wavelet based corner detection abstract a non parametric algorithm for detecting and locating corners of planar curves is proposed the algorithm is based on the multiscale wavelet transform of the orientation of the curve which can effectively utilize both the information of local extrema positions and magnitudes of the transform results the corner candidates can then be selected easily based on this information according to the angle preserving concept intrinsic ratios of several corner models have been derived and used to evaluate the corner candidates the corner angles can also be obtained during these processes to make the evaluation process robust a masking algorithm is proposed experiments depict that our detector is more effective than the single scale corner detectors while is more efficient than the multiscale corner detector by rattarangsi and chin proc int conf pattern recognition pp 923 930 1990

random geometric graphs we analyze graphs in which each vertex is assigned random coordinates in a geometric space of arbitrary dimensionality and only edges between adjacent points are present the critical connectivity is found numerically by examining the size of the largest cluster we derive an analytical expression for the cluster coefficient which shows that the graphs are distinctly different from standard random graphs even for infinite dimensionality insights relevant for graph bipartitioning are included

enhancement of cooperation in highly clustered scale free networks we study the effect of clustering on the organization of cooperation by analyzing the evolutionary dynamics of the prisoner s dilemma on scale free networks with a tunable value of clustering we find on the one hand that a high value of the clustering coefficient produces an overall enhancement of cooperation in the network even for a very high temptation to defect on the other hand high clustering homogenizes the process of invasion of degree classes by defectors decreasing the chances of survival of low densities of cooperators in the network

a parallel algorithm for detecting dominant points on multiple digital curves abstract a new parallel algorithm is proposed for detecting corners of multiple planar curves or shapes this algorithm is based on an analysis of morphological residues and corner characteristics the method is different from traditional chain code based corner detection algorithms which associate with a chain coding and floating point computation for multiple objects in an image the proposed algorithm treats them uniformly and applies simultaneously on curves and shapes of multiple objects the experiments demonstrate that the algorithm can be effectively executed on an simd parallel computer a boundary constrained morphological method is presented for filling closed curves into shapes which are subsequently used for corner detection

robust image corner detection through curvature scale space this paper describes a novel method for image corner detection based on the curvature scale space css representation the first step is to extract edges from the original image using a canny detector 1986 the corner points of an image are defined as points where image edges have their maxima of absolute curvature the corner points are detected at a high scale of the css and tracked through multiple lower scales to improve localization this method is very robust to noise and we believe that it performs better than the existing corner detectors an improvement to canny edge detector s response to 45 spl deg and 135 spl deg edges is also proposed furthermore the css detector can provide additional point features curvature zero crossings of image edge contours in addition to the traditional corners

intensity weighted average of instantaneous frequency as a model for frequency discrimination the intensity weighted average of instantaneous frequency iwaif is developed as a model to predict listener performance in tasks primarily requiring frequency discrimination iwaif is closely related to the envelope weighted average of instantaneous frequency ewaif model proposed by feth for similar tasks the primary difference is that the iwaif model uses intensity envelope squared as the weighting function instead of the envelope the advantages of iwaif over ewaif are that a it has a convenient frequency domain interpretation and b it is much simpler to compute than the ewaif the iwaif is the center of gravity of the energy spectral density function of the signal

range data based object surface segmentation via edges and critical points a novel method for range image segmentation is presented in this paper it is based on an integration of edge and region information the algorithm consists of three steps edge and critical point detection triangulation and region growing experimental results show that the method is efficient for segmentation of the range images that contain polyhedral objects a three dimensional 3 d surface structure graph ssg obtained from the segmentation is a description of the surface structure about an object therefore a segmentation result also presents a data set that can be used to establish a surface model for computer aided design based cad based vision and object recognition

an improved corner detection algorithm based on chain coded plane curves abstract one possible measure of corners on chain coded plane curves is the product of the length of the uniform chain sections to either side the forward and backward arms of a point times the angle of discontinuity of that particular point this paper describes a corner detection algorithm which performs close to humans expectations the improved algorithm is based on previous work done by freeman and davis the algorithm uses an approach of averaging multiple s values to avoid the selection of spurious corners the algorithm also uses a maximum cut off value a certain fraction of the entire chain to determine the length of the forward and backward arms of each point and hence eliminates the detection of spurious corners the improved algorithm was tested with results close to humans expectations the results of the improved algorithm are shown by way of examples

