generic bifurcation of hamiltonian systems with symmetry we study generic bifurcations of equilibria in one parameter hamiltonian systems with symmetry group on the generalized eigenvalues of the linearized system go through zero theorem 3 3 classifies expected actions of on the generalized eigenspace of this zero eigenvalue generic one degree of freedom symmetric systems is section 4 remarks concerning systems with more degrees of freedom are given in section 5

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

a comparison of algorithms for connected set openings and closings the implementation of morphological connected set operators for image filtering and pattern recognition is discussed two earlier algorithms based on priority queues and hierarchical queues respectively are compared to a more recent union find approach unlike the earlier algorithms which process regional extrema in the image sequentially the union find method allows simultaneous processing of extrema in the context of area openings closings and pattern spectra the union find algorithm outperforms the previous methods on almost all natural and synthetic images tested finally extensions to pattern spectra and the more general class of attribute operators are presented for all three algorithms and memory usages are compared

feature guided gaussian mixture model with semi supervised em and local geometric constraint for retinal image registration abstract retinal image registration which can be formulated by matching two sets of sparse features extracted from two observed retinal images is a crucial step in the diagnosis and treatment of various eye diseases existing methods suffer from missing true correspondences or do not fully consider local appearance information which causes difficulty in matching low quality retinal images due to insufficient reliable features in addition the relationships between retinal image pairs are usually modeled by linear transformation such as affine transformation which cannot generate accurate alignments in large viewpoint changes due to the nonplanar eyeball surface to address these issues a feature guided gaussian mixture model gmm is proposed for the non rigid registration of retinal images we formulate the problem as an estimation of a feature guided mixture of densities a gmm is fitted to one point set in which the centers of the gaussian densities characterized by spatial positions associated with local appearance descriptors are constrained to coincide with the other point set the problem is solved under a maximum likelihood framework and semi supervised expectation maximization is used to iteratively estimate the feature correspondence and spatial transformation which is initialized by a set of confidential feature matches obtained previously non rigid transformation is specified in a reproducing kernel hilbert space and a local geometric constraint is imposed to establish the transformation estimation for obtaining a meaningful solution a fast implementation based on sparse approximation is also provided and reduces the time complexity from cubic to quadratic moreover we use the edge map which can extract more reliable features as a uniform representation of retinal images experimental results on publicly available retinal images show that our approach is robust in different registration tasks and outperforms several competing approaches especially when data is severely degraded

robustness of shape descriptors to incomplete contour representations with inspiration from psychophysical researches of the human visual system we propose a novel aspect and a method for performance evaluation of contour based shape recognition algorithms regarding their robustness to incompleteness of contours we use complete contour representations of objects as a reference training set incomplete contour representations of the same objects are used as a test set the performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained we call this evaluation procedure the icr test we consider three types of contour incompleteness viz segment wise contour deletion occlusion and random pixel depletion as an illustration the robustness of two shape recognition algorithms to contour incompleteness is evaluated these algorithms use a shape context and a distance multiset as local shape descriptors qualitatively both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours the distance multiset method performs better than the shape context method in this test framework

modal matching for correspondence and recognition modal matching is a new method for establishing correspondences and computing canonical descriptions the method is based on the idea of describing objects in terms of generalized symmetries as defined by each object s eigenmodes the resulting modal description is used for object recognition and categorization where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects in general modes provide a global to local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison in contrast to previous techniques which required correspondence to be computed with an initial or prototype shape modal matching utilizes a new type of finite element formulation that allows for an object s eigenmodes to be computed directly from available image information this improved formulation provides greater generality and accuracy and is applicable to data of any dimensionality correspondence results with 2d contour and point feature data are shown and recognition experiments with 2d images of hand tools and airplanes are described

