visual word spatial arrangement for image retrieval and classification we present word spatial arrangement wsa an approach to represent the spatial arrangement of visual words under the bag of visual words model it lies in a simple idea which encodes the relative position of visual words by splitting the image space into quadrants using each detected point as origin wsa generates compact feature vectors and is flexible for being used for image retrieval and classification for working with hard or soft assignment requiring no pre post processing for spatial verification experiments in the retrieval scenario show the superiority of wsa in relation to spatial pyramids experiments in the classification scenario show a reasonable compromise between those methods with spatial pyramids generating larger feature vectors while wsa provides adequate performance with much more compact features as wsa encodes only the spatial information of visual words and not their frequency of occurrence the results indicate the importance of such information for visual categorization highlightsspatial arrangement of visual words wsa for image retrieval and classification wsa generates vectors more compact than the traditional spatial pooling methods wsa outperforms spatial pyramids in the retrieval scenario wsa presents adequate performance in the classification scenario

quantifying the connectivity of a network the network correlation function method networks are useful for describing systems of interacting objects where the nodes represent the objects and the edges represent the interactions between them the applications include chemical and metabolic systems food webs as well as social networks lately it was found that many of these networks display some common topological features such as high clustering small average path length small world networks and a power law degree distribution scale free networks the topological features of a network are commonly related to the network s functionality however the topology alone does not account for the nature of the interactions in the network and their strength here we present a method for evaluating the correlations between pairs of nodes in the network these correlations depend both on the topology and on the functionality of the network a network with high connectivity displays strong correlations between its interacting nodes and thus features small world functionality we quantify the correlations between all pairs of nodes in the network and express them as matrix elements in the correlation matrix from this information one can plot the correlation function for the network and to extract the correlation length the connectivity of a network is then defined as the ratio between this correlation length and the average path length of the network using this method we distinguish between a topological small world and a functional small world where the latter is characterized by long range correlations and high connectivity clearly networks that share the same topology may have different connectivities based on the nature and strength of their interactions the method is demonstrated on metabolic networks but can be readily generalized to other types of networks

3d object modeling and recognition using local affine invariant image descriptors and multi view spatial constraints this article introduces a novel representation for three dimensional 3d objects in terms of local affine invariant descriptors of their images and the spatial relationships between the corresponding surface patches geometric constraints associated with different views of the same patches under affine projection are combined with a normalized representation of their appearance to guide matching and reconstruction allowing the acquisition of true 3d affine and euclidean models from multiple unregistered images as well as their recognition in photographs taken from arbitrary viewpoints the proposed approach does not require a separate segmentation stage and it is applicable to highly cluttered scenes modeling and recognition results are presented

efficient high order matching we present a computational approach to high order matching of data sets in rd those are matchings based on data affinity measures that score the matching of more than two pairs of points at a time high order affinities are represented by tensors and the matching is then given by a rank one approximation of the affinity tensor and a corresponding discretization our approach is rigorously justified by extending zass and shashua s hypergraph matching to high order spectral matching this paves the way for a computationally efficient dual marginalization spectral matching scheme we also show that based on the spectral properties of random matrices affinity tensors can be randomly sparsified while retaining the matching accuracy our contributions are experimentally validated by applying them to synthetic as well as real data sets

simplifying mixture models through function approximation the finite mixture model is widely used in various statistical learning problems however the model obtained may contain a large number of components making it inefficient in practical applications in this paper we propose to simplify the mixture model by minimizing an upper bound of the approximation error between the original and the simplified model under the use of the l 2 distance measure this is achieved by first grouping similar components together and then performing local fitting through function approximation the simplified model obtained can then be used as a replacement of the original model to speed up various algorithms involving mixture models during training e g bayesian filtering belief propagation and testing e g kernel density estimation support vector machine svm testing encouraging results are observed in the experiments on density estimation clustering based image segmentation and simplification of svm decision functions

rotation invariant local frequency descriptors for texture classification this paper presents a novel rotation invariant method for texture classification based on local frequency components the local frequency components are computed by applying 1 d fourier transform on a neighboring function defined on a circle of radius r at each pixel we observed that the low frequency components are the major constituents of the circular functions and can effectively represent textures three sets of features are extracted from the low frequency components two based on the phase and one based on the magnitude the proposed features are invariant to rotation and linear changes of illumination moreover by using low frequency components the proposed features are very robust to noise while the proposed method uses a relatively small number of features it outperforms state of the art methods in three well known datasets brodatz outex and curet in addition the proposed method is very robust to noise and can remarkably improve the classification accuracy especially in the presence of high levels of noise

vertex similarity in networks we consider methods for quantifying the similarity of vertices in networks we propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar this leads to a self consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network we test our similarity measure on computer generated networks for which the expected results are known and on a number of real world networks

