recognition using region correspondences recognition systems attempt to recover information about the identity of observed objects and their location in the environment a fundamental problem in recognition is pose estimation this is the problem of using a correspondence between some portions of an object model and some portions of an image to determine whether the image contains an instance of the object and in case it does to determine the transformation that relates the model to the image the current approaches to this problem are divided into methods that use global properties of the object e g centroid and moments of inertia and methods that use local properties of the object e g corners and line segments global properties are sensitive to occlusion and specifically to self occlusion local properties are difficult to locate reliably and their matching involves intensive computation we present a novel method for recognition that uses region information in our approach the model and the image are divided into regions given a match between subsets of regions without any explicit correspondence between different pieces of the regions the alignment transformation is computed the method applies to planar objects under similarity affine and projective transformations and to projections of 3 d objects undergoing affine and projective transformations the new approach combines many of the advantages of the previous two approaches while avoiding some of their pitfalls like the global methods our approach makes use of region information that reflects the true shape of the object but like local methods our approach can handle occlusion

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

bifurcation analysis of a speed gradient continuum traffic flow model abstract a bifurcation analysis approach is presented based on the macroscopic traffic flow model this method can be used to describe and predict the nonlinear traffic phenomena on the highway from a system global stability perspective based on a recently proposed speed gradient continuum traffic flow model the types and stabilities of the equilibrium solutions are discussed and the existence of hopf bifurcation and saddle node bifurcation is proved then various bifurcations such as hopf bifurcation saddle node bifurcation limit point bifurcation of cycles cusp bifurcation and bogdanov takens bifurcation are found and the traffic flow behaviors at some of them are analyzed when the hopf bifurcation is selected as the starting point of density temporal evolution it may help to explain the stop and go traffic phenomena

learning a probabilistic topology discovering model for scene categorization a recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components to that effect local features are typically used to handle photographing variances such as occlusions and clutters however in many cases the local features alone cannot well capture the scene semantics since they are extracted from tiny regions e g 4 times 4 patches within an image in this paper we mine a discriminative topology and a low redundant topology from the local descriptors under a probabilistic perspective which are further integrated into a boosting framework for scene categorization in particular by decomposing a scene image into basic components a graphlet model is used to describe their spatial interactions accordingly scene categorization is formulated as an intergraphlet matching problem the above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes the selected graphlets are highly discriminative and independent characterizing the topological characteristics of scene images a weak learner is subsequently trained for each topology which are boosted together to jointly describe the scene image in our experiment the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories and our proposed method beats state of the art models on five popular scene data sets

transition to chaotic motion of a cyclic bus induced by nonstops abstract we study the dynamical behavior of a cyclic bus on a circular route with many bus stops when the cyclic bus passes some bus stops without stopping to retrieve the delay the recurrent time one period is described in terms of a piecewise nonlinear map it is found that the cyclic bus exhibits a dynamical transition to chaotic behavior when the number of bus stops without stopping is larger than the critical value it is shown that there are three distinct dynamical states the schedule time phase convergence of the recurrent time the delay phase divergence of the recurrent time and the chaotic motion liapunov exponent is calculated for distinct dynamical states the chaotic motion depends on the property of an attractor of the nonlinear map

analysis of a continuous car following model for a bus route existence stability and bifurcations of synchronous motions abstract this paper studies the synchronous motions motions for which each of the buses moves with a constant velocity of a car following model for a circular bus route different types of synchronous motion are identified and their local stability is studied using root locus methods the bifurcations of synchronous motions are described and simulation results are given

vehicular traffic through a self similar sequence of traffic lights abstract we study the dynamical behavior of vehicular traffic through a sequence of traffic lights positioned self similarly on a highway where all traffic lights turn on and off simultaneously with cycle time t s the signals are positioned self similarly by cantor set the nonlinear map model of vehicular traffic controlled by self similar signals is presented the vehicle exhibits the complex behavior with varying cycle time the tour time is much lower such that signals are positioned periodically with the same interval the arrival time t x at position x scales as t x x x d f where d f is the fractal dimension of cantor set the landscape in the plot of t x x against cycle time t s shows a self affine fractal with roughness exponent 1 d f

