shape description and matching using integral invariants on eccentricity transformed images matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision to keep track of changes inside the breast for example it is important for a computer aided detection system to establish correspondences between regions of interest shape transformations computed both with integral invariants ii and with geodesic distance yield signatures that are invariant to isometric deformations such as bending and articulations integral invariants describe the boundaries of planar shapes however they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure conversely eccentricity transforms ecc can match shapes by signatures of geodesic distance histograms based on information from inside the shape but they ignore the boundary information we describe a method that combines the boundary signature of a shape obtained from ii and structural information from the ecc to yield results that improve on them separately

bootstrapping boosted random ferns for discriminative and efficient object classification in this paper we show that the performance of binary classifiers based on boosted random ferns can be significantly improved by appropriately bootstrapping the training step this results in a classifier which is both highly discriminative and computationally efficient and is particularly suitable when only small sets of training images are available during the learning process a small set of labeled images is used to train the boosting binary classifier the classifier is then evaluated over the training set and warped versions of the classified and misclassified patches are progressively added into the positive and negative sample sets for a new re training step in this paper we thoroughly study the conditions under which this bootstrapping scheme improves the detection rates in particular we assess the quality of detection both as a function of the number of bootstrapping iterations and the size of the training set we compare our algorithm against state of the art approaches for several databases including faces cars motorbikes and horses and show remarkable improvements in detection rates with just a few bootstrapping steps

competition interfaces and second class particles the one dimensional nearest neighbor totally asymmetric simple exclusion process can be constructed in the same space as a last passage percolation model in z 2 we show that the trajectory of a second class particle in the exclusion process can be linearly mapped into the competition interface between two growing clusters in the last passage percolation model using technology built up for geodesics in percolation we show that the competition interface converges almost surely to an asymptotic random direction as a consequence we get a new proof for the strong law of large numbers for the second class particle in the rarefaction fan and describe the distribution of the asymptotic angle of the competition interface

skeleton search category specific object recognition and segmentation using a skeletal shape model we describe a top down object detection and segmentation approach that uses a skeleton based shape model and that works directly on real images the approach is based on three components first we propose a fragment based generative model for shape that is based on the shock graph and has minimal dependency among its shape fragments the model is capable of generating a wide variation of shapes as instances of a given object category second we develop a progressive selection mechanism to search among the generated shapes for the category instances that are present in the image the search begins with a large pool of candidates identified by a dynamic programming dp algorithm and progressively reduces it in size by applying series of criteria namely local minimum criterion extent of shape overlap and thresholding of the objective function to select the final object candidates third we propose the partitioned chamfer matching pcm measure to capture the support of image edges for a hypothesized shape this measure overcomes the shortcomings of the oriented chamfer matching and is robust against spurious edges missing edges and accidental alignment between the image edges and the shape boundary contour we have evaluated our approach on the ethz dataset and found it to perform well in both object detection and object segmentation tasks

a statistical basis for lognormal shadowing effects in multipath fading channels empirical justifications for the lognormal rayleigh and suzuki 1977 probability density functions in multipath fading channels are examined by quantifying the rates of convergence of the central limit theorem clt for the addition and multiplication of random variables the accuracy of modeling the distribution of rays which experience multiple reflections diffractions between transmitter and receiver as lognormal is quantified in addition it is shown that the vector sum of lognormal rays such as in a narrow band signal envelope may best be approximated as being either rayleigh lognormal or suzuki distributed depending on the fading channel conditions these conditions are defined statistically

a shape reconstructability measure of object part importance with applications to object detection and localization we propose a computational model which computes the importance of 2 d object shape parts and we apply it to detect and localize objects with and without occlusions the importance of a shape part a localized contour fragment is considered from the perspective of its contribution to the perception and recognition of the global shape of the object accordingly the part importance measure is defined based on the ability to estimate recall the global shapes of objects from the local part namely the part s shape reconstructability more precisely the shape reconstructability of a part is determined by two factors part variation and part uniqueness i part variation measures the precision of the global shape reconstruction i e the consistency of the reconstructed global shape with the true object shape and ii part uniqueness quantifies the ambiguity of matching the part to the object i e taking into account that the part could be matched to the object at several different locations taking both these factors into consideration an information theoretic formulation is proposed to measure part importance by the conditional entropy of the reconstruction of the object shape from the part experimental results demonstrate the benefit with the proposed part importance in object detection including the improvement of detection rate localization accuracy and detection efficiency by comparing with other state of the art object detectors in a challenging but common scenario object detection with occlusions we show a considerable improvement using the proposed importance measure with the detection rate increased over 10 10 on a subset of the challenging pascal dataset the interpolated average precision as used in the pascal voc challenge is improved by 4 8 moreover we perform a psychological experiment which provides evidence suggesting that humans use a similar measure for part importance when perceiving and recognizing shapes

invariant properties of straight homogeneous generalized cylinders and their contours a fundamental group in computer vision is the recovery of three dimensional shape from image data while this problem is in general underconstrained the authors show that it can be simplified in the case where the objects being viewed are generalized cylinders they consider the class of straight homogeneous generalized cylinders shgcs without any further assumption on the viewing direction or the precise shape of these objects they present a rigorous mathematical study of the geometry of shgcs and characterize their gaussian curvature and occluding contours and use these results to prove several new invariant properties of the contours of shgcs these properties are in turn used in two implemented algorithms for recovering shgc descriptions from image contours several examples of segmentation of real images are given other applications are also discussed

recovering surface shape and orientation from texture texture provides an important source of information about the three dimensional structure of visible surfaces particularly for stationary monocular views to recover 3d structure the distorting effects of projection must be distinguished from properties of the texture on which the distortion acts this requires that assumptions must be made about the texture yet the unpredictability of natural textures precludes the use of highly restrictive assumptions the recovery method reported in this paper exploits the minimal assumption that textures do not mimic projective effects this assumption determines the strategy of attributing as much as possible of the variation observed in the image to projection equivalently the interpretation is chosen for which the texture prior to projection is made as uniform as possible this strategy was implemented using statistical methods first for the restricted case of planar surfaces and then by extension for curved surfaces the technique was applied successfully to natural images

jeu de taquin dynamics on infinite young tableaux and second class particles we study an infinite version of the jeu de taquin sliding game which can be thought of as a natural measure preserving transformation on the set of infinite young tableaux equipped with the plancherel probability measure we use methods from representation theory to show that the robinson schensted knuth rsk algorithm gives an isomorphism between this measure preserving dynamical system and the one sided shift dynamics on a sequence of independent and identically distributed random variables distributed uniformly on the unit interval we also show that the jeu de taquin paths induced by the transformation are asymptotically straight lines emanating from the origin in a random direction whose distribution is computed explicitly and show that this result can be interpreted as a statement on the limiting speed of a second class particle in the plancherel tasep particle system a variant of the totally asymmetric simple exclusion process associated with plancherel growth in analogy with earlier results for second class particles in the ordinary tasep