characterization of chaotic dynamic behavior in the gas liquid slug flow using directed weighted complex network analysis abstract understanding the nonlinear and complex dynamics underlying the gas liquid slug flow is a significant but challenging problem we systematically carried out gas liquid two phase flow experiments for measuring the time series of flow signals which is studied in terms of the mapping from time series to complex networks in particular we construct directed weighted complex networks dwcn from time series and then associate different aspects of chaotic dynamics with the topological indices of the dwcn and further demonstrate that the dwcn can be exploited to detect unstable periodic orbits of low periods examples using time series from classical chaotic systems are provided to demonstrate the effectiveness of our approach we construct and analyze numbers of dwcns for different gas liquid flow patterns and find that our approach can quantitatively distinguish different experimental gas liquid flow patterns furthermore the dwcn analysis indicates that slug flow shows obvious chaotic behavior and its unstable periodic orbits reflect the intermittent quasi periodic oscillation behavior between liquid slug and large gas slug these interesting and significant findings suggest that the directed weighted complex network can potentially be a powerful tool for uncovering the underlying dynamics leading to the formation of the gas liquid slug flow

illuminance flow estimation by regression we investigate the estimation of illuminance flow using histograms of oriented gradient features hogs in a regression setting we found for both ridge regression and support vector machines that the optimal solution shows close resemblance to the gradient based structure tensor also known as the second moment matrix theoretical results are presented showing in detail how the structure tensor and the hogs are connected this relation will benefit computer vision tasks such as affine invariant texture object matching using hogs several properties of hogs are presented among others how many bins are required for a directionality measure and how to estimate hogs through spatial averaging that requires no binning

study of a robust feature the pointwise lipschitz regularity the aim of this paper is to highlight the relevance in computer vision of the pointwise lipschitz regularity the regularity gives a measure of the local regularity of the intensity function associated to an image known wavelet methods provide an efficient computation of at contour points of the image from a theoretical point of view we study the effect of geometric deformations and other specific transformations applied to the image showing invariance properties from a practical point of view we assess the robustness of the regularity when the image undergoes various transformations the results we obtain show the lipschitz regularity is a suitable feature for applications in computer vision

robust object detection with interleaved categorization and segmentation this paper presents a novel method for detecting and localizing objects of a visual category in cluttered real world scenes our approach considers object categorization and figure ground segmentation as two interleaved processes that closely collaborate towards a common goal as shown in our work the tight coupling between those two processes allows them to benefit from each other and improve the combined performance the core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the generalized hough transform the resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result this segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background moreover the information from where in the image a hypothesis draws its support is employed in an mdl based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion an extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories including both rigid and articulated objects in addition its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems

rasim a novel rotation and scale invariant matching of local image interest points this paper presents a novel algorithm for matching image interest points potential interest points are identified by searching for local peaks in difference of gaussian dog images we refine and assign rotation scale and location for each keypoint by using the sift algorithm pseudo log polar sampling grid is then applied to properly scaled image patches around each keypoint and a weighted adaptive lifting scheme transform is designed for each ring of the log polar grid the designed adaptive transform for a ring in the reference keypoint and the general non adaptive transform are applied to the corresponding ring in a test keypoint similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non adaptive transforms we refer to the proposed versatile system of rotation and scale invariant matching as rasim our experiments show that the accuracy of rasim is more than sift which is the most widely used interest point matching algorithm in the literature rasim is also more robust to image deformations while its computation time is comparable to sift

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

matching images with multiple descriptors an unsupervised approach for locally adaptive descriptor selection with the aim to improve the performance of feature matching we present an unsupervised approach for adaptive description selection in the space of homographies inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain our approach stands on the unsupervised nature of feature matching and can choose a good descriptor locally for matching each feature point instead of using one global descriptor to this end the homography space serves as the domain for selecting various heterogeneous descriptors correspondences obtained by any descriptors are considered as points in the space and their geometric coherence and spatial continuity are measured via computing the geodesic distances in this way mutual verification across different descriptors is allowed and correct correspondences will be highlighted with a high degree of consistency short geodesic distances here it follows that one class svm can be applied to identifying these correct correspondences and achieves adaptive descriptor selection the proposed approach is comprehensively compared with the state of the art approaches and evaluated on five benchmarks of image matching the promising results manifest its effectiveness

global propagation of affine invariant features for robust matching local invariant features have been successfully used in image matching to cope with viewpoint change partial occlusion and clutters however when these factors become too strong there will be a lot of mismatches due to the limited repeatability and discriminative power of features in this paper we present an efficient approach to remove the false matches and propagate the correct ones for the affine invariant features which represent the state of the art local invariance first a pair wise affine consistency measure is proposed to evaluate the consensus of the matches of affine invariant regions the measure takes into account both the keypoint location and the region shape size and orientation based on this measure a geometric filter is then presented which can efficiently remove the outliers from the initial matches and is robust to severe clutters and non rigid deformation to increase the correct matches we propose a global match refinement and propagation method that simultaneously finds a optimal group of local affine transforms to relate the features in two images the global method is capable of producing a quasi dense set of matches even for the weakly textured surfaces that suffer strong rigid transformation or non rigid deformation the strong capability of the proposed method in dealing with significant viewpoint change non rigid deformation and low texture objects is demonstrated in experiments of image matching object recognition and image based rendering