passenger s fluctuation and chaos on ferryboats abstract we study the fluctuation of shipping passengers on a few ferryboats which shuttle between an origin and a destination repeatedly we present the dynamical model for the ferryboats the model is described in terms of nonlinear maps defined from the vectors ti n and wi n i 1 2 n for n ferryboats where ti n is the arrival time of ferryboat i at the origin on trip n and wi n the number of passengers waiting at the origin on trip n we clarify the fluctuations of shipping passengers and tour time for the ferry schedule it is found that the dynamical transitions among the regular periodic and chaotic motions occur by varying the ferry s capacity fmax headway tmin and loading parameter even if the second ferryboat follows the leader first ferryboat keeping the constant headway the passengers shipping on the second fluctuate highly when the parameters take specific values

variable length signature for near duplicate image matching we propose a variable length signature for near duplicate image matching in this paper an image is represented by a signature the length of which varies with respect to the number of patches in the image a new visual descriptor viz probabilistic center symmetric local binary pattern is proposed to characterize the appearance of each image patch beyond each individual patch the spatial relationships among the patches are captured in order to compute the similarity between two images we utilize the earth mover s distance which is good at handling variable length signatures the proposed image signature is evaluated in two different applications i e near duplicate document image retrieval and near duplicate natural image detection the promising experimental results demonstrate the validity and effectiveness of the proposed variable length signature

an eigendecomposition approach to weighted graph matching problems an approximate solution to the weighted graph matching problem is discussed for both undirected and directed graphs the weighted graph matching problem is that of finding the optimum matching between two weighted graphs which are graphs with weights at each arc the proposed method uses an analytic instead of a combinatorial or iterative approach to the optimum matching problem using the eigendecompositions of the adjacency matrices in the case of the undirected graph matching problem or hermitian matrices derived from the adjacency matrices in the case of the directed graph matching problem a matching close to the optimum can be found efficiently when the graphs are sufficiently close to each other simulation results are given to evaluate the performance of the proposed method

a low complexity region based video coder using backward morphological motion field segmentation we introduce a novel region based video compression framework based on morphology to efficiently capture motion correspondences between consecutive frames in an image sequence our coder is built on the observation that the motion field associated with typical image sequences can be segmented into component motion subfield clusters associated with distinct objects or regions in the scene and further that these clusters can be efficiently captured using morphological operators in a backward framework that avoids the need to send region shape information region segmentation is performed directly on the motion field by introducing a small core for each cluster that captures the essential features of the cluster and reliably represents its motion behavior cluster matching is used in lieu of the conventional block matching methods of standard video coders to define a cluster motion representation paradigm furthermore a region based pel recursive approach is applied to find the refinement motion field for each cluster and the cluster motion prediction error image is coded by a novel adaptive scalar quantization method experimental results reveal a 10 20 reduction in prediction error energy and 1 3 db gain in the final reconstructed peak signal to noise ratio psnr over the standard mpeg 1 coder at typical bit rates of 500 kb s to 1 mb s on standard test sequences while also requiring lower computational complexity

an image understanding system using attributed symbolic representation and inexact graph matching this paper presents a powerful image understanding system that utilizes a semantic syntactic or attributed synibolic representation scheme in the form of attributed relational graphs arg s for comprehending the global information contents of images nodes in the arg represent the global image features while the relations between those features are represented by attributed branches between their corresponding nodes the extraction of arg representation from images is achieved by a multilayer graph transducer scheme this scheme is basically a rule based system that uses a combination of model driven and data driven concepts in performing a hierarchical symbolic mapping of the image information content from the spatial domain representation into a global representation further analysis and inter pretation of the imagery data is performed on the extracted arg representation a distance measure between images is defined in terms of the distance between their respective arg representations the distance between two arg s and the inexact matching of their respective components are calculated by an efficient dynamic programming technique the system handles noise distortion and ambiguity in real world images by two means namely through modeling and embedding them into the transducer s mapping rules as well as through the appropriate cost of error transformation for the inexact matching of the arg image representation two illustrative experiments are presented to demonstrate some capabilities of the proposed system experiment i deals with locating objects in multiobject scenes while experiment ii is concerned with target detection in sar images