inference and learning with hierarchical shape models in this work we introduce a hierarchical representation for object detection we represent an object in terms of parts composed of contours corresponding to object boundaries and symmetry axes these are in turn related to edge and ridge features that are extracted from the image we propose a coarse to fine algorithm for efficient detection which exploits the hierarchical nature of the model this provides a tractable framework to combine bottom up and top down computation we learn our models from training images where only the bounding box of the object is provided we automate the decomposition of an object category into parts and contours and discriminatively learn the cost function that drives the matching of the object to the image using multiple instance learning using shape based information we obtain state of the art localization results on the uiuc and ethz datasets

shape based object detection via boundary structure segmentation we address the problem of object detection and segmentation using global holistic properties of object shape global shape representations are highly susceptible to clutter inevitably present in realistic images and thus can be applied robustly only using a precise segmentation of the object to this end we propose a figure ground segmentation method for extraction of image regions that resemble the global properties of a model boundary structure and are perceptually salient our shape representation called the chordiogram is based on geometric relationships of object boundary edges while the perceptual saliency cues we use favor coherent regions distinct from the background we formulate the segmentation problem as an integer quadratic program and use a semidefinite programming relaxation to solve it the obtained solutions provide a segmentation of the object as well as a detection score used for object recognition our single step approach achieves state of the art performance on several object detection and segmentation benchmarks

local part chamfer matching for shape based object detection chamfer matching is one of the elegant and powerful tools for shape based detection in cluttered images however the chamfer matching methods including oriented chamfer matching ocm and directional chamfer matching dcm tend to produce bad detections due to deformation of object shapes and cluttering in the scene to improve detection accuracy of these chamfer matching methods we propose local part oriented chamfer matching lpocm and local part directional chamfer matching lpdcm first shape templates and discriminative contour fragments are learned and then a shape representation is built using a markov random field mrf finally the template detection in an input image is formulated as an inference in the mrf experimental results for benchmark datasets including ethz shape classes inria horses and weizmann horses clearly demonstrate that the proposed lpocm and lpdcm significantly improve the detection accuracy of ocm and dcm without sacrificing much time efficiency highlightswe proposed two novel deformable chamfer matching methods based on orientation chamfer matching ocm and directional chamfer matching dcm respectively docm and ddcm can be implemeted easily and have great values on practice

point set morphological filtering and semantic spatial configuration modeling application to microscopic image and bio structure analysis high level spatial relation and configuration modeling issues are gaining momentum in the image analysis and pattern recognition fields in particular it is deemed important whenever one needs to mine high content images or large scale image databases in a more expressive way than a purely statistically one continuing previous efforts to incorporate structural analysis by developing specific efficient morphological tools performing on mesh representations like delaunay triangulations we propose to formalize spatial relation modeling techniques dedicated to unorganized point sets we provide an original mesh lattice framework which is more convenient for structural representations of large image data by means of interest point sets and their morphological analysis the set of designed numerical operators is based on a specific dilation operator that makes it possible to handle concepts like between or left of over sparse representations of image data such as graphs based on this new theoretical framework for reasoning about images we are able to process high level queries over large histopathological images knowing that digitized histopathology is a new challenge in the field of bio imaging due to the high content nature and large size of these images

stationary distributions of multi type totally asymmetric exclusion processes we consider totally asymmetric simple exclusion processes with n types of particle and holes n taseps on z and on the cycle z n angel recently gave an elegant construction of the stationary measures for the 2 tasep based on a pair of independent product measures we show that angel s construction can be interpreted in terms of the operation of a discrete time m m 1 queueing server the two product measures correspond to the arrival and service processes of the queue we extend this construction to represent the stationary measures of an n tasep in terms of a system of queues in tandem the proof of stationarity involves a system of n 1 taseps whose evolutions are coupled but whose distributions at any fixed time are independent using the queueing representation we give quantitative results for stationary probabilities of states of the n tasep on z n and simple proofs of various independence and regeneration properties for systems on z

line drawing interpretation bilateral symmetry results previously derived by the author are used to investigate the implication of bilateral symmetry in line drawings it is shown that the line drawing of an orthographically protected surface of revolution exhibits bilateral symmetry about the projection of its axis of revolution irrespective of the viewing direction barring one exception a bilaterally symmetric line drawing is necessarily the orthographic projection of a local surface of revolution whenever its symmetry axis continues to be the projection of a fixed line in space under perturbation of viewpoint the axis of revolution is the invariant preimage of the symmetry axis various line drawing causes are detailed which facilitate the deduction of invariant preimages of symmetry axes and consequently of local surfaces of revolution

part based 3d descriptions of complex objects from a single image volumetric 3d part based descriptions of complex objects in a scene can be highly beneficial for many tasks such as generic object recognition navigation and manipulation however it has been difficult to derive such descriptions from image data there has been some progress in getting such descriptions from range data or from perfect contours but analysis of a real intensity image presents many difficulties the object and part boundaries do not completely correspond to image boundaries the detected boundaries are often fragmented and many boundaries due to surface markings shadows and noise are present in addition inference of 3d from a 2d image is difficult the paper describes a method to compute the desired descriptions from a single image by exploiting projective properties of a class of generalized cylinders and of possible joints between them experimental results on some examples are given

object recognition by discriminative combinations of line segments ellipses and appearance features we present a novel contour based approach that recognizes object classes in real world scenes using simple and generic shape primitives of line segments and ellipses compared to commonly used contour fragment features these primitives support more efficient representation since their storage requirements are independent of object size additionally these primitives are readily described by their geometrical properties and hence afford very efficient feature comparison we pair these primitives as shape tokens and learn discriminative combinations of shape tokens here we allow each combination to have a variable number of shape tokens this coupled with the generic nature of primitives enables a variety of class specific shape structures to be learned building on the contour based method we propose a new hybrid recognition method that combines shape and appearance features each discriminative combination can vary in the number and the types of features where these two degrees of variability empower the hybrid method with even more flexibility and discriminative potential we evaluate our methods across a large number of challenging classes and obtain very competitive results against other methods these results show the proposed shape primitives are indeed sufficiently powerful to recognize object classes in complex real world scenes

feature matching constrained by cross ratio invariance abstract the main aim of this work is to propose a new technique to solve the well known feature correspondence problem for motion estimation the problem is formulated as an optimization process whose energy function includes constraints based on projective invariance of cross ratio of five coplanar points starting from some approximated correspondences estimated by radiometric similarity for features with high directional variance optimal matches are obtained through an optimization technique the new contribution of this work consists of a matching process refining the raw measurements based on an energy function minimization technique converging to an optimal solution for most of the features by taking advantage of some good initial guess and in the use of cross ratio as geometrical invariant constraint to detect and correct the mismatches due to wrong radiometric similarity measures though the method is based on geometrical invariance of coplanar points it is not required that all features have to be coplanar or to preprocess the images to detect the planar regions experimental results are presented for real and synthetic images and the performance of the novel approach is evaluated on different image sequences and compared to well known techniques