review of shape representation and description techniques more and more images have been generated in digital form around the world there is a growing interest in 1nding images in large collections or from remote databases in order to 1nd an image the image has to be described or represented by certain features shape is an important visual feature of an image searching for images using shape features has attracted much attention there are many shape representation anddescription techniques in the literature in this paper we classify and review these important techniques we examine implementation procedures for each technique and discuss its advantages and disadvantages some recent research results are also included and discussed in this paper finally we identify some promising techniques for image retrieval according to standard principles

information theoretic shape matching in this paper we describe two related algorithms that provide both rigid and non rigid point set registration with different computational complexity and accuracy the first algorithm utilizes a nonlinear similarity measure known as correntropy the measure combines second and high order moments in its decision statistic showing improvements especially in the presence of impulsive noise the algorithm assumes that the correspondence between the point sets is known which is determined with the surprise metric the second algorithm mitigates the need to establish a correspondence by representing the point sets as probability density functions pdf the registration problem is then treated as a distribution alignment the method utilizes the cauchy schwarz divergence to measure the similarity distance between the point sets and recover the spatial transformation function needed to register them both algorithms utilize information theoretic descriptors however correntropy works at the realizations level whereas cauchy schwarz divergence works at the pdf level this allows correntropy to be less computationally expensive and for correct correspondence more accurate the two algorithms are robust against noise and outliers and perform well under varying levels of distortion they outperform several well known and state of the art methods for point set registration

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

sacoseg object cosegmentation by shape conformability in this paper an object cosegmentation method based on shape conformability is proposed different from the previous object cosegmentation methods which are based on the region feature similarity of the common objects in image set our proposed sacoseg cosegmentation algorithm focuses on the shape consistency of the foreground objects in image set in the proposed method given an image set where the implied foreground objects may be varied in appearance but share similar shape structures the implied common shape pattern in the image set can be automatically mined and regarded as the shape prior of those unsatisfactorily segmented images the sacoseg algorithm mainly consists of four steps 1 the initial grabcut segmentation 2 the shape mapping by coherent point drift registration 3 the common shape pattern discovery by affinity propagation clustering and 4 the refinement by grabcut with common shape constraint to testify our proposed algorithm and establish a benchmark for future work we built the coshape data set to evaluate the shape based cosegmentation the experiments on coshape data set and the comparison with some related cosegmentation algorithms demonstrate the good performance of the proposed sacoseg algorithm

prioritized dct for compression and progressive transmission of images an approach is based on the block discrete cosine transform dct the novelty of this approach is that the transform coefficients of all image blocks are coded and transmitted in absolute magnitude order the resulting ordered by magnitude transmission is accomplished without sacrificing coding efficiency by using partition priority coding coding and transmission are adaptive to the characteristics of each individual image and therefore very efficient another advantage of this approach is its high progression effectiveness since the largest transform coefficients that capture the most important characteristics of images are coded and transmitted first this method is well suited for progressive image transmission further compression of the image data is achieved by multiple distribution entropy coding a technique based on arithmetic coding experiments show that the approach compares favorably with previously reported dct and subband image codecs

fast descriptors and correspondence propagation for robust global point cloud registration in this paper we present a robust global approach for point cloud registration from uniformly sampled points based on eigenvalues and normals computed from multiple scales we design fast descriptors to extract local structures of these points the eigenvalue based descriptor is effective at finding seed matches with low precision using nearest neighbor search generally recovering the transformation from matches with low precision is rather challenging therefore we introduce a mechanism named correspondence propagation to aggregate each seed match into a set of numerous matches with these sets of matches multiple transformations between point clouds are computed a quality function formulated from distance errors is used to identify the best transformation and fulfill a coarse alignment of the point clouds finally we refine the alignment result with the trimmed iterative closest point algorithm the proposed approach can be applied to register point clouds with significant or limited overlaps and small or large transformations more encouragingly it is rather efficient and very robust to noise a comparison to traditional descriptor based methods and other global algorithms demonstrates the fine performance of the proposed approach we also show its promising application in large scale reconstruction with the scans of two real scenes in addition the proposed approach can be used to register low resolution point clouds captured by kinect as well