structure of shells in complex networks we define shell l in a network as the set of nodes at distance l with respect to a given node and define rl as the fraction of nodes outside shell l in a transport process information or disease usually diffuses from a random node and reach nodes shell after shell thus understanding the shell structure is crucial for the study of the transport property of networks we study the statistical properties of the shells of a randomly chosen node for a randomly connected network with given degree distribution we derive analytically the degree distribution and average degree of the nodes residing outside shell l as a function of rl further we find that rl follows an iterative functional form rl phi rl 1 where phi is expressed in terms of the generating function of the original degree distribution of the network our results can explain the power law distribution of the number of nodes bl found in shells with l larger than the network diameter d which is the average distance between all pairs of nodes for real world networks the theoretical prediction of rl deviates from the empirical rl we introduce a network correlation function c rl identical with rl phi rl 1 to characterize the correlations in the network where rl is the empirical value and phi rl 1 is the theoretical prediction c rl 1 indicates perfect agreement between empirical results and theory we apply c rl to several model and real world networks we find that the networks fall into two distinct classes i a class of poorly connected networks with c rl 1 where a larger smaller fraction of nodes resides outside inside distance l from a given node than in randomly connected networks with the same degree distributions examples include the watts strogatz model and networks characterizing human collaborations such as citation networks and the actor collaboration network ii a class of well connected networks with c rl 1 examples include the barabasi albert model and the autonomous system internet network

balanced exploration and exploitation model search for efficient epipolar geometry estimation the estimation of the epipolar geometry is especially difficult when the putative correspondences include a low percentage of inlier correspondences and or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect this work presents the balanced exploration and exploitation model beem search algorithm which works very well especially for these difficult scenes the algorithm handles these two problems in a unified manner it includes the following main features 1 balanced use of three search techniques global random exploration local exploration near the current best solution and local exploitation to improve the quality of the model 2 exploitation of available prior information to accelerate the search process 3 use of the best found model to guide the search process escape from degenerate models and define an efficient stopping criterion 4 presentation of a simple and efficient method to estimate the epipolar geometry from two scale invariant feature transform sift correspondences and 5 use of the locality sensitive hashing lsh approximate nearest neighbor algorithm for fast putative correspondence generation the resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the state of the art algorithms

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

a surface approximation method for image and video correspondences although finding correspondences between similar images is an important problem in image processing the existing algorithms cannot find accurate and dense correspondences in images with significant changes in lighting transformation or with the non rigid objects this paper proposes a novel method for finding accurate and dense correspondences between images even in these difficult situations starting with the non rigid dense correspondence algorithm 1 to generate an initial correspondence map we propose a new geometric filter that uses cubic b spline surfaces to approximate the correspondence mapping functions for shared objects in both images thereby eliminating outliers and noise we then propose an iterative algorithm which enlarges the region containing valid correspondences compared with the existing methods our method is more robust to significant changes in lighting color or viewpoint furthermore we demonstrate how to extend our surface approximation method to video editing by first generating a reliable correspondence map between a given source frame and each frame of a video the user can then edit the source frame and the changes are automatically propagated through the entire video using the correspondence map to evaluate our approach we examine applications of unsupervised image recognition and video texture editing and show that our algorithm produces better results than those from state of the art approaches

efficient planar affine canonicalization abstract this paper presents a fast and accurate affine canonicalization method for planar shapes this method improves on previous ones based on iterative optimization that produce multiple canonical versions canonicalization provides a common reference frame for shape comparison without the loss of discrimination ability often caused by invariant features it also gives for free the alignment transformation between any pair of shapes the proposed method is based on the properties of the joint angular distribution of marginal skewness and kurtosis the so called sk signature which can be efficiently computed in closed form from the raw image moments the experiments demonstrate that the method is robust to the non affine distortions caused by natural perspective image conditions thus it can be used as an automatic preprocessing step to add affine invariance in statistical pattern recognition applications

scale rotation invariant pattern entropy for keypoint based near duplicate detection near duplicate nd detection appears as a timely issue recently being regarded as a powerful tool for various emerging applications in the web 2 0 environment particularly the identification of near duplicates enables the tasks such as copyright enforcement news topic tracking image and video search in this paper we describe an algorithm namely scale rotation invariant pattern entropy sr pe for the detection of near duplicates in large scale video corpus sr pe is a novel pattern evaluation technique capable of measuring the spatial regularity of matching patterns formed by local keypoints more importantly the coherency of patterns and the perception of visual similarity under the scenario that there could be multiple nd regions undergone arbitrary transformations respectively are carefully addressed through entropy measure to demonstrate our work in large scale dataset a practical framework composed of three components bag of words representation local keypoint matching and sr pe evaluation is also proposed for the rapid detection of near duplicates

scale invariant feature transform on the sphere theory and applications a sift algorithm in spherical coordinates for omnidirectional images is proposed this algorithm can generate two types of local descriptors local spherical descriptors and local planar descriptors with the first ones point matching between two omnidirectional images can be performed and with the second ones the same matching process can be done but between omnidirectional and planar images furthermore a planar to spherical mapping is introduced and an algorithm for its estimation is given this mapping allows to extract objects from an omnidirectional image given their sift descriptors in a planar image several experiments confirming the promising and accurate performance of the system are conducted