learning compositional shape models of multiple distance metrics by information projection this paper presents a novel compositional contour based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations our approach contains two key steps 1 contour feature generation and 2 generative model pursuit for each category we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics these metrics are diverse and complementary with each other to capture various shape deformations we regard the parameterized contour segment plus an additive residual epsilon as a basic subspace namely epsilon ball in the sense that it represents local shape variance under the certain distance metric using these epsilon balls as features we then propose a generative learning algorithm to pursue the compositional shape model which greedily selects the most representative features under the information projection principle in experiments we evaluate our model on several public challenging data sets and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations articulations and background clutter hence boosting system performance

fast and robust 2d shape extraction using discrete point sampling and centerline grouping in complex images this paper initially develops the discrete point sampling operator s concept model and parameters that we have previously proposed and makes its belt shaped regions in a discrete point sampling map more salient and appropriate for centerline extraction the cross sectional features of these belt shaped regions are then analyzed and seven types of feature points are defined to facilitate descriptions of such features based on these feature points a three level detection system is proposed including feature points line segments and centerlines to extract centerlines from the belt shaped regions eight basic types of centerlines and five types of relationships among the centerlines are defined by computational geometry algorithms and gestalt laws are used to cluster them into groupings if some prior information about a desired shape is available retrieval grouping may be carried out by a discrete point sampling map the purpose of which is to find centerlines by best matching with prior information discrete point sampling effectually overcomes the influences of interference from noise textures and uneven illumination and greatly reduces the difficulty of centerline extraction centerline clustered groupings and retrieval grouping can offer a strong anti interference ability with nonlinear deformations such as articulation and occlusion this method can extract large scale complex shapes combined of lines and planes from complex images the wheel location results of noise test and other shape extraction experiments show that our method has a strong capability to persist with nonlinear deformations

backscatter communications over correlated nakagami m fading channels in this paper we consider a radio frequency identification rfid backscatter system with two scenarios single input multiple output simo and multiple input single output miso we investigate the impact of channel correlation between the forward and backscatter links on the symbol error rate ser and we compare the performance of simo and miso rfid systems with maximum ratio combining reception over arbitrarily correlated nakagami m fading channels in order to gain an insight for the system design closed form expressions for the asymptotic ser and an upper bound are derived for m ary phase shift keying and quadrature amplitude modulation the diversity order in the fully correlated cases revealed from these expressions is half of that in the partially correlated and uncorrelated cases finally simulations are performed to validate the theoretical analysis

groups of adjacent contour segments for object detection we present a family of scale invariant local shape features formed by chains of k connected roughly straight contour segments kas and their use for object class detection kas are able to cleanly encode pure fragments of an object boundary without including nearby clutter moreover they offer an attractive compromise between information content and repeatability and encompass a wide variety of local shape structures we also define a translation and scale invariant descriptor encoding the geometric configuration of the segments within a kas making kas easy to reuse in other frameworks for example as a replacement or addition to interest points ips software for detecting and describing kas is released at http lear inrialpes fr software we demonstrate the high performance of kas within a simple but powerful sliding window object detection scheme through extensive evaluations involving eight diverse object classes and more than 1 400 images we 1 study the evolution of performance as the degree of feature complexity k varies and determine the best degree 2 show that kas substantially outperform ips for detecting shape based classes and 3 compare our object detector to the recent state of the art system by dalal and triggs 2005

gaussian class multivariate weibull distributions theory and applications in fading channels ascertaining on the suitability of the weibull distribution to model fading channels a theoretical framework for a class of multivariate weibull distributions originated from gaussian random processes is introduced and analyzed novel analytical expressions for the joint probability density function pdf moment generating function mgf and cumulative distribution function cdf are derived for the bivariate distribution of this class with not necessarily identical fading parameters and average powers two specific distributions with arbitrary number of correlated variates are considered and studied with exponential and with constant correlation where their pdfs are introduced both cases assume equal average fading powers but not necessarily identical fading parameters for the multivariate weibull distribution with exponential correlation useful corresponding formulas as for the bivariate case are derived the presented theoretical results are applied to analyze the performance of several diversity receivers employed with selection equal gain and maximal ratio combining mrc techniques operating over correlated weibull fading channels for these diversity receivers several useful performance criteria such as the moments of the output signal to noise ratio snr including average output snr and amount of fading and outage probability are analytically derived moreover the average symbol error probability for several coherent and noncoherent modulation schemes is studied using the mgf approach the proposed mathematical analysis is complemented by various evaluation results showing the effects of the fading severity as well as the fading correlation on the diversity receivers performance

recognizing planar symbols with severe perspective deformation a common problem encountered in recognizing real scene symbols is the perspective deformation in this paper a recognition method resistant to perspective deformation is proposed based on cross ratio spectrum descriptor this method shows good resistance to severe perspective deformation and good discriminating power to similar symbols

correlations in the multispecies tasep and a conjecture by lam we study correlations in the multispecies tasep on a ring results on the correlation of two adjacent points prove two conjectures by thomas lam on a the limiting direction of a reduced random walk in a over tilde n 1 and b the asymptotic shape of a random integer partition with no hooks of length n a so called n core we further investigate two point correlations far apart and three point nearest neighbour correlations and prove explicit formulas in almost all cases these results can be seen as a finite strengthening of correlations in the tasep speed process by amir angel and valko we also give conjectures for certain higher order nearest neighbour correlations we find an unexplained independence property provably for two points conjecturally for more points between points that are closer in position than in value that deserves more study

space and time bounds on indexing 3d models from 2d images model based visual recognition systems often match groups of image features to groups of model features to form initial hypotheses which are then verified in order to accelerate recognition considerably the model groups can be arranged in an index space hashed offline such that feasible matches are found by indexing into this space for the case of 2d images and 3d models consisting of point features bounds on the space required for indexing and on the speedup that such indexing can achieve are demonstrated it is proved that even in the absence of image error each model must be represented by a 2d surface in the index space this places an unexpected lower bound on the space required to implement indexing and proves that no quantity is invariant for all projections of a model into the image theoretical bounds on the speedup achieved by indexing in the presence of image error are also determined and an implementation of indexing for measuring this speedup empirically is presented it is found that indexing can produce only a minimal speedup on its own however when accompanied by a grouping operation indexing can provide significant speedups that grow exponentially with the number of features in the groups