semantic preserving distance metric learning and applications how do we accurately browse a large set of images or efficiently annotate the images from an image library image clustering methods are invaluable tools for applications such as content based image retrieval and image annotation to perform these tasks it is critical to have proper features to describe the visual and semantic content of images and to define an accurate distance metric to measure the dissimilarity between any two images however existing methods which adopt the features of color histograms edge direction histograms and shape context lack the ability to describe semantic content to solve this problem we propose a new approach that utilizes user provided pairwise constraints to describe the semantic relationship between two images a semantic preserving distance metric learning sp dml algorithm is developed to explore the complementary characteristics of the visual features and pairwise constraints in a unified feature space in this space the learned distance metric can be used to measure the dissimilarity between two images specifically the manifold structure adopted in sp dml is revealed by the image s visual features to integrate semantic contents in distance metric learning sp dml utilizes pairwise constraints to build semantic patches and align these patches to obtain the optimal distance metric for the new feature space experimental results in image clustering demonstrate that the performance of sp dml is appealing

perceptually relevant and piecewise linear matching of silhouettes in this paper a novel alignment method for silhouettes is proposed this method is based on the establishment of correspondences between landmarks on their boundaries and in turn on the establishment of correspondences of the boundary pieces in between these landmarks the method yields more correct correspondences than conventional methods that scale the arc length descriptions of silhouettes to align them the selection of landmarks is investigated as to the robustness of their localization and their perceptual relevance matching of silhouettes is then achieved by quantifying the dissimilarity of a pair of silhouette boundaries based on a novel dissimilarity metric the matching procedure is evaluated based on retrieval experiments and it is concluded that the precision of the results is higher than that obtained by conventional pointwise comparison methods

dynamics of the kirchhoff equations i coincident centers of gravity and buoyancy abstract we study the kirchhoff equations for a rigid body immersed in an incompressible irrotational inviscid fluid in the case that the centers of buoyancy and gravity coincide the resulting dynamical equations form a non canonical hamiltonian system with a six dimensional phase space which may be reduced to a four dimensional two degree of freedom canonical system using the two casimir invariants of motion restricting ourselves to ellipsoidal bodies we identify several completely integrable subcases in the general case we analyze existence linear and nonlinear stability and bifurcations of equilibria corresponding to steady translations and rotations including mixed modes involving simultaneous motion along two body axes some of which we show can be stable by perturbing from the axisymmetric integrable case we show that slightly asymmetric ellipsoids are typically non integrable and we investigate their dynamics with a view to using motions along homo and heteroclinic orbits to execute specific maneuvers in autonomous underwater vehicles

subband coding of images subband coding has become quite popular for the source encoding of speech this paper presents a simple yet efficient extension of this concept to the source coding of images we specify the constraints for a set of two dimensional quadrature mirror filters qmf s for a particular frequency domain partition and show that these constraints are satisfied by a separable combination of one dimensional qmf s bits are then optimally allocated among the subbands to minimize the mean squared error for dpcm coding of the subbands also an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask optimum quantization is employed with quantizers matched to the laplacian distribution subband coded images are presented along with their signal to noise ratios snr s the snr performance of the subband coder is compared to that of the adaptive discrete cosine transform dct vector quantization and differential vector quantization for bit rates of 0 67 1 0 and 2 0 bits per pixel for 256 256 monochrome images the adaptive subband coder has the best snr performance