communication scheme based on evolutionary spatial 2 2 games abstract a visual communication scheme based on evolutionary spatial 2 2 games is proposed in this paper self organizing patterns induced by complex interactions between competing individuals are exploited for hiding and transmitting secret visual information properties of the proposed communication scheme are discussed in details it is shown that the hiding capacity of the system the minimum size of the detectable primitives and the minimum distance between two primitives is sufficient for the effective transmission of digital dichotomous images also it is demonstrated that the proposed communication scheme is resilient to time backwards plain image attacks and is highly sensitive to perturbations of private and public keys several computational experiments are used to demonstrate the effectiveness of the proposed communication scheme

fast decomposition of digital curves into polygons using the haar transform the perimeters of features in digital images are decomposed into sets of straight lines using the haar transform the haar basis functions preserve sharp changes in direction along the perimeter while still allowing smoothing the coefficients of the haar transform are simply related to the error between the polygonal decomposition and the true perimeter the haar transform can be calculated efficiently with a computational overhead linear in the number of data points

an adaptive reduction procedure for the piecewise linear approximation of digitized curves a new algorithm is presented for the piecewise linear approximation of two dimensional digitized curves against a square grid the algorithm utilizes an adaptive reduction procedure in two approximation phases to select the critical points of a digitized curve such that the deviation from the digitized curve to its final approximated curve is bounded by a uniform error tolerance the time complexity of this algorithm is o m sup 2 rather than o n sup 2 on the theoretical plane in the experiments of fixing the initial and the final processing points the performance of the algorithm has been compared to those of three prominent other algorithms regarding the required number of critical points and the total execution time of the program of the four algorithms compared the present algorithm consistently has the shortest execution time of the program and it tends most to require as few critical points as the optimum algorithm that was tested

naming games in two dimensional and small world connected random geometric networks we investigate a prototypical agent based model the naming game on two dimensional random geometric networks the naming game a baronchelli et al j stat mech theory exp 2006 p06014 is a minimal model employing local communications that captures the emergence of shared communication schemes languages in a population of autonomous semiotic agents implementing the naming games with local broadcasts on random geometric graphs serves as a model for agreement dynamics in large scale autonomously operating wireless sensor networks further it captures essential features of the scaling properties of the agreement process for spatiallyembedded autonomous agents among the relevant observables capturing the temporal properties of the agreement process we investigate the cluster size distribution and the distribution of the agreement times both exhibiting dynamic scaling we also present results for the case when a small density of long range communication links are added on top of the random geometric graph resulting in a small world like network and yielding a significantly reduced time to reach global agreement we construct a finite size scaling analysis for the agreement times in this case

new method to find corner and tangent vertices in sketches using parametric cubic curves approximation some recent approaches have been presented as simple and highly accurate corner finders in the sketches including curves which is useful to support natural human computer interaction but these in most cases do not consider tangent vertices smooth points between two geometric entities present in engineering models what implies an important drawback in the field of design in this article we present a robust approach based on the approximation to parametric cubic curves of the stroke for further radius function calculation in order to detect corner and tangent vertices we have called our approach tangent and corner vertices detection tcvd and it works in the following way first corner vertices are obtained as minimum radius peaks in the discrete radius function where radius is obtained from differences second approximated piecewise parametric curves on the stroke are obtained and the analytic radius function is calculated then curves are obtained from stretches of the stroke that have a small radius finally the tangent vertices are found between straight lines and curves or between curves where no corner vertices are previously located the radius function to obtain curves is calculated from approximated piecewise curves which is much more noise free than discrete radius calculation several tests have been carried out to compare our approach to that of the current best benchmarked and the obtained results show that our approach achieves a significant accuracy even better finding corner vertices and moreover tangent vertices are detected with an accuracy near to 92 and a false positive rate near to 2