scale free brain functional networks functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites analysis of the resulting networks in different tasks shows that a the distribution of functional connections and the probability of finding a link versus distance are both scale free b the characteristic path length is small and comparable with those of equivalent random networks and c the clustering coefficient is orders of magnitude larger than those of equivalent random networks all these properties typical of scale free small world networks reflect important functional information about brain states

a performance evaluation of local descriptors in this paper we compare the performance of descriptors computed for local interest regions as for example extracted by the harris affine detector mikolajczyk k and schmid c 2004 many different descriptors have been proposed in the literature it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector the descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations we compare shape context belongie s et al april 2002 steerable filters freeman w and adelson e setp 1991 pca sift ke y and sukthankar r 2004 differential invariants koenderink j and van doorn a 1987 spin images lazebnik s et al 2003 sift lowe d g 1999 complex filters schaffalitzky f and zisserman a 2002 moment invariants van gool l et al 1996 and cross correlation for different types of interest regions we also propose an extension of the sift descriptor and show that it outperforms the original method furthermore we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the sift based descriptors perform best moments and steerable filters show the best performance among the low dimensional descriptors

enhanced figure ground classification with background prior propagation we present an adaptive figure ground segmentation algorithm that is capable of extracting foreground objects in a generic environment starting from an interactively assigned background mask an initial background prior is defined and multiple soft label partitions are generated from different foreground priors by progressive patch merging these partitions are fused to produce a foreground probability map the probability map is then binarized via threshold sweeping to create multiple hard label candidates a set of segmentation hypotheses is formed using different evaluation scores from this set the hypothesis with maximal local stability is propagated as the new background prior and the segmentation process is repeated until convergence similarity voting is used to select a winner set and the corresponding hypotheses are fused to yield the final segmentation result experiments indicate that our method performs at or above the current state of the art on several data sets with particular success on challenging scenes that contain irregular or multiple connected foregrounds

origin of degree correlations in the internet and other networks it has been argued that the observed anticorrelation between the degrees of adjacent vertices in the network representation of the internet has its origin in the restriction that no two vertices have more than one edge connecting them here we propose a formalism for modeling ensembles of graphs with single edges only and derive values for the exponents and correlation coefficients characterizing them our results confirm that the conjectured mechanism does indeed give rise to correlations of the kind seen in the internet although only a part of the measured correlation can be accounted for in this way

spectral symmetry analysis we present a spectral approach for detecting and analyzing rotational and reflectional symmetries in n dimensions our main contribution is the derivation of a symmetry detection and analysis scheme for sets of points irn and its extension to image analysis by way of local features each object is represented by a set of points s irn where the symmetry is manifested by the multiple self alignments of s the alignment problem is formulated as a quadratic binary optimization problem with an efficient solution via spectral relaxation for symmetric objects this results in a multiplicity of eigenvalues whose corresponding eigenvectors allow the detection and analysis of both types of symmetry we improve the scheme s robustness by incorporating geometrical constraints into the spectral analysis our approach is experimentally verified by applying it to 2d and 3d synthetic objects as well as real images

a comparison of affine region detectors the paper gives a snapshot of the state of the art in affine covariant region detectors and compares their performance on a set of test images under varying imaging conditions six types of detectors are included detectors based on affine normalization around harris mikolajczyk and schmid 2002 schaffalitzky and zisserman 2002 and hessian points mikolajczyk and schmid 2002 a detector of maximally stable extremal regions proposed by matas et al 2002 an edge based region detector tuytelaars and van gool 1999 and a detector based on intensity extrema tuytelaars and van gool 2000 and a detector of salient regions proposed by kadir zisserman and brady 2004 the performance is measured against changes in viewpoint scale illumination defocus and image compression the objective of this paper is also to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework

robust point matching via vector field consensus in this paper we propose an efficient algorithm called vector field consensus for establishing robust point correspondences between two sets of points our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences or outliers in addition to a limited number of true correspondences inliers next we solve for correspondence by interpolating a vector field between the two point sets which involves estimating a consensus of inlier points whose matching follows a nonparametric geometrical constraint we formulate this a maximum a posteriori map estimation of a bayesian model with hidden latent variables indicating whether matches in the putative set are outliers or inliers we impose nonparametric geometrical constraints on the correspondence as a prior distribution using tikhonov regularizers in a reproducing kernel hilbert space map estimation is performed by the em algorithm which by also estimating the variance of the prior model initialized to a large value is able to obtain good estimates very quickly e g avoiding many of the local minima inherent in this formulation we illustrate this method on data sets in 2d and 3d and demonstrate that it is robust to a very large number of outliers even up to 90 we also show that in the special case where there is an underlying parametric geometrical model e g the epipolar line constraint that we obtain better results than standard alternatives like ransac if a large number of outliers are present this suggests a two stage strategy where we use our nonparametric model to reduce the size of the putative set and then apply a parametric variant of our approach to estimate the geometric parameters our algorithm is computationally efficient and we provide code for others to use it in addition our approach is general and can be applied to other problems such as learning with a badly corrupted training data set