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

constructing ensembles of classifiers by means of weighted instance selection in this paper we approach the problem of constructing ensembles of classifiers from the point of view of instance selection instance selection is aimed at obtaining a subset of the instances available for training capable of achieving at least the same performance as the whole training set in this way instance selection algorithms try to keep the performance of the classifiers while reducing the number of instances in the training set meanwhile boosting methods construct an ensemble of classifiers iteratively focusing each new member on the most difficult instances by means of a biased distribution of the training instances in this work we show how these two methodologies can be combined advantageously we can use instance selection algorithms for boosting using as objective to optimize the training error weighted by the biased distribution of the instances given by the boosting method our method can be considered as boosting by instance selection instance selection has mostly been developed and used for k nearest neighbor k nn classifiers so as a first step our methodology is suited to construct ensembles of k nn classifiers constructing ensembles of classifiers by means of instance selection has the important feature of reducing the space complexity of the final ensemble as only a subset of the instances is selected for each classifier however the methodology is not restricted to k nn classifier other classifiers such as decision trees and support vector machines svms may also benefit from a smaller training set as they produce simpler classifiers if an instance selection algorithm is performed before training in the experimental section we show that the proposed approach is able to produce better and simpler ensembles than random subspace method rsm method for k nn and standard ensemble methods for c4 5 and svms

sufficient conditions for a nondegenerate hopf bifurcation in a generalized lagrange poisson problem in this paper we provide sufficient conditions for the existence of a nondegenerate hamiltonian hopf bifurcation at the equilibria corresponding to the rotation around the vertical axis of a symmetric gyrostat with a fixed point under the effect of an axially symmetric potential

robust symbolic representation for shape recognition and retrieval a new method for shape recognition and retrieval is proposed here the suggested algorithm is based on several steps the algorithm analyzes the contour of pairs of shapes their contours are recovered and represented by a pair of n points obtained by linear interpolation given two points p i and q j from the two shapes the cost of their matching is evaluated by using the shape context and by using dynamic programming the best matching between the point sets is obtained dynamic programming not only recovers the best matching but also identifies occlusions i e points in the two shapes which cannot be properly matched given the correspondence between the two point sets the two contours are aligned using procrustes analysis after alignment each contour is transformed into a string of symbols and a modified version of edit distance is used to compute the similarity between strings of symbols finally recognition and retrieval are obtained by a simple nearest neighbor procedure the algorithm has been tested on a large set of shape databases kimia mpeg 7 natural silhouette database gesture database marine database swedish leaf database diatom database eth 80 3d object database providing performances for both in recognition and in retrieval superior to most of previously proposed approaches

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

an improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells in this paper we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells this problem is notoriously difficult because of the degree of overlap among cells the poor contrast of cell cytoplasm and the presence of mucus blood and inflammatory cells our methodology addresses these issues by utilizing a joint optimization of multiple level set functions where each function represents a cell within a clump that have both unary intracell and pairwise intercell constraints the unary constraints are based on contour length edge strength and cell shape while the pairwise constraint is computed based on the area of the overlapping regions in this way our methodology enables the analysis of nuclei and cytoplasm from both free lying and overlapping cells we provide a systematic evaluation of our methodology using a database of over 900 images generated by synthetically overlapping images of free lying cervical cells where the number of cells within a clump is varied from 2 to 10 and the overlap coefficient between pairs of cells from 0 1 to 0 5 this quantitative assessment demonstrates that our methodology can successfully segment clumps of up to 10 cells provided the overlap between pairs of cells is sim 0 5 we also evaluate our approach quantitatively and qualitatively on a set of 16 extended depth of field images where we are able to segment a total of 645 cells of which only sim 10 are free lying finally we demonstrate that our method of cell nuclei segmentation is competitive when compared with the current state of the art

view based recognition using an eigenspace approximation to the hausdorff measure view based recognition methods such as those using eigenspace techniques have been successful for a number of recognition tasks such approaches however are somewhat limited in their ability to recognize objects that are partly hidden from view or occur against cluttered backgrounds in order to address these limitations we have developed a view matching technique based on an eigenspace approximation to the generalized hausdorff measure this method achieves compact storage and fast indexing that are the main advantages of eigenspace view matching techniques while also being tolerant of partial occlusion and background clutter the method applies to binary feature maps such as intensity edges rather than directly to intensity images

flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes curve matching is one instance of the fundamental correspondence problem our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations a syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming we present extensive experiments where we apply the algorithm to silhouette matching in these experiments we examine partial occlusion viewpoint variation articulation and class matching where silhouettes of similar objects are matched based on the qualitative syntactic matching we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images we use this experiment to objectively evaluate our algorithm first we compare our results to those reported by others second we use the dissimilarity values in order to organize the image database into shape categories the veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation

building the component tree in quasi linear time the level sets of a map are the sets of points with level above a given threshold the connected components of the level sets thanks to the inclusion relation can be organized in a tree structure that is called the component tree this tree under several variations has been used in numerous applications various algorithms have been proposed in the literature for computing the component tree the fastest ones considering the worst case complexity have been proven to run in o nln n in this paper we propose a simple to implement quasi linear algorithm for computing the component tree on symmetric graphs based on tarjan s union find procedure we also propose an algorithm that computes the n most significant lobes of a map

moment polytopes for symplectic manifolds with monodromy abstract a natural way of generalising hamiltonian toric manifolds is to permit the presence of generic isolated singularities for the moment map for a class of such almost toric 4 manifolds which admits a hamiltonian s 1 action we show that one can associate a group of convex polygons that generalise the celebrated moment polytopes of atiyah guillemin sternberg as an application we derive a duistermaat heckman formula demonstrating a strong effect of the possible monodromy of the underlying integrable system

perceptually motivated image features using contours dong et al examined the ability of 51 computational feature sets to estimate human perceptual texture similarity however none performed well for this task while it is well known that the human visual system is extremely adept at exploiting longer range aperiodic and periodic contour characteristics in images none of the investigated feature sets exploit higher order statistics hos over larger image regions 19 times 19 pixels we therefore hypothesise that long range hos in the form of contour data are useful for perceptual texture similarity estimation we present the results of a psychophysical experiment that shows that contour data are more important than local image patches or global second order data to human observers for this task inspired by this finding we propose a set of perceptually motivated image features pmif that encode the long range hos computed from spatial and angular distributions of contour segments we use two perceptual texture similarity estimation tasks to compare pmif against the 51 feature sets referred to above and four commonly used contour representations this new feature set is also examined in the context of two additional tasks sketch based image retrieval and natural scene recognition the results show that the proposed feature set performs better or at least comparably to all the other feature sets we attribute this promising performance to the fact that the proposed feature set exploits both short range and long range hos

principal warps thin plate splines and the decomposition of deformations the decomposition of deformations by principal warps is demonstrated the method is extended to deal with curving edges between landmarks this formulation is related to other applications of splines current in computer vision how they might aid in the extraction of features for analysis comparison and diagnosis of biological and medical images in indicated

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

vector scalar classification for transform image coding this paper introduces vector scalar classification vsc for discrete cosine transform dct coding of images two main characteristics of vsc differentiate it from previously proposed classification methods first pattern classification is effectively performed in the energy domain of the dct subvectors using vector quantization second the subvectors instead of the dct vectors are mapped into a prescribed number of classes according to a pattern to class link established by scalar quantization simulation results demonstrate that the dct coding systems based on vsc are superior to the other proposed dct coding systems and are competitive compared to the best subband and wavelet coding systems reported in the literature

new algorithms for 2d and 3d point matching pose estimation and correspondence abstract a fundamental open problem in computer vision determining pose and correspondence between two sets of points in space is solved with a novel fast robust and easily implementable algorithm the technique works on noisy 2d or 3d point sets that may be of unequal sizes and may differ by non rigid transformations using a combination of optimization techniques such as deterministic annealing and the softassign which have recently emerged out of the recurrent neural network statistical physics framework analog objective functions describing the problems are minimized over thirty thousand experiments on randomly generated points sets with varying amounts of noise and missing and spurious points and on hand written character sets demonstrate the robustness of the algorithm