differential and numerically invariant signature curves applied to object recognition we introduce a new paradigm the differential invariant signature curve or manifold for the invariant recognition of visual objects a general theorem of e cartan implies that two curves are related by a group transformation if and only if their signature curves are identical the important examples of the euclidean and equi affine groups are discussed in detail secondly we show how a new approach to the numerical approximation of differential invariants based on suitable combination of joint invariants of the underlying group action allows one to numerically compute differential invariant signatures in a fully group invariant manner applications to a variety of fundamental issues in vision including detection of symmetries visual tracking and reconstruction of occlusions are discussed

planar object recognition using projective shape representation we describe a model based recognition system called lewis for the identification of planar objects based on a projectively invariant representation of shape the advantages of this shape description include simple model acquisition direct from images no need for camera calibration or object pose computation and the use of index functions we describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using index functions index functions are used to select models from a model base and are constructed from projective invariants based on algebraic curves and a canonical projective coordinate frame examples are given of object recognition from images of real scenes with extensive object libraries successful recognition is demonstrated despite partial occlusion by unmodelled objects and realistic lighting conditions

robust and efficient detection of salient convex groups this paper describes an algorithm that robustly locates salient convex collections of line segments in an image the algorithm is guaranteed to find all convex sets of line segments in which the length of the gaps between segments is smaller than some fixed proportion of the total length of the lines this enables the algorithm to find convex groups whose contours are partially occluded or missing due to noise we give an expected case analysis of the algorithm performance this demonstrates that salient convexity is unlikely to occur at random and hence is a strong clue that grouped line segments reflect underlying structure in the scene we also show that our algorithm run time is o n sup 2 log n nm when we wish to find the m most salient groups in an image with n line segments we support this analysis with experiments on real data and demonstrate the grouping system as part of a complete recognition system

how to initialize a second class particle we identify the ballistically and diffusively rescaled limit distribution of the second class particle position in a wide range of asymmetric and symmetric interacting particle systems with established hydrodynamic behavior respectively including zero range misanthrope and many other models the initial condition is a step profile which in some classical cases of asymmetric models gives rise to a rarefaction fan scenario we also point out a model with nonconcave nonconvex hydrodynamics where the rescaled second class particle distribution has both continuous and discrete counterparts the results follow from a substantial generalization of ferrari and kipnis arguments ann inst h poincare 31 1995 143 154 for the totally asymmetric simple exclusion process the main novelty is the introduction of a signed coupling measure as initial data which nevertheless results in a proper probability initial distribution for the site of the second class particle and makes the extension possible we also reveal in full generality a very interesting invariance property of the one site marginal distribution of the process underneath the second class particle which in particular proves the intrinsicality of our choice for the initial distribution finally we give a lower estimate on the probability of survival of a second class particle antiparticle pair

orientation and anisotropy of multi component shapes from boundary information we define a method for computing the orientation of compound shapes based on boundary information the orientation of a given compound shape s is taken as the direction a that maximises the integral of the squared length of projections of all the straight line segments whose end points belong to particular boundaries of components of s to a line that has the slope a just as the concept of orientation can be extended from single component shapes to multiple components elongation can also be applied to multiple components and we will see that it effectively produces a measure of anisotropy since it is maximised when all components are aligned in the same direction the presented method enables a closed formula for an easy computation of both orientation and anisotropy

zero aliasing correlation filters for object recognition correlation filters cfs are a class of classifiers that are attractive for object localization and tracking applications traditionally cfs have been designed in the frequency domain using the discrete fourier transform dft where correlation is efficiently implemented however existing cf designs do not account for the fact that the multiplication of two dfts in the frequency domain corresponds to a circular correlation in the time spatial domain because this was previously unaccounted for prior cf designs are not truly optimal as their optimization criteria do not accurately quantify their optimization intention in this paper we introduce new zero aliasing constraints that completely eliminate this aliasing problem by ensuring that the optimization criterion for a given cf corresponds to a linear correlation rather than a circular correlation this means that previous cf designs can be significantly improved by this reformulation we demonstrate the benefits of this new cf design approach with several important cfs we present experimental results on diverse data sets and present solutions to the computational challenges associated with computing these cfs code for the cfs described in this paper and their respective zero aliasing versions is available at http vishnu boddeti net projects correlation filters html

the tasep speed process in the multi type totally asymmetric simple exclusion process tasep each site ofz is occupied by a labeled particle and two neighboring particles are int erchanged at rate one if their labels are in increasing order consider the process with the initial con figuration where each particle is labeled by its position it is known that in this case a s each particle has an asymptotic speed which is distributed uniformly on 1 1 we study the joint distribution of these speeds the tasep speed process we prove that the tasep speed process is a stationary measure for the multi type tasep dynamics consequently we can describe every ergodic stationary measure as a projection of the speed process measure this generalizes previous descriptions restricted to finitely many classes by combining this result with known stationary measures for taseps with finitely many types we compute several marginals of the speed process including the joint density of two and three consecutive speeds one striking property of the distribut ion is that two speeds are equal with positive probability and for any given particle there are infinitely m any others with the same speed we also study the partially asymmetric simple exclusion process asep we prove that the states of the asep with the above initial configuration seen as permutations ofz are symmetric in distribution this allows us to extend some of our results i ncluding the stationarity and description of all ergodic stationary measures also to the asep

exact solution of the totally asymmetric simple exclusion process shock profiles the microscopic structure of macroscopic shocks in the one dimensional totally asymmetric simple exclusion process is obtained exactly from the complete solution of the stationary state of a model system containing two types of particles first and second class this nonequilibrium steady state factorizes about any second class particle which implies factorization in the one component system about the random shock position it also exhibits several other interesting features including long range correlations in the limit of zero density of the second class particles the solution also shows that a finite number of second class particles in a uniform background of first class particles form a weakly bound state

finding the limbs and cusps of generalized cylinders this paper addresses the problem of finding analytically the limbs and cusps of generalized cylinders orthographic projections of generalized cylinders whose axis is straight and whose axis is an arbitrary 3d curve are considered in turn in both cases the general equations of the limbs and cusps are given they are solved for three classes of generalized cylinders solids of revolution straight homogeneous generalized cylinders whose scaling sweeping rule is a polynomial of degree less than or equal to 5 and generalized cylinders whose axis is an arbitrary 3d curve but the cross section is circular and constant examples of limbs and cusps found for each class are given applications and extensions to perspective projection and completely general straight generalized cylinders are discussed

shape detection from line drawings with local neighborhood structure an object detection method from line drawings is presented the method adopts the local neighborhood structure as the elementary descriptor which is formed by grouping several nearest neighbor lines curves around one reference with this representation both the appearance and the geometric structure of the line drawing are well described the detection algorithm is a hypothesis test scheme the top k most similar local structures in the drawing are firstly obtained for each local structure of the model and the transformation parameters are estimated for each of the k candidates such as object center scale and rotation factors by treating each estimation result as a point in the parameter space a dense region around the ground truth is then formed provided that there exist a model in the drawing the mean shift method is used to detect the dense regions and the significant modes are accepted as the occurrence of object instances