matching widely separated views based on affine invariant regions invariant regions are self adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene such regions can be extracted directly from a single image they are then described by a set of invariant features which makes it relatively easy to match them between views even under wide baseline conditions in this contribution two methods to extract invariant regions are presented the first one starts from corners and uses the nearby edges while the second one is purely intensity based as a matter of fact the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit this yields more correspondences and a system that can deal with a wider range of images to increase the robustness of the system two semi local constraints on combinations of region correspondences are derived one geometric the other photometric they allow to test the consistency of correspondences and hence to reject falsely matched regions experiments on images of real world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach

local log euclidean multivariate gaussian descriptor and its application to image classification this paper presents a novel image descriptor to effectively characterize the local high order image statistics our work is inspired by the diffusion tensor imaging and the structure tensor method or covariance descriptor and motivated by popular distribution based descriptors such as sift and hog our idea is to associate one pixel with a multivariate gaussian distribution estimated in the neighborhood the challenge lies in that the space of gaussians is not a linear space but a riemannian manifold we show for the first time to our knowledge that the space of gaussians can be equipped with a lie group structure by defining a multiplication operation on this manifold and that it is isomorphic to a subgroup of the upper triangular matrix group furthermore we propose methods to embed this matrix group in the linear space which enables us to handle gaussians with euclidean operations rather than complicated riemannian operations the resulting descriptor called local log euclidean multivariate gaussian l 2 emg descriptor works well with low dimensional and high dimensional raw features moreover our descriptor is a continuous function of features without quantization which can model the first and second order statistics extensive experiments were conducted to evaluate thoroughly l 2 emg and the results showed that l 2 emg is very competitive with state of the art descriptors in image classification

on the waveguide modelling of dynamic stiffness of cylindrical vibration isolators part i the model solution and experimental comparison a waveguide model of the axial dynamic stiffness for cylindrical vibration isolators in the audible frequency range is presented the problems of satisfying the cylinder boundary conditions simulta

compressed matching for feature vectors the problem of compressing a large collection of feature vectors is investigated so that object identification can be processed on the compressed form of the features the idea is to perform matching of a query image against an image database using directly the compressed form of the descriptor vectors without decompression specifically we concentrate on the scale invariant feature transform sift a known object detection method as well as on dense sift and phow features that contain for each image about 300 times as many vectors as the original sift given two feature vectors we suggest achieving our goal by compressing them using a lossless encoding by means of a fibonacci code for which the pairwise matching can be done directly on the compressed files in our experiments this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase

robust l_ 2 e estimation of transformation for non rigid registration we introduce a new transformation estimation algorithm using the l 2 e estimator and apply it to non rigid registration for building robust sparse and dense correspondences in the sparse point case our method iteratively recovers the point correspondence and estimates the transformation between two point sets feature descriptors such as shape context are used to establish rough correspondence we then estimate the transformation using our robust algorithm this enables us to deal with the noise and outliers which arise in the correspondence step the transformation is specified in a functional space more specifically a reproducing kernel hilbert space in the dense point case for nonrigid image registration our approach consists of matching both sparsely and densely sampled sift features and it has particular advantages in handling significant scale changes and rotations the experimental results show that our approach greatly outperforms state of the art methods particularly when the data contains severe outliers

a rotationally invariant descriptor based on mixed intensity feature histograms abstract this paper proposes a novel method to build a rotation invariant local descriptor by mixed intensity feature histogram most existing local descriptors based on intensity ordinal information typically encode only one local feature for each sampling point in the image patch to address this problem we proposed a method to encode more than one different local features for each pixel in the image patch and construct a 2d mixed intensity feature histogram from which our proposed mifh descriptor is then constructed since the rotation invariant coordinate system is adopted the mifh descriptor does not need to estimate the reference orientation in order to evaluate the performance and the robustness of the mifh with other well known local descriptors e g sift gloh daisy hri csltp liop mrogh image matching experiments were carried out on standard oxford dataset additional image pairs with complex illumination changes and image sequences with different noises to further investigate the discriminative ability of the mifh descriptor a simple object recognition experiment was conducted on three public datasets the experimental results demonstrate that our descriptor mifh exhibits better performance and robustness than other evaluated local descriptors

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

identifying influential nodes in complex networks abstract identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance the degree centrality method is very simple but of little relevance global metrics such as betweenness centrality and closeness centrality can better identify influential nodes but are incapable to be applied in large scale networks due to the computational complexity in order to design an effective ranking method we proposed a semi local centrality measure as a tradeoff between the low relevant degree centrality and other time consuming measures we use the susceptible infected recovered sir model to evaluate the performance by using the spreading rate and the number of infected nodes simulations on four real networks show that our method can well identify influential nodes