transform trellis coding of images at low bit rates a transform trellis coding scheme is used to encode images at rates below 1 b pel resulting in high snr values and very good subjective performance a discrete cosine transform dct is used to decorrelate the data samples and a trellis encoder is used to encode the transformed coefficients to overcome image nonstationarity a clustering algorithm is used to segment the transformed image into distinct regions and a separate trellis diagram is then constructed for each cluster to further improve the image quality particularly to reduce the blocking effect a scalar quantizer is used to quantize large magnitude coefficients of the error image in the transform domain the performance of the proposed scheme is found to be better than that of other recently reported schemes

non rigid point set registration by preserving global and local structures in previous work on point registration the input point sets are often represented using gaussian mixture models and the registration is then addressed through a probabilistic approach which aims to exploit global relationships on the point sets for non rigid shapes however the local structures among neighboring points are also strong and stable and thus helpful in recovering the point correspondence in this paper we formulate point registration as the estimation of a mixture of densities where local features such as shape context are used to assign the membership probabilities of the mixture model this enables us to preserve both global and local structures during matching the transformation between the two point sets is specified in a reproducing kernel hilbert space and a sparse approximation is adopted to achieve a fast implementation extensive experiments on both synthesized and real data show the robustness of our approach under various types of distortions such as deformation noise outliers rotation and occlusion it greatly outperforms the state of the art methods especially when the data is badly degraded

on the nonintegrability of the generalized van der waals hamiltonian system in this paper we aim to prove that except for the three known cases the uniparametric family of hamiltonian systems defined by the generalized van der waals potential is nonintegrable in the liouville arnold sense the proof is based on the theorem of morales and ramis about nonintegrability by differential galois theory

joint induction of shape features and tree classifiers we introduce a very large family of binary features for two dimensional shapes the salient ones for separating particular shapes are determined by inductive learning during the construction of classification trees there is a feature for every possible geometric arrangement of local topographic codes the arrangements express coarse constraints on relative angles and distances among the code locations and are nearly invariant to substantial affine and nonlinear deformations they are also partially ordered which makes it possible to narrow the search for informative ones at each node of the tree different trees correspond to different aspects of shape they are statistically and weakly dependent due to randomization and are aggregated in a simple way adapting the algorithm to a shape family is then fully automatic once training samples are provided as an illustration we classified handwritten digits from the nist database the error rate was 0 7 percent

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

convexity properties of the moment mapping ii be its associated momen t mapping see w for definitions the set x is the union of co adjoint orbits the main result of this paper is a description of the orbit structure of this set to describe this result we first note that the coadjoint orbits in q can be parametr ized as follows let t be a caf tan subalgebra of g and let b be a positive definite g invariant bilinear form on by means of b we get a g equivariant identification q let t be the subspace of q corresponding to t then every co adjoint orbit intersects i in a weyl group orbit so there is a one one correspondence between co adjoint orbits in q and weyl group orbits in t to get an even sharper parametrization let t be a weyl chamber in then every weyl group orbit intersects t in a single point so t is a modul i space for the co adjoint orbits two orbits in x are said to be of the same type if they are isomorphic as homogeneous g spaces since x is compact there are at most a finite number of different types of orbits see 1113 let h be a closed subgroup of t of codimension r and let cit be the set of all orbits in x of type g h

disconnected skeleton shape at its absolute scale we present a new skeletal representation along with a matching framework to address the deformable shape recognition problem the disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale our motivation is to rely on stable properties the shape instead of inaccurately measured secondary details the new representation does not suffer from the common instability problems of the traditional connected skeletons and the matching process gives quite successful results on a diverse database of 2d shapes an important difference of our approach from the conventional use of skeleton is that we replace the local coordinate frame with a global euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations as a result we can produce descriptions that are sensitive to any combination of changes in scale position orientation and articulation as well as invariant ones

semidirect products and reduction in mechanics this paper shows how to reduce a hamiltonian system on the cotangent bundle of a lie group to a hamiltonian system in the dual of the lie algebra of a semidirect product the procedure simplifies unifies and extends work of greene guillemin holm holmes kupershmidt marsden morrison ratiu sternberg and others the heavy top compressible fluids magnetohydrodynamics elasticity the maxwell vlasov equations and multifluid plasmas are presented as examples starting with lagrangian variables our method explains in a direct way why semidirect products occur so frequently in examples it also provides a framework for the systematic introduction of clebsch or canonical variables