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

invariant measures for a two species asymmetric process we consider a process of two classes of particles jumping on a one dimensional lattice the marginal system of the first class of particles is the one dimensional totally asymmetric simple exclusion process when classes are disregarded the process is also the totally asymmetric simple exclusion process the existence of a unique invariant measure with product marginals with density and for the first and first plus second class particles respectively was shown by ferrari kipnis and saada recently derrida janowsky lebowitz and speer have computed this invariant measure for finite boxes and performed the infinite volume limit based on this computation we give a complete description of the measure and derive some of its properties in particular we show that the invariant measure for the simple exclusion process as seen from a second class particle with asymptotic densities and is equivalent to the product measure with densities to the left of the origin and to the right of the origin

shape vocabulary a robust and efficient shape representation for shape matching in this paper a learning based shape descriptor for shape matching is demonstrated formulated in a bag of words like framework the proposed method summarizes the local features extracted from certain shape to generate a integrated representation it contributes to the speed up of shape matching since the distance metric in the vector space analysis can be directly applied to compare the constructed global descriptors eliminating the time consuming stage of local feature matching similar to the philosophy in spatial pyramid matching a strategy for feature division is applied in the phase of encoded feature pooling and vocabulary learning which helps to construct a more discriminative descriptor incorporating both global and local information also a local contour based feature extraction method is designed for 2d shapes while significant properties of the local contours are inspected for the design of feature division rules the designed local feature extraction method and the feature division rules manage to reduce the variances of shape representation due to the changes in rotation in addition to 2d shape we also present a simple and natural method to extend the proposed method to the scenario of 3d shape representation the proposed shape descriptor is validated on several benchmark data sets for evaluating 2d and 3d shape matching algorithms and it is observed that the investigated shape descriptor maintains superior discriminative power as well as high time efficiency

retrieval of images of man made structures based on projective invariance in this paper we propose a geometry based image retrieval scheme that makes use of projectively invariant features cross ratio cr is an invariant feature under projective transformations for collinear points we compute the crs of point sets in quadruplets and the cr histogram is used as the feature for retrieval purposes being a geometric feature it allows us to retrieve similar images irrespective of view point and illumination changes we can retrieve the same building even if the facade has undergone a fresh coat of paints color and textural features can also be included if desired experimental results show a favorably very good retrieval accuracy when tested on an image database of size 4000 the method is very effective in retrieving images having man made objects rich in polygonal structures like buildings rail tracks etc

power law polya s urn and fractional brownian motion we introduce a natural family of random walks s n on mathbb z that scale to fractional brownian motion the increments x n s n s n 1 in pm 1 have the property that given x k k n the conditional law of x n is that of x n k n where k n is sampled independently from a fixed law mu on the positive integers when mu has a roughly power law decay precisely when mu lies in the domain of attraction of an alpha stable subordinator for 0 alpha 1 2 the walks scale to fractional brownian motion with hurst parameter alpha 1 2 the walks are easy to simulate and their increments satisfy an fkg inequality in a sense we describe they are the natural fractional analogues of simple random walk on mathbb z

virtual double sided image probing a unifying framework for non linear grayscale pattern matching this paper focuses on non linear pattern matching transforms based on mathematical morphology for gray level image processing our contribution is on two fronts first we unify the existing and a priori unconnected approaches to this problem by establishing their theoretical links with topology setting them within the same context allows to highlight their differences and similarities and to derive new variants second we develop the concept of virtual double sided image probing vdip a broad framework for non linear pattern matching in grayscale images vdip extends our work on the multiple object matching using probing momp transform we previously defined to locate multiple grayscale patterns simultaneously we show that available methods as well as the topological approach can be generalized within the vdip framework they can be formulated as particular variants of a general transform designed for virtual probing furthermore a morphological metric called svdip single vdip is deduced from the vdip concept some results are presented and compared with those obtained with classical methods

discriminant saliency the detection of suspicious coincidences and applications to visual recognition a discriminant formulation of top down visual saliency intrinsically connected to the recognition problem is proposed the new formulation is shown to be closely related to a number of classical principles for the organization of perceptual systems including infomax inference by detection of suspicious coincidences classification with minimal uncertainty and classification with minimum probability of error the implementation of these principles with computational parsimony by exploitation of the statistics of natural images is investigated it is shown that barlow s principle of inference by the detection of suspicious coincidences enables computationally efficient saliency measures which are nearly optimal for classification this principle is adopted for the solution of the two fundamental problems in discriminant saliency feature selection and saliency detection the resulting saliency detector is shown to have a number of interesting properties and act effectively as a focus of attention mechanism for the selection of interest points according to their relevance for visual recognition experimental evidence shows that the selected points have good performance with respect to 1 the ability to localize objects embedded in significant amounts of clutter 2 the ability to capture information relevant for image classification and 3 the richness of the set of visual attributes that can be considered salient

h transforms for wireless communication the h transforms are integral transforms that involve fox s h functions as kernels a large variety of integral transforms can be put into particular forms of the h transform since h functions subsume most of the known special functions including meijer s g functions in this paper we embody the h transform theory into a unifying framework for modeling and analysis in wireless communication first we systematize the use of elementary identities and properties of the h transform by introducing operations on parameter sequences of h functions we then put forth h fading and degree 2 irregular h fading to model radio propagation under composite specular and or inhomogeneous conditions the h fading describes composite effects of multipath fading and shadowing as a single h variate including most of typical models such as rayleigh nakagami m weibull alpha mu n ast nakagami m generalized k fading and weibull gamma fading as its special cases as a new class of h variates called the degree zeta irregular h variate the degree 2 irregular h fading characterizes specular and or inhomogeneous radio propagation in which the multipath component consists of a strong specularly reflected or line of sight los wave as well as unequal power or correlated in phase and quadrature scattered waves this fading includes a variety of typical models such as rician nakagami q kappa mu eta mu rician los gamma and kappa mu los gamma fading as its special cases finally we develop a unifying h transform analysis for the amount of fading error probability channel capacity and error exponent in wireless communication using the new systematic language of transcendental h functions by virtue of two essential operations called mellin and convolution operations involved in the mellin transform and mellin convolution of two h functions the h transforms for these performance measures culminate in h functions using the algebraic asymptotic expansions of the h transform we further analyze the error probability and capacity at high and low signal to noise ratios in a unified fashion