one dimensional mapping for estimating projective transformations in this paper the 1 d mapping is studied for estimating a general projective transformation between images first a ray model of image patches is proposed to convert a complex projective deformation of a patch in the cartesian space into two simple rigid shifts of radial lines in the log polar inverse polar space to capture these shifts via direct search two types of radial image line matching viz 1 d log polar mapping and 1 d inverse polar mapping are formally defined also the unique features of these shifts are analyzed then a novel two step mathematical framework is devised for transformation estimation in the first step the 1 d log polar mapping is applied to estimating four affine parameters via two alternative methods for simulated real images in the second step the 1 d inverse polar mapping is applied to estimating two projective parameters by a direct search combined with the two step line matching two translational parameters are also determined finally the performance of the framework is evaluated first in estimating a variety of projective transformations by comparing the estimates with their ground truths and between the two proposed methods and second in registering a wide range of challenging image pairs by comparing with a state of the art feature based method

extremal regions detection guided by maxima of gradient magnitude a problem of computer vision applications is to detect regions of interest under different imaging conditions the state of the art maximally stable extremal regions msers detects affine covariant regions by applying all possible thresholds on the input image and through three main steps including 1 making a component tree of extremal regions evolution 2 obtaining region stability criterion and 3 cleaning up the mser performs very well but it does not consider any information about the boundaries of the regions which are important for detecting repeatable extremal regions we have shown in this paper that employing prior information about boundaries of regions results in a novel region detector algorithm that not only outperforms mser but avoids the mser s rather complicated steps of enumeration and the cleaning up to employ the information about the region boundaries we introduce maxima of gradient magnitudes mgms which are shown to be points that are mostly around the boundaries of the regions having found the mgms the method obtains a global criterion for each level of the input image which is used to find extremum levels els the found els are then used to detect extremal regions the proposed algorithm which is called extremal regions of extremum levels erel has been tested on the public benchmark data set of mikolajczyk the obtained experimental results show that the inclusion of region boundaries through mgms results in a detector that detects regions with high repeatability scores and is more robust against noise compared with mser

rotation and illumination invariant interleaved intensity order based local descriptor the region descriptors using local intensity ordering patterns have become more popular recent years for image matching due to its enhanced discriminative ability however the dimension of these descriptors increases rapidly with the slight increase in the number of local neighbors under consideration and becomes unreasonable for image matching due to time constraint in this paper we reduce the dimension of the descriptor and matching time significantly while keeping up the comparable performance by considering the number of neighboring sample points in an interleaved manner the proposed interleaved order based local descriptor iold considers the local neighbors of a pixel as a set of interleaved neighbors and constructs the descriptor over each set separately and finally combines them to produce a single pattern we extract the local ordering pattern to cope up with the illumination effect in an inherent rotation invariant manner the novelty lies with using multiple neighboring sets in an interleaved fashion we also explored the local intensity order pattern in a multisupport region scenario results are compared over three challenging and widely adopted image matching data sets with other prominent descriptors under various image transformations results based on experiments suggest that the proposed iold descriptor outperforms in terms of both improved matching performance and reduced matching time we also found that the amount of improvement is significant under complex illumination difference while showing more robustness toward noise

affine covariant features for fisheye distortion local modeling perspective cameras are the most popular imaging sensors used in computer vision however many application fields including automotive surveillance and robotics require the use of wide angle cameras e g fisheye which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images affine covariant feature detectors have proved successful in a variety of computer vision applications including object recognition image registration and visual search moreover their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature in this paper we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses as theoretical outcome we show that the inherently non linear radial distortion can be locally approximated by linear functions with a reasonably small error the experimental analysis builds on mikolajczyk s benchmark to assess the robustness of three popular affine region detectors i e maximally stable extremal regions and harris and hessian affine region detectors with respect to different variabilities as well as to radial distortion to support the evaluations we rely on the oxford data set and introduce a novel benchmark data set comprising 50 images depicting different scene categories experiments are carried out on rectilinear images to which radial distortion is artificially added and on real world images acquired using fisheye lenses our analysis points out that affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modeled as an additional affine variability

boundary preserving dense local regions we propose a dense local region detector to extract features suitable for image matching and object recognition tasks whereas traditional local interest operators rely on repeatable structures that often cross object boundaries e g corners scale space blobs our sampling strategy is driven by segmentation and thus preserves object boundaries and shape at the same time whereas existing region based representations are sensitive to segmentation parameters and object deformations our novel approach to robustly sample dense sites and determine their connectivity offers better repeatability in extensive experiments we find that the proposed region detector provides significantly better repeatability and localization accuracy for object matching compared to an array of existing feature detectors in addition we show our regions lead to excellent results on two benchmark tasks that require good feature matching weakly supervised foreground discovery and nearest neighbor based object recognition

transverse tilting and cross coupling stiffness of cylindrical rubber isolators in the audible frequency range the wave guide solution audio frequency wave guide models for antisymmetric dynamic stiffness of arbitrary long elastomer cylinders are presented the locally non mixed boundary conditions at the lateral and radial surfaces are simultaneously satisfied by using the modes corresponding to the dispersion relation for axial waves in cylinders satisfying the stress free boundary conditions at the curved radial boundaries while the displacement conditions on the flat cylinder ends are satisfied by mode matching the elastomer is modelled as nearly incompressible with deviatoric visco elasticity based on a fractional derivative standard linear solid embodying a mittag leffler relaxation kernel the main advantage being the minimum parameter number required to successfully model the material properties over a broad frequency band the stiffness is found to depend strongly on frequency displaying resonances and anti resonances the method is compared with and verified against finite element models in addition comparison to thin beam theories i e euler and timoschenko theory and a simple shear model is presented illustrating the limitations of these models

