of chinese stocks abstract the cumulative distribution of trading volume is investigated for chinese stocks different from the power law scaling of mature markets the distribution is well fitted by a stretched exponential function f x e x with the autocorrelation function and the detrended fluctuation analysis the long range autocorrelation of trading volume is revealed the conditional dependence of volume on volatility and the volume volatility cross correlation are studied and a positive long range correlation between volume and volatility is observed