learning shape classes using a mixture of tree unions this paper poses the problem of tree clustering as that of fitting a mixture of tree unions to a set of sample trees the tree unions are structures from which the individual data samples belonging to a cluster can be obtained by edit operations the distribution of observed tree nodes in each cluster sample is assumed to be governed by a bernoulli distribution the clustering method is designed to operate when the correspondences between nodes are unknown and must be inferred as part of the learning process we adopt a minimum description length approach to the problem of fitting the mixture model to data we make maximum likelihood estimates of the bernoulli parameters the tree unions and the mixing proportions are sought so as to minimize the description length criterion this is the sum of the negative logarithm of the bernoulli distribution and a message length criterion that encodes both the complexity of the union trees and the number of mixture components we locate node correspondences by minimizing the edit distance with the current tree unions and show that the edit distance is linked to the description length criterion the method can be applied to both unweighted and weighted trees we illustrate the utility of the resulting algorithm on the problem of classifying 20 shapes using a shock graph representation

new exact traveling and non traveling wave solutions for 2 1 dimensional burgers equation abstract in this paper some novel solitary wave solutions including solitary like wave solution x periodic soliton solution y periodic soliton solution doubly periodic solution rational solution and new non traveling wave solution are obtained for 2 1 dimensional burgers equation by means of the generalized direct ansatz method and different test functions

multiscale image segmentation by integrated edge and region detection this paper is concerned with the detection of low level structure in images it describes an algorithm for image segmentation at multiple scales the detected regions are homogeneous and surrounded by closed edge contours previous approaches to multiscale segmentation represent an image at different scales using a scale space however structure is only represented implicitly in this representation structures at coarser scales are inherently smoothed and the problem of structure extraction is unaddressed this paper argues that the issues of scale selection and structure detection cannot be treated separately a new concept of scale is presented that represents image structures at different scales and not the image itself this scale is integrated into a nonlinear transform which makes structure explicit in the transformed domain structures that are stable locally invariant to changes in scale are identified as being perceptually relevant the transform can be viewed as collecting spatially distributed evidence for edges and regions and making it available at contour locations thereby facilitating integrated detection of edges and regions without restrictive models of geometry or homogeneity in this sense it performs gestalt analysis all scale parameters of the transform are automatically determined and the structure of any arbitrary geometry can be identified without any smoothing even at coarse scales

approximating the maximum weight clique using replicator dynamics given an undirected graph with weights on the vertices the maximum weight clique problem mwcp is to find a subset of mutually adjacent vertices a clique having the largest total weight this is a generalization of the problem of finding the maximum cardinality clique of an unweighted graph which is the special case of the mwcp when all vertex weights are equal the problem is np hard for arbitrary graphs and so is the problem of approximating it within a constant factor we present a parallel distributed heuristic for approximating the mwcp based on dynamics principles it centers around a continuous characterization of the mwcp a purely combinatorial problem and lets it be formulated in terms of continuous quadratic programming one drawback is the presence of spurious solutions and we present their characterizations to avoid them we introduce a regularized continuous formulation of the mwcp and show how it completely solves the problem the formulation naturally maps onto a parallel distributed computational network whose dynamical behavior is governed by the replicator equations these are dynamical systems introduced in evolutionary game theory and population genetics to model evolutionary processes on a macroscopic scale we present theoretical results which guarantee that the solutions provided by our clique finding replicator network are actually those sought experimental results confirm the effectiveness of the proposed approach

on pattern formation in a class of traffic models abstract we consider a simple microscopic follow the leader model of n identical cars on a circular road in the classical setting a car is not allowed to reach its leader if it happens at a particular time t e we propose to extend the model beyond this time t e the idea is to define a physically reasonable leader for each car mathematically the resulting model is a kind of filippov system the objective is to study a long time behavior of the model in case n 3 we observed several invariant patterns which apparently exhibit spatial temporal symmetries we managed to classify and identify two kinds of rotating waves the classification exploits symbolic dynamical notions