sampling minimal subsets with large spans for robust estimation when sampling minimal subsets for robust parameter estimation it is commonly known that obtaining an all inlier minimal subset is not sufficient the points therein should also have a large spatial extent this paper investigates a theoretical basis behind this principle based on a little known result which expresses the least squares regression as a weighted linear combination of all possible minimal subset estimates it turns out that the weight of a minimal subset estimate is directly related to the span of the associated points we then derive an analogous result for total least squares which unlike ordinary least squares corrects for errors in both dependent and independent variables we establish the relevance of our result to computer vision by relating total least squares to geometric estimation techniques as practical contributions we elaborate why naive distance based sampling fails as a strategy to maximise the span of all inlier minimal subsets produced in addition we propose a novel method which unlike previous methods can consciously target all inlier minimal subsets with large spans

gaussian field consensus a robust nonparametric matching method for outlier rejection abstract in this paper we propose a robust method called gaussian field consensus gfc for outlier rejection from given putative point set matching correspondences finding correct correspondences inliers is a key component in many computer vision and pattern recognition tasks and the goal of outlier mismatch rejection is to fit the transformation function that maps one feature point set to another our gfc starts by inputting a putative correspondence set which is contaminated by many outliers and the main task of our gfc is to identify the underlying true correspondences from outliers then we formulate this challenging problem by gaussian field nonparametric matching model which bases on the exponential distance loss and kernel method in a reproducing kernel hilbert space next we introduce a local linear constraint based on the regularization theory to preserve the topological structure of the feature points moreover the sparse approximation is used to reduce the search space in this way we can handle a large number of points easily finally we test the gfc method on several real image datasets in the presence of outliers where the experimental results show that our proposed method outperforms current state of the art methods in most test scenarios

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

corner detection based on gradient correlation matrices of planar curves an efficient and novel technique is developed for detecting and localizing corners of planar curves this paper discusses the gradient feature distribution of planar curves and constructs gradient correlation matrices gcms over the region of support ros of these planar curves it is shown that the eigen structure and determinant of the gcms encode the geometric features of these curves such as curvature features and the dominant points the determinant of the gcms is shown to have a strong corner response and is used as a cornerness measure of planar curves a comprehensive performance evaluation of the proposed detector is performed using the acu and localization error criteria experimental results demonstrate that the gcm detector has a strong corner position response along with a high detection rate and good localization performance

shape based invariant texture indexing this paper introduces a new texture analysis scheme which is invariant to local geometric and radiometric changes the proposed methodology relies on the topographic map of images obtained from the connected components of level sets this morphological tool providing a multi scale and contrast invariant representation of images is shown to be well suited to texture analysis we first make use of invariant moments to extract geometrical information from the topographic map this yields features that are invariant to local similarities or local affine transformations these features are invariant to any local contrast change we then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases the obtained experimental results outperform the current state of the art in locally invariant texture analysis

local grayvalue invariants for image retrieval this paper addresses the problem of retrieving images from large image databases the method is based on local grayvalue invariants which are computed at automatically detected interest points a voting algorithm and semilocal constraints make retrieval possible indexing allows for efficient retrieval from a database of more than 1 000 images experimental results show correct retrieval in the case of partial visibility similarity transformations extraneous features and small perspective deformations

mapping from structure to dynamics a unified view of dynamical processes on networks although it is unambiguously agreed that structure plays a fundamental role in shaping the collective dynamics of complex systems how structure determines dynamics exactly still remains unclear we investigate a general computational transformation by which we can map the network topology directly to the dynamical patterns emergent on it independent of the nature of the dynamical processes remarkably we find that many seemingly different dynamical processes on networks such as coupled oscillators ensemble neuron firing epidemic spreading and diffusion can all be understood and unified through this same procedure utilizing the inherent multiscale nature of this structure dynamics transformation we further define a multiscale complexity measure which can quantify the functional diversity a general network can support at different organization levels using only its structure we find that a wide variety of topological features observed in real networks such as modularity hierarchy degree heterogeneity and mixing all result in higher complexity this result suggests that the demand for functional diversity is driving the structural evolution of physical networks

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

revising the simple measures of assortativity in complex networks we find that traditional statistics for measuring degree mixing are strongly affected by superrich nodes to counteract and measure the effect of superrich nodes we propose a paradigm to quantify the mixing pattern of a real network in which different mixing patterns may appear among low degree nodes and among high degree nodes this paradigm and the simple revised measure uncover the true complex degree mixing patterns of complex networks with superrich nodes the alternate method indicates that some networks show a false disassortative mixing induced by superrich nodes and have no tendency to be genuinely disassortative our results also show that the previously observed fragility of scale free networks is actually greatly exacerbated by the presence of even a very small number of superrich nodes