fast connected component labeling labeling of connected components in a binary image is one of the most fundamental operations in pattern recognition labeling is required whenever a computer needs to recognize objects connected components in a binary image this paper presents a fast two scan algorithm for labeling of connected components in binary images we propose an efficient procedure for assigning provisional labels to object pixels and checking label equivalence our algorithm is very simple in principle easy to implement and suitable for hardware and parallel implementation we show the correctness of our algorithm analyze its complexity and compare it with other labeling algorithms experimental results demonstrated that our algorithm is superior to conventional labeling algorithms

structured prediction dual extragradient and bregman projections we present a simple and scalable algorithm for maximum margin estimation of structured output models including an important class of markov networks and combinatorial models we formulate the estimation problem as a convex concave saddle point problem that allows us to use simple projection methods based on the dual extragradient algorithm nesterov 2003 the projection step can be solved using dynamic programming or combinatorial algorithms for min cost convex flow depending on the structure of the problem we show that this approach provides a memory efficient alternative to formulations based on reductions to a quadratic program qp we analyze the convergence of the method and present experiments on two very different structured prediction tasks 3d image segmentation and word alignment illustrating the favorable scaling properties of our algorithm

regularization theory and neural networks architectures we had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units called regularization networks in particular s

fast and scalable approximate spectral graph matching for correspondence problems establishing consistent correspondences between two sets of features is a fundamental problem in computer vision this problem can be well formulated as graph matching in which nodes and edges represent feature points and pairwise relationships between feature points respectively spectral matching 19 is the state of the art eigenvector based method for graph matching the spectral matching algorithm has been used successfully for small data but its heavy memory requirement limited the maximum data sizes and contexts it can be used in this paper we propose fasm a fast and scalable approximate spectral matching algorithm the main ideas are twofold first we exploit the redundancy in the data generation process to approximate the affinity matrix with the linear combination of kronecker products between bases and index matrices the bases and index matrices are highly compressed representation of the approximated affinity matrix requiring much smaller memory than in previous works which store the whole affinity matrix second we compute the eigenvector of the approximated affinity matrix using the small bases and index matrices without explicitly materializing the approximated matrix experimental results show that our proposed method is up to 33x faster requiring up to 645x smaller memory than the exact algorithm with little or no loss of accuracy

spectra of shape contexts an application to symbol recognition the pixel level constraint plc histograms are known for robustness and invariance in symbol recognition but limited in o n 3 complexity this paper proves that matching two plc histograms can approximately be solved as matching the power spectra of the corresponding shape contexts as a result spectra of shape contexts ssc inherit robustness and invariance from plc while the computational cost can be reduced moreover a maximum clique based scheme is proposed for outlier rejection the theoretical and experimental validation justifies that ssc possesses the desired properties for symbol recognition that is robustness invariance and efficiency it outperforms plc in terms of robustness and time efficiency and shape context in terms of rotation invariance

an embedded system for real time facial expression recognition based on the extension theory this paper presents a novel facial expression recognition scheme based on extension theory the facial region is detected and segmented by using feature invariant approaches accurate positions of the lips are then extracted as the features of a face next based on the extension theory basic facial expressions are classified by evaluating the correlation functions among various lip types and positions of the corners of the mouth additionally the proposed algorithm is implemented using the xscale pxa270 embedded system in order to achieve real time recognition for various facial expressions experimental results demonstrate that the proposed scheme can recognize facial expressions precisely and efficiently

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

shape of elastic strings in euclidean space we construct a 1 parameter family of geodesic shape metrics on a space of closed parametric curves in euclidean space of any dimension the curves are modeled on homogeneous elastic strings whose elasticity properties are described in terms of their tension