invariant descriptors for 3d object recognition and pose invariant descriptors are shape descriptors that are unaffected by object pose by perspective projection or by the intrinsic parameters of the camera these descriptors can be constructed using the methods of invariant theory which are briefly surveyed a range of applications of invariant descriptors in 3d model based vision is demonstrated first a model based vision system that recognizes curved plane objects irrespective of their pose is demonstrated curves are not reduced to polyhedral approximations but are handled as objects in their own right models are generated directly from image data once objects have been recognized their pose can be computed invariant descriptors for 3d objects with plane faces are described all these ideas are demonstrated using images of real scenes the stability of a range of invariant descriptors to measurement error is treated in detail

quasi invariant parameterisations and matching of curves in images in this paper we investigate quasi invariance on a smooth manifold and show that there exist quasi invariant parameterisations which are not exactly invariant but approximately invariant under group transformations and do not require high order derivatives the affine quasi invariant parameterisation is investigated in more detail and exploited for defining general affine semi local invariants from second order derivatives only the new invariants are implemented and used for matching curve segments under general affine motions and extracting symmetry axes of objects with 3d bilateral symmetry

volumetric descriptions from a single intensity image since the early days of computer vision research shape from contour has been one of the most challenging problems many researchers in the field have attempted to understand this problem and proposed different approaches to solve it shape from contour still remains one of the hardest problems in the field the problem has two major difficulties first 2d properties of contours of viewed objects are generally not sufficient by themselves to uniquely determine 3d shape as one dimension is lost in the projection second real images produce imperfect contours that make their interpretation particularly difficult the first problem has received some attention in the research community but in the context of perfect contours the second one however has received very little in this work we propose a promising methodology to address this last problem for a large class of objects generalized cylinders it is based on exploiting mathematical invariant properties of the contours of generalized cylinders in a perceptual grouping approach we show that using these properties greatly helps addressing the figure ground problem in a more rigorous way than previous intuitive perceptual grouping methods our approach exploits the interplay between local and global features by handling different levels of the feature hierarchy we have developed and implemented a method that handles shgcs in complex seenes with markings and occlusion we demonstrate the application of our method of shape description and scene segmentation on complex real images we also demonstrate the usage of the obtained descriptions for recovery of complete 3 d object centered descriptions of viewed objects from a single intensity image

localizing overlapping parts by searching the interpretation tree this paper discusses how local measurements of positions and surface normals may be used to identify and locate overlapping objects the objects are modeled as polyhedra or polygons having up to six degrees of positional freedom relative to the sensors the approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on distances between faces angles between face normals and angles relative to the surface normals of vectors between sensed points the method described here is an extension of a method for recognition and localization of nonoverlapping parts previously described in 18 and 15

semi local projective invariants for the recognition of smooth plane curves recently several methods have been proposed for describing plane non algebraic curves in a projectively invariant fashion these curve representations are invariant under changes in viewpoint and therefore ideally suited for recognition we report the results of a study where the strengths and weaknesses of a number of semi local methods are compared on the basis of the same images and edge data all the methods define a distinguished or canonical projective frame for the curve segment which is used for projective normalisation in this canonical frame the curve has a viewpoint invariant signature measurements on the signature are invariants all the methods presented are designed to work on real images where extracted data will not be ideal and parts of curves will be missing because of poor contrast or occlusion we compare the stability and discrimination of the signatures and invariants over a number of example curves and viewpoints the paper concludes with a discussion of how the various methods can be integrated within a recognition system

busemann functions and the speed of a second class particle in the rarefaction fan in this paper we will show how the results found in probab theory related fields 154 2012 89 125 about the busemann functions in last passage percolation can be used to calculate the asymptotic distribution of the speed of a single second class particle starting from an arbitrary deterministic configuration which has a rarefaction fan in either the totally asymetric exclusion process or the hammersley interacting particle process the method will be to use the well known last passage percolation description of the exclusion process and of the hammersley process and then the well known connection between second class particles and competition interfaces

integral invariants for shape matching for shapes represented as closed planar contours we introduce a class of functionals which are invariant with respect to the euclidean group and which are obtained by performing integral operations while such integral invariants enjoy some of the desirable properties of their differential counterparts such as locality of computation which allows matching under occlusions and uniqueness of representation asymptotically they do not exhibit the noise sensitivity associated with differential quantities and therefore do not require presmoothing of the input shape our formulation allows the analysis of shapes at multiple scales based on integral invariants we define a notion of distance between shapes the proposed distance measure can be computed efficiently and allows warping the shape boundaries onto each other its computation results in optimal point correspondence as an intermediate step numerical results on shape matching demonstrate that this framework can match shapes despite the deformation of subparts missing parts and noise as a quantitative analysis we report matching scores for shape retrieval from a database

applying hankel s expansion for performance analysis in double nakagami generalized k fading channels the double scattering fading models have found applications in many transmission scenarios in this paper approximate probability density functions pdfs are proposed for double nakagami fading and gamma gamma fading based on hankel s expansion of the modified bessel function of the second kind the proposed pdf is not only consistent with the exact pdf when the difference between the two shape parameters is equal to an integer plus one half but also accurately approximates the distribution when the shape parameters are both greater than or equal to 2 the proposed approximate pdf has been compared with the power series expansion and the mixture gamma model it is found that the proposed pdf is in simple form and no parameters need to be tabulated moreover the proposed pdf does not require an increasing number of terms to maintain precision with increasing diversity order of the reception scheme closed form outage probability ergodic capacity and symbol error rate expressions have been derived using the proposed approximate pdfs for equal gain combining selection combining and two stage reception schemes the proposed approximations are also leveraged for performance analysis of space time block codes and free space optical links the precision of the proposed approximations is validated by comparing with the simulation results

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

a novel shape based non redundant local binary pattern descriptor for object detection motivated by the discriminative ability of shape information and local patterns in object recognition this paper proposes a window based object descriptor that integrates both cues in particular contour templates representing object shape are used to derive a set of so called key points at which local appearance features are extracted these key points are located using an improved template matching method that utilises both spatial and orientation information in a simple and effective way at each of the extracted key points a new local appearance feature namely non redundant local binary pattern nr lbp is computed an object descriptor is formed by concatenating the nr lbp features from all key points to encode the shape as well as the appearance of the object the proposed descriptor was extensively tested in the task of detecting humans from static images on the commonly used mit and inria datasets the experimental results have shown that the proposed descriptor can effectively describe non rigid objects with high articulation and improve the detection rate compared to other state of the art object descriptors

three dimensional descriptions based on the analysis of the invariant and quasi invariant properties of some curved axis generalized cylinders we address the recovery of object level 3d descriptions of some classes of curved axis generalized cylinders for this the first part of the paper analyzes the projective properties of two common generic shapes planar right constant generalized cylinders prcgcs and circular planar right generalized cylinders circular prgcs the properties we analyze include new geometric invariant and quasi invariant properties of the orthographic projection of the above shapes and a useful classification of their structural properties as functions of their pose the second part of the paper describes an implemented system which detects and recovers prcgcs and circular prgcs from an intensity image in the presence of noise surface markings shadows and partial occlusion the methods exploit the projective properties to hypothesize and verify relevant curved axis objects thus explicitly using the three dimensionality of the objects and of the desired descriptions this work extends past work on the recovery of volumetric shapes from an intensity image by addressing new primitives deriving new properties and by developing a system that recovers them from an intensity image we demonstrate our method on several real intensity images