polynomial time metrics for attributed trees we address the problem of comparing attributed trees and propose four novel distance measures centered around the notion of a maximal similarity common subtree the proposed measures are general and defined on trees endowed with either symbolic or continuous valued attributes and can be applied to rooted as well as unrooted trees we prove that our measures satisfy the metric constraints and provide a polynomial time algorithm to compute them this is a remarkable and attractive property since the computation of traditional edit distance based metrics is in general np complete at least in the unordered case we experimentally validate the usefulness of our metrics on shape matching tasks and compare them with an approximation of edit distance

indexing hierarchical structures using graph spectra hierarchical image structures are abundant in computer vision and have been used to encode part structure scale spaces and a variety of multiresolution features in this paper we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph dag into a low dimensional vector space based on a novel spectral characterization of a dag this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest neighbor search we establish the insensitivity of the signature to minor perturbation of graph structure due to noise occlusion or node split merge to accommodate large scale occlusion the dag rooted at each nonleaf node of the query votes for model objects that share that part effectively accumulating local evidence in a model dag s topological subspaces we demonstrate the approach with a series of indexing experiments in the domain of view based 3d object recognition using shock graphs

matching hierarchical structures using association graphs it is well known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a derived association graph however it is not clear how to apply this approach to computer vision problems where the graphs are hierarchically organized i e are trees since maximal cliques are not constrained to preserve the partial order we provide a solution to the problem of matching two trees by constructing the association graph using the graph theoretic concept of connectivity we prove that in the new formulation there is a one to one correspondence between maximal cliques and maximal subtree isomorphisms this allows us to cast the tree matching problem as an indefinite quadratic program using the motzkin straus theorem and we use replicator dynamical systems developed in theoretical biology to solve it such continuous solutions to discrete problems are attractive because they can motivate analog and biological implementations the framework is also extended to the matching of attributed trees by using weighted association graphs we illustrate the power of the approach by matching articulated and deformed shapes described by shock trees

pattern vectors from algebraic graph theory graph structures have proven computationally cumbersome for pattern analysis the reason for this is that before graphs can be converted to pattern vectors correspondences must be established between the nodes of structures which are potentially of different size to overcome this problem in this paper we turn to the spectral decomposition of the laplacian matrix we show how the elements of the spectral matrix for the laplacian can be used to construct symmetric polynomials that are permutation invariants the coefficients of these polynomials can be used as graph features which can be encoded in a vectorial manner we extend this representation to graphs in which there are unary attributes on the nodes and binary attributes on the edges by using the spectral decomposition of a hermitian property matrix that can be viewed as a complex analogue of the laplacian to embed the graphs in a pattern space we explore whether the vectors of invariants can be embedded in a low dimensional space using a number of alternative strategies including principal components analysis pca multidimensional scaling mds and locality preserving projection lpp experimentally we demonstrate that the embeddings result in well defined graph clusters our experiments with the spectral representation involve both synthetic and real world data the experiments with synthetic data demonstrate that the distances between spectral feature vectors can be used to discriminate between graphs on the basis of their structure the real world experiments show that the method can be used to locate clusters of graphs

multiple kink solutions and multiple singular kink solutions for the 2 1 dimensional burgers equations abstract in this work we study the 2 1 dimensional painleve integrable burgers equation multiple kink solutions and multiple singular kink solutions are formally derived for this equation the analysis depends mainly on the hirota s bilinear method

interactive image segmentation by matching attributed relational graphs a model based graph matching approach is proposed for interactive image segmentation it starts from an over segmentation of the input image exploiting color and spatial information among regions to propagate the labels from the regions marked by the user provided seeds to the entire image the region merging procedure is performed by matching two graphs the input graph representing the entire image and the model graph representing only the marked regions the optimization is based on discrete search using deformed graphs to efficiently evaluate the spatial information note that by using a model based approach different interactive segmentation problems can be tackled binary and multi label segmentation of single images as well as of multiple similar images successful results for all these cases are presented in addition to a comparison between our binary segmentation results and those obtained with state of the art approaches an implementation is available at http structuralsegm sourceforge net