defocus blur invariant scale space feature extractions we propose modifications to scale space feature extraction techniques scale invariant feature transform sift and speeded up robust features surfs that make the feature detection and description invariant to defocus blur specifically the scale space blob detection relies on the second derivative responses of images our analysis of circular defocus blur which sufficiently approximates a real camera blur kernel and its effect on scale space blob detection suggests that fourth derivative and not the usual second derivative is optimal for detecting the blurred blobs while multi scale descriptors of blurred blobs are effective at establishing correspondences between the blurred images the proposed defocus blur invariant dbi scale space feature extraction techniques which we refer to as dbi sift and dbi surf do not require image deblurring nor blur kernel estimation meaning that their accuracy does not depend on the quality of image deblurring we offer empirical evidence of blur invariance by establishing interest point correspondences between sharp or blurred reference images and blurred target images

scale space analysis and active contours for omnidirectional images a new generation of optical devices that generate images covering a larger part of the field of view than conventional cameras namely catadioptric cameras is slowly emerging these omnidirectional images will most probably deeply impact computer vision in the forthcoming years provided that the necessary algorithmic background stands strong in this paper we propose a general framework that helps define various computer vision primitives we show that geometry which plays a central role in the formation of omnidirectional images must be carefully taken into account while performing such simple tasks as smoothing or edge detection partial differential equations pdes offer a very versatile tool that is well suited to cope with geometrical constraints we derive new energy functionals and pdes for segmenting images obtained from catadioptric cameras and show that they can be implemented robustly using classical finite difference schemes various experimental results illustrate the potential of these new methods on both synthetic and natural images

on the waveguide modelling of dynamic stiffness of cylindrical vibration isolators part ii the dispersion relation solution convergence analysis and comparison with simple models abstract based on a waveguide model presented in a companion paper l kari 2001 journal of sound and vibration 244 211 233 1 the influences of higher order modes and structure borne sound dispersion on the axial dynamic stiffness for cylindrical vibration isolators are investigated on the whole a moderate mode number results in an accurate stiffness prediction while an accurate stress point value prediction requires more modes the dispersion relation is solved by a modified newton raphson method with initial values given by an asymptotic expansion or a winding integral method the integral technique is based on the argument principle but as the square root operators in the dispersion relation yield branch points some modifications are needed to create single valued functions conforming to the argument principle the winding integral search domain is split into branch cut absent subdomains containing adaptively defined square root operators the subregion method used for the fulfilment of the boundary conditions at the lateral surfaces is shown to converge faster than for the point matching method however the latter reveals a similar convergence rate as the former at overdetermination comparisons with simple stiffness models are made these models known as the long rod the love the bishop the kynch the mindlin and herrmann and the mindlin and mcniven theories are shown to diverge substantially from the presented exact theory to a great extent the pertinent stress and displacement fields derived from the presented waveguide model explain the discrepancies reported for the approximate theories

simultaneous object recognition and segmentation from single or multiple model views we present a novel object recognition approach based on affine invariant regions it actively counters the problems related to the limited repeatability of the region detectors and the difficulty of matching in the presence of large amounts of background clutter and particularly challenging viewing conditions after producing an initial set of matches the method gradually explores the surrounding image areas recursively constructing more and more matching regions increasingly farther from the initial ones this process covers the object with matches and simultaneously separates the correct matches from the wrong ones hence recognition and segmentation are achieved at the same time the approach includes a mechanism for capturing the relationships between multiple model views and exploiting these for integrating the contributions of the views at recognition time this is based on an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces integration is achieved by measuring the consistency of configurations of groups arising from different model views experimental results demonstrate the stronger power of the approach in dealing with extensive clutter dominant occlusion and large scale and viewpoint changes non rigid deformations are explicitly taken into account and the approximative contours of the object are produced all presented techniques can extend any view point invariant feature extractor

sphorb a fast and robust binary feature on the sphere in this paper we propose sphorb a new fast and robust binary feature detector and descriptor for spherical panoramic images in contrast to state of the art spherical features our approach stems from the geodesic grid a nearly equal area hexagonal grid parametrization of the sphere used in climate modeling it enables us to directly build fine grained pyramids and construct robust features on the hexagonal spherical grid thus avoiding the costly computation of spherical harmonics and their associated bandwidth limitation we further study how to achieve scale and rotation invariance for the proposed sphorb feature extensive experiments show that sphorb consistently outperforms other existing spherical features in accuracy efficiency and robustness to camera movements the superior performance of sphorb has also been validated by real world matching tests

enclosure transform for interest point detection from speckle imagery we present a fast enclosure transform et to localize complex objects of interest from speckle imagery this approach explores the spatial confinement on regional features from a sparse image feature representation unrelated broken ridge features surrounding an object are organized collaboratively giving rise to the enclosureness of the object three enclosure likelihood measures are constructed consisting of the enclosure force potential energy and encloser count in the transform domain the local maxima manifest the locations of objects of interest for which only the intrinsic dimension is known a priori the discrete et algorithm is computationally efficient being on the order of o mn using n measuring distances across an image of m ridge pixels it involves easy and few parameter settings we demonstrate and assess the performance of et on the automatic detection of the prostate locations from supra pubic ultrasound images et yields superior results in terms of positive detection rate accuracy and coverage