scale based detection of corners of planar curves a technique for detecting and localizing corners of planar curves is proposed the technique is based on gaussian scale space which consists of the maxima of absolute curvature of the boundary function presented at all scales the scale space of isolated simple and double corners is first analyzed to investigate the behavior of scale space due to smoothing and interactions between two adjacent corners the analysis shows that the resulting scale space contains line patterns that either persist terminate or merge with a neighboring line next the scale space is transformed into a tree that provides simple but concise representation of corners at multiple scales finally a multiple scale corner detection scheme is developed using a coarse to fine tree parsing technique the parsing scheme is based on a stability criterion that states that the presence of a corner must concur with a curvature maximum observable at a majority of scales experiments were performed to show that the scale space corner detector is reliable for objects with multiple size features and noisy boundaries and compares favorably with other corner detectors tested

performance comparisons of contour based corner detectors corner detectors have many applications in computer vision and image identification and retrieval contour based corner detectors directly or indirectly estimate a significance measure e g curvature on the points of a planar curve and select the curvature extrema points as corners while an extensive number of contour based corner detectors have been proposed over the last four decades there is no comparative study of recently proposed detectors this paper is an attempt to fill this gap the general framework of contour based corner detection is presented and two major issues curve smoothing and curvature estimation which have major impacts on the corner detection performance are discussed a number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets it is observed that while the detectors using indirect curvature estimation techniques are more robust the detectors using direct curvature estimation techniques are faster

on the detection of dominant points on digital curves a parallel algorithm is presented for detecting dominant points on a digital closed curve the procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve the procedure first determines the region of support for each point based on its local properties then computes measures of relative significance e g curvature of each point and finally detects dominant points by a process of nonmaximum suppression this procedure leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance but also on the precise determination of the region of support this solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms the inherent nature of scale space filtering in the procedure is addressed and the performance of the procedure is compared to those of several other dominant point detection algorithms using a number of examples

on the minimum audible difference in direct to reverberant energy ratio the goals of this study were to measure sensitivity to the direct to reverberant energy ratio d r across a wide range of d r values and to gain insight into which cues are used in the discrimination process the main finding is that changes in d r are discriminated primarily based on spectral cues temporal cues may be used but only when spectral cues are diminished or not available while sensitivity to interaural cross correlation is too low to be useful in any of the conditions tested these findings are based on an acoustic analysis of these variables and the results of two psychophysical experiments the first experiment employs wideband noise with two values for onset and offset times to determine the d r just noticeable difference at 10 0 10 and 20db d r this yielded substantially higher sensitivity to d r at 0 and 10db d r 2 3db than has been reported previously while sensitivity is much lower at 10 and 20db d r the second experiment consists of three parts where specific cues to d r are reduced or removed which enabled the specified rank ordering of the cues the acoustic analysis and psychophysical experiments also provide an explanation for the auditory horizon effect

universality of weak selection weak selection which means a phenotype is slightly advantageous over another is an important limiting case in evolutionary biology recently it has been introduced into evolutionary game theory in evolutionary game dynamics the probability to be imitated or to reproduce depends on the performance in a game the influence of the game on the stochastic dynamics in finite populations is governed by the intensity of selection in many models of both unstructured and structured populations a key assumption allowing analytical calculations is weak selection which means that all individuals perform approximately equally well in the weak selection limit many different microscopic evolutionary models have the same or similar properties how universal is weak selection for those microscopic evolutionary processes we answer this question by investigating the fixation probability and the average fixation time not only up to linear but also up to higher orders in selection intensity we find universal higher order expansions which allow a rescaling of the selection intensity with this we can identify specific models which violate linear weak selection results such as the one third rule of coordination games in finite but large populations

partial shape recognition a landmark based approach a method of recognizing partially occluded objects is presented in which each object is represented by a set of landmarks given a scene consisting of partially occluded objects a model object in the scene is hypothesized by matching the landmarks of the model with those in the scene a measure of similarity between two landmarks is needed to perform this matching a local shape measure sphericity is introduced it is shown that any invariant function under a similarity transformation is a function of the sphericity to match landmarks between the model and the scene a table of compatibility is constructed a technique known as hopping dynamic programming is described to guide the landmark matching through the compatibility table the location of the model in the scene is estimated with a least squares fit among the matched landmarks a heuristic measure is then computed to decide if the model is in the scene