on the second order statistics of the multihop rayleigh fading channel second order statistics provides a dynamic representation of a fading channel and plays an important role in the evaluation and design of the wireless communication systems in this paper we present a novel analytical framework for the evaluation of important second order statistical parameters as the level crossing rate lcr and the average fade duration afd of the amplify and forward multihop rayleigh fading channel more specifically motivated by the fact that this channel is a cascaded one and can be modeled as the product of n fading amplitudes we derive novel analytical expressions for the average lcr and the afd of the product of n rayleigh fading envelopes or of the recently so called nrayleigh channel furthermore we derive simple and efficient closed form approximations to the aforementioned parameters using the multivariate laplace approximation theorem it is shown that our general results reduce to the corresponding ones of the specific dual hop case previously published numerical and computer simulation examples verify the accuracy of the presented mathematical analysis and show the tightness of the proposed approximations

product of two envelopes taken from alpha mu kappa mu and eta mu distributions this paper presents exact novel formulations for the probability density function and cumulative distribution function for the product of two independent and non identically distributed alpha mu kappa mu and eta mu variates the expressions are given in terms of both 1 generalized fox h function and 2 easily computable series expansions the formulations derived can be directly used to explore the performance of a number of wireless communication processes including multihop systems cascaded channels radar communications multiple input multiple output links and others due to the high flexibility of the above mentioned distributions the results presented here comprise a substantial number of useful product distributions as application examples performance metrics for the cascaded fading channel are derived the validity of the expressions is confirmed via monte carlo simulation it is noteworthy that because any composite multipath shadowing fading model is obtained as a particular case of the product of two fading variables the results given here provide a plethora of composite multipath shadowing fading scenarios

n ast nakagami a novel stochastic model for cascaded fading channels a generic and novel distribution referred to as nakagami constructed as the product of n statistically independent but not necessarily identically distributed nakagami m random variables rvs is introduced and analyzed the proposed distribution turns out to be a very convenient tool for modelling cascaded nakagami m fading channels and analyzing the performance of digital communications systems operating over such channels the moments generating probability density cumulative distribution and moments functions of the n nakagami distribution are developed in closed form using the meijer s g function using these formulas generic closed form expressions for the outage probability amount of fading and average error probabilities for several binary and multilevel modulation signals of digital communication systems operating over the n nakagami fading and the additive white gaussian noise channel are presented complementary numerical and computer simulation performance evaluation results verify the correctness of the proposed formulation the suitability of the n nakagami fading distribution to approximate the lognormal distribution is also being investigated using kolmogorov smirnov tests the rate of convergence of the central limit theorem as pertaining to the multiplication of nakagami m rvs is quantified

efficient subwindow search a branch and bound framework for object localization most successful object recognition systems rely on binary classification deciding only if an object is present or not but not providing information on the actual object location to estimate the object s location one can take a sliding window approach but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows in this paper we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages it converges to a globally optimal solution typically in linear or even sublinear time in contrast to the quadratic scaling of exhaustive or sliding window search we show how our method is applicable to different object detection and image retrieval scenarios the achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task such as svms with a spatial pyramid kernel or nearest neighbor classifiers based on the lambda2 distance we demonstrate state of the art localization performance of the resulting systems on the uiuc cars data set the pascal voc 2006 data set and in the pascal voc 2007 competition

contour grouping based on contour skeleton duality in this paper we present a method for grouping relevant object contours in edge maps by taking advantage of contour skeleton duality regularizing contours and skeletons simultaneously allows us to combine both low level perceptual constraints as well as higher level model constraints in a very effective way the models are represented using paths in symmetry sets skeletons are treated as trajectories of an imaginary virtual robot in a discrete space of symmetric points obtained from pairs of edge segments boundaries are then defined as the maps obtained by grouping the associated pairs of edge segments along the trajectories casting the grouping problem in this manner makes it similar to the problem of simultaneous localization and mapping slam hence we adapt the state of the art probabilistic framework namely rao blackwellized particle filtering that has been successfully applied to slam we use the framework to maximize the joint posterior over skeletons and contours

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

efficient graph based search for object detection in this paper we focus on the object localization problem in images given a single hand drawn example as the object model we propose a novel framework for shape based object detection and recognition which we formulate as a graph based search problem in our method we first propose five preprocessing procedures to reduce the irrelevant edge fragments in cluttered real images that often occur in edge detection then we build a graph to represent the edge images therefore our goal is to find the group of adjacent nodes in the graph that match well to the model contours finally we present a new evaluation method to verify the candidate hypotheses we did experiments on the ethz shape classes dataset and the inria horses dataset experimental results demonstrate that the proposed method achieves not only accurate object detection but also precise contour localization in cluttered real images comparisons with other recent template based matching methods further demonstrate the effectiveness and efficiency of the proposed method

the oriented swap process particles labelled 1 n are initially arranged in increasing order subsequently each pair of neighboring particles that is currently in increasing order swaps according to a poisson process of rate 1 we analyze the asymptotic behavior of this process as n oo we prove that the space time trajectories of individual particles converge when suitably scaled to a certain family of random curves with two points of non differentiability and that the permutation matrix at a given time converges to a certain deterministic measure with absolutely continuous and singular parts the absorbing state where all particles are in decreasing order is reached at time 2 o 1 n the finishing times of individual particles converge to deterministic limits with fluctuations asymptotically governed by the tracy widom distribution

rigid shape matching by segmentation averaging we use segmentations to match images by shape the new matching technique does not require point to point edge correspondence and is robust to small shape variations and spatial shifts to address the unreliability of segmentations computed bottom up we give a closed form approximation to an average over all segmentations our method has many extension yielding new algorithms for tracking object detection segmentation and edge preserving smoothing for segmentation instead of a maximum a posteriori approach we compute the central segmentation minimizing the average distance to all segmentations of an image for smoothing instead of smoothing images based on local structures we smooth based on the global optimal image structures our methods for segmentation smoothing and object detection perform competitively and we also show promising results in shape based tracking

structural indexing efficient 3 d object recognition the authors present an approach for the recognition of multiple 3 d object models from three 3 d scene data the approach uses two different types of primitives for matching small surface patches where differential properties can be reliably computed and lines corresponding to depth or orientation discontinuities these are represented by splashes and 3 d curves respectively it is shown how both of these primitives can be encoded by a set of super segments consisting of connected linear segments these super segments are entered into a table and provide the essential mechanism for fast retrieval and matching the issues of robustness and stability of the features are addressed in detail the acquisition of the 3 d models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3 d curves the authors present results with the current system 3 d object recognition based on super segments and discuss further extensions