region based hierarchical image matching this paper presents an approach to region based hierarchical image matching where given two images the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions e g area boundary shape and color as well as region topology e g recursive embedding of regions to this end each image is represented by a tree of recursively embedded regions obtained by a multiscale segmentation algorithm this allows us to pose image matching as the tree matching problem to overcome imaging noise one to one many to one and many to many node correspondences are allowed the trees are first augmented with new nodes generated by merging adjacent sibling nodes which produces directed acyclic graphs dags then transitive closures of the dags are constructed and the tree matching problem reformulated as finding a bijection between the two transitive closures on dags while preserving the connectivity and ancestor descendant relationships of the original trees the proposed approach is validated on real images showing similar objects captured under different types of noise including differences in lighting conditions scales or viewpoints amidst limited occlusion and clutter

an algorithm based on exponential modified cubic b spline differential quadrature method for nonlinear burgers equation in this paper the authors developed a new differential quadrature method exponential modified cubic b spline differential quadrature method expo mcb dqm by using exponential modified cubic b spline functions as test functions in the traditional differential quadrature method 32 the new method is tested on one and two dimensional nonlinear burgers equations to check the efficiency and accuracy of the proposed method five numerical problems have been considered the numerical results of the method are compared with some existing methods and found that the proposed numerical method produces more accurate results than existing methods stability analysis of the algorithm is also done by using matrix stability analysis method

transitions to chaos of a shuttle bus induced by continuous speedup abstract we study the effect of continuous speedup on the dynamical behavior of a single shuttle bus we present a nonlinear map model of a shuttle bus to take into account the continuous speedup when the shuttle bus is delayed the bus speeds up continuously to overcome the delay if the shuttle bus speeds up discontinuously the dynamics is described in terms of the piecewise linear map and the bus then exhibits the complex oscillatory motion with multiple periods for the continuous speedup it is found that the bus exhibits chaotic motion by the pitchfork bifurcation the chaotic motion depends on loading parameter maximum speed and speedup rate the dependence of the recurrence time one period and liapunov exponent on these parameters is calculated it is shown that the speedup rate has the significant effect on the bus motion

matching free trees maximal cliques and monotone game dynamics motivated by our recent work on rooted tree matching in this paper we provide a solution to the problem of matching two free i e unrooted trees by constructing an association graph whose maximal cliques are in one to one correspondence with maximal common subtrees we then solve the problem using simple payoff monotonic dynamics from evolutionary game theory we illustrate the power of the approach by matching articulated and deformed shapes described by shape axis trees experiments on hundreds of larger uniformly random trees are also presented the results are impressive despite the inherent inability of these simple dynamics to escape from local optima they always returned a globally optimal solution

a transform for multiscale image segmentation by integrated edge and region detection describes a transform to extract image regions at all geometric and photometric scales it is argued that linear approaches have the shortcoming that they require a priori models of region shape the proposed transform avoids this by letting the structure emerge bottom up from interactions among pixels the transform involves global computations on pairs of pixels followed by vector integration of the results an attraction force field is computed over the image in which pixels belonging to the same region are mutually attracted and the region is characterized by a convergent flow it is shown that the transform possesses properties that allow multiscale segmentation or extraction of original unblurred structure at all different geometric and photometric scales present this is in contrast with much previous work wherein multiscale structure is viewed as the smoothed structure in a multiscale signal decimation scale is an integral parameter of the force computation and the number and values of scale parameters associated with the image can be estimated automatically regions are detected at all a priori unknown scales resulting in automatic construction of a segmentation tree in which each pixel is annotated with descriptions of all the regions it belongs to transform properties are presented for piecewise constant images but hold for more general ones thus the proposed method is intended as a solution to the problem of multiscale integrated edge and region detection or low level image segmentation experimental results are given