spectrographic measurement of instantaneous frequency and the time dependent weighted average instantaneous frequency instantaneous frequency if is an important signal characteristic arising in many fields it is a concept intimately linked to time frequency analysis where it can be obtained from a time frequency distribution tfd as the first conditional moment in frequency suggesting that the if is the average frequency at each time however this interpretation is questionable since it is well known that the if often ranges beyond the spectral support of the signal in addition to obtain the if from a spectrogram which is one possible tfd a very wideband and thus severely spectrally distorted spectrogram must be used more reasonable bandwidths are investigated and in particular give the conditions by which the first conditional moment of a spectrogram can indeed be interpreted as the average frequency at each time under these conditions namely when the spectrogram bandwidth is less than the frequency separation of the signal components in the time frequency plane the spectrogram yields not the usual if but a time dependent weighted average instantaneous frequency waif of the signal also while the if and waif are generally different sometimes they are the same in particular when there is symmetry in the time frequency spectrum of the signal in that case the first conditional spectral moments of both wideband and narrow band spectrograms are the same and interpretable as the average frequency at each time

using dominant points and variants for profile extraction from chromosomes abstract the most accurate methods for automatic analysis of chromosomes under a light microscope today extract numerical features from band pattern profiles along their longitudinal axes the construction of a reliable axis is a crucial step in this process we propose a new way based on the dominant points of the contour and cubic splines the dominant points serve as candidates for the tips of the chromosome ambiguities are dissolved by the recently developed method of variants for object identification a voronoi diagram decomposes the chromosome in slices for profile extraction the method improves the currently best classification results significantly yielding a test set error rate of 0 61

the curvature primal sketch in this paper we introduce a novel representation of the significant changes in curvature along the bounding contour of planar shape we call the representation the curvature primal sketch because of the close analogy to the primal sketch representation advocated by marr for describing significant intensity changes we define a set of primitive parameterized curvature discontinuities and derive expressions for their convolutions with the first and second derivatives of a gaussian we describe an implemented algorithm that computes the curvature primal sketch by matching the multiscale convolutions of a shape and illustrate its performance on a set of tool shapes several applications of the representation are sketched

curvature and tangential deflection of discrete arcs a theory based on the commutator of scatter matrix pairs and its application to vertex detection in planar shape data this paper introduces a new theory for the tangential deflection and curvature of plane discrete curves our theory applies to discrete data in either rectangular boundary coordinate or chain coded formats its rationale is drawn from the statistical and geometric properties associated with the eigenvalue eigenvector structure of sample covariance matrices specifically we prove that the nonzero entry of the commutator of a piar of scatter matrices constructed from discrete arcs is related to the angle between their eigenspaces and further we show that this entry is in certain limiting cases also proportional to the analytical curvature of the plane curve from which the discrete data are drawn these results lend a sound theoretical basis to the notions of discrete curvature and tangential deflection and moreover they provide a means for computationally efficient implementation of algorithms which use these ideas in various image processing contexts as a concrete example we develop the commutator vertex detection cvd algorithm which identifies the location of vertices in shape data based on excessive cummulative tangential deflection and we compare its performance to several well established corner detectors that utilize the alternative strategy of finding approximate curvature extrema

on possible cues in profile analysis identification of the incremented component the ability to identify the frequency of the incremented component in a multitone complex was assessed in two conditions both using the same 11 tone complexes fixed frequency equilog spaced random phases in the standard plus signal complex all components had equal level except one of the inner nine which was incremented in level by an in phase addition of a sinusoid of the same frequency the signal the overall level and signal position were randomly varied on each presentation in the standard absent condition the standard plus signal complex was presented first and it was followed by a single sinusoid either at the signal frequency or at that of an adjacent component below or above in the standard present condition a complex with equal level components the standard complex was also presented on each trial either before or after the standard plus signal complex in random order these two complexes were followed by the single sinusoid in both conditions the subjects had to indica

a cost minimization approach to edge detection using simulated annealing the authors cast edge detection as a problem in cost minimization this is achieved by the formulation of a cost function that evaluates the quality of edge configurations the function is a linear sum of weighted cost factors the cost factors capture desirable characteristics of edges such as accuracy in localization thinness and continuity edges are detected by finding the edge configurations that minimize the cost function the authors give a mathematical description of edges and analyze the cost function in terms of the characteristics of the edges in minimum cost configurations through the analysis guidelines are provided on the choice of weights to achieve certain characteristics of the detected edges the cost function is minimized by the simulated annealing method a set of strategies is presented for generating candidate states and to devise a suitable temperature schedule