statistical analysis and characterization of the indoor propagation channel this paper presents statistical analysis of data obtained by measuring narrowband path loss at dect digital enhanced cordless telecommunications frequency 1 89 ghz in an indoor environment specific goodness of fit tests are applied to the data the tests assess whether the data generating source belongs to a known family of random variables results obtained support that local path loss distribution can be represented as weibull or nakagami in most environments the close resemblance between such distributions and rice distribution with rayleigh as a special case confirms that rice rayleigh description of fading can be applied to indoor environments that similarity leads to a general simple approximate expression for the cumulative distribution function of the signal to interference ratio the expression can be used without limitations to the number and the parameters of the rice interferers

interpreting line drawings as three dimensional surfaces understanding how line drawings convey tri dimensionality is of fundamental importance in explaining surface perception when photometry is either uninformative or too complex to model analytically we put forward here a computational model for interpreting line drawings as three dimensional surfaces based on constraints on local surface orientation along extremal and discontinuity boundaries specific techniques are described for two key processes recovering the three dimensional conformation of a space curve e g a surface boundary from its two dimensional projection in an image and interpolating smooth surfaces from orientation constraints along extremal boundaries the relevance of the model to a general theory of low level vision is discussed

inverse perspective transform using zero curvature contour points application to the localization of some generalized cylinders from a single view the localization of some kinds of modeled generalized cylinders from a single brightness perspective image is addressed it is shown how the zero curvature points of their contours can be used to solve the inverse perspective problem three key theorems about the perspective projection of space curves and of the limbs of a straight homogeneous generalized cylinder whose scaling function has at least one zero curvature point are discussed in view of the localization of homogeneous generalized cylinders an algorithm previously developed by the authors which estimates the pose of a line triplet is adapted a new theoretical result about the inverse perspective projection of cones of revolution useful for the localization of objects of revolution is presented the corresponding algorithms have been implemented and results of experiments demonstrate the feasibility of the proposed localization methods

local symmetry detection in natural images using a particle filtering approach in this paper we propose an algorithm to detect smooth local symmetries and contours of ribbon like objects in natural images the detection is formulated as a spatial tracking task using a particle filtering approach extracting one part of a structure at a time using an adaptive local geometric model the method can detect straight reflection symmetries in perfectly symmetrical objects as well as smooth local symmetries in curved elongated objects in addition the proposed approach jointly estimates spine and contours making it possible to generate back ribbon objects experiments for local symmetry detection have been conducted on a recent extension of the berkeley segmentation data sets we also show that it is possible to retrieve specific geometrical objects using intuitive prior structural information

matching trajectories between video sequences by exploiting a sparse projective invariant representation identifying correspondences between trajectory segments observed from nonsynchronized cameras is important for reconstruction of the complete trajectory of moving targets in a large scene such a reconstruction can be obtained from motion data by comparing the trajectory segments and estimating both the spatial and temporal alignments exhaustive testing of all possible correspondences of trajectories over a temporal window is only viable in the cases with a limited number of moving targets and large view overlaps therefore alternative solutions are required for situations with several trajectories that are only partially visible in each view in this paper we propose a new method that is based on view invariant representation of trajectories which is used to produce a sparse set of salient points for trajectory segments observed in each view only the neighborhoods at these salient points in the view invariant representation are then used to estimate the spatial and temporal alignment of trajectory pairs in different views it is demonstrated that for planar scenes the method is able to recover with good precision and efficiency both spatial and temporal alignments even given relatively small overlap between views and arbitrary unknown temporal shifts of the cameras the method also provides the same capabilities in the case of trajectories that are only locally planar but exhibit some nonplanarity at a global level

contour based object detection as dominant set computation contour based object detection can be formulated as a matching problem between model contour parts and image edge fragments we propose a novel solution by treating this problem as the problem of finding dominant sets in weighted graphs the nodes of the graph are pairs composed of model contour parts and image edge fragments and the weights between nodes are based on shape similarity because of high consistency between correct correspondences the correct matching corresponds to a dominant set of the graph consequently when a dominant set is determined it provides a selection of correct correspondences as the proposed method is able to get all the dominant sets we can detect multiple objects in an image in one pass moreover since our approach is purely based on shape we also determine an optimal scale of target object without a common enumeration of all possible scales both theoretic analysis and extensive experimental evaluation illustrate the benefits of our approach

bounds for multihop relayed communications in nakagami m fading we present closed form lower bounds for the performance of multihop transmissions with nonregenerative relays over not necessarily identically distributed nakagami m fading channels the end to end signal to noise ratio is formulated and upper bounded by using an inequality between harmonic and geometric means of positive random variables rvs novel closed form expressions are derived for the moment generating function the probability density function and the cumulative distribution function of the product of rational powers of statistically independent gamma rvs these statistical results are then applied to studying the outage probability and the average bit error probability for phase and frequency modulated signaling numerical examples compare analytical and simulation results verifying the tightness of the proposed bounds

recovery of the three dimensional shape of an object from a single view given a single picture which is a projection of a three dimensional scene onto the two dimensional picture plane we usually have definite ideas about the 3 d shapes of objects to do this we need to use assumptions about the world and the image formation process since there exist a large number of shapes which can produce the same picture the purpose of this paper is to identify some of these assumptions mostly geometrical ones by demonstrating how the theory and techniques which exploit such assumptions can provide a systematic shape recovery method the method consists of two parts the first is the application of the origami theory which models the world as a collection of plane surfaces and recovers the possible shapes qualitatively the second is the technique of mapping image regularities into shape constraints for recovering the probable shapes quantitatively actual shape recovery from a single view is demonstrated for the scenes of an object such as a box and a chair given a single image the method recovers the 3 d shapes of an object in it and generates images of the same object as we would see it from other directions

combining color and shape information for illumination viewpoint invariant object recognition in this paper we propose a new scheme that merges color and shape invariant information for object recognition to obtain robustness against photometric changes color invariant derivatives are computed first color invariance is an important aspect of any object recognition scheme as color changes considerably with the variation in illumination object pose and camera viewpoint these color invariant derivatives are then used to obtain similarity invariant shape descriptors shape invariance is equally important as under a change in camera viewpoint and object pose the shape of a rigid object undergoes a perspective projection on the image plane then the color and shape invariants are combined in a multidimensional color shape context which is subsequently used as an index as the indexing scheme makes use of a color shape invariant context it provides a high discriminative information cue robust against varying imaging conditions the matching function of the color shape context allows for fast recognition even in the presence of object occlusion and cluttering from the experimental results it is shown that the method recognizes rigid objects with high accuracy in 3 d complex scenes and is robust against changing illumination camera viewpoint object pose and noise