random graphs with arbitrary degree distributions and their applications recent work on the structure of social networks and the internet has focused attention on graphs with distributions of vertex degree that are significantly different from the poisson degree distributions that have been widely studied in the past in this paper we develop in detail the theory of random graphs with arbitrary degree distributions in addition to simple undirected unipartite graphs we examine the properties of directed and bipartite graphs among other results we derive exact expressions for the position of the phase transition at which a giant component first forms the mean component size the size of the giant component if there is one the mean number of vertices a certain distance away from a randomly chosen vertex and the average vertex vertex distance within a graph we apply our theory to some real world graphs including the worldwide web and collaboration graphs of scientists and fortune 1000 company directors we demonstrate that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world while in others there is a measurable discrepancy between theory and reality perhaps indicating the presence of additional social structure in the network that is not captured by the random graph

relative weights for frequency glide detection using narrowband noise narrowband noise stimuli were used to derive relative weights for detecting frequency glides in a yes no procedure one stimulus set was restricted to the duration of the glide for the second stimulus set there were fringe noise bands preceding and following the glide for both sets the center frequency of the linear glide was either fixed at 1000hz or randomly chosen on each trial from the range of 800 1200hz relative weights as a function of time were derived using a linear model and the linear classification method a ahumada j vis 2 121 131 2002 sensitivity was better for the fixed than random frequency conditions and weight patterns from the random frequency conditions were less reliable than those obtained from the fixed frequency conditions the magnitudes of the relative weights tended to be larger for the second half of the stimulus suggesting that subjects paid more attention to the later than the earlier parts of stimuli in the random frequency conditions the linear model failed to account for subjects performance unless the stimuli were expressed in terms of relative changes in frequency rather than absolute frequency

supercooperation in evolutionary games on correlated weighted networks in this work we study the behavior of classical two person two strategies evolutionary games on a class of weighted networks derived from barabasi albert and random scale free unweighted graphs using customary imitative dynamics our numerical simulation results show that the presence of link weights that are correlated in a particular manner with the degree of the link end points leads to unprecedented levels of cooperation in the whole games phase space well above those found for the corresponding unweighted complex networks we provide intuitive explanations for this favorable behavior by transforming the weighted networks into unweighted ones with particular topological properties the resulting structures help us to understand why cooperation can thrive and also give ideas as to how such supercooperative networks might be built

correction to rotation and gray scale transform invariant texture classification using spiral resampling subband decomposition and hidden markov model this paper proposes a new texture classification algorithm that is invariant to rotation and gray scale transformation first we convert two dimensional 2 d texture images to one dimensional 1 d signals by spiral resampling then we use a quadrature mirror filter qmf bank to decompose sampled signals into subbands in each band we take high order autocorrelation functions as features features in different bands which form a vector sequence are then modeled as a hidden markov model bmm during classification the unknown texture is matched against all the models and the best match is taken as the classification result simulations showed that the highest correct classification rate for 16 kinds of texture was 95 14

evolution of cooperation on spatially embedded networks in this work we study the behavior of classical two person two strategies evolutionary games on networks embedded in a euclidean two dimensional space with different kinds of degree distributions and topologies going from regular to random and to scale free ones using several imitative microscopic dynamics we study the evolution of global cooperation on the above network classes and find that specific topologies having a hierarchical structure and an inhomogeneous degree distribution such as apollonian and grid based networks are very conducive to cooperation spatial scale free networks are still good for cooperation but to a lesser degree both classes of networks enhance average cooperation in all games with respect to standard random geometric graphs and regular grids by shifting the boundaries between cooperative and defective regions these findings might be useful in the design of interaction structures that maintain cooperation when the agents are constrained to live in physical two dimensional space

a new algorithm for dominant points detection and polygonization of digital curves a new algorithm for detecting dominant points and polygonal approximation of digitized closed curves is presented it uses an optimal criterion for determining the region of support of each boundary point and a new mechanism for selecting the dominant points the algorithm does not require an input parameter and can handle shapes that contain features of multiple sizes efficiently in addition the approximating polygon preserves the symmetry of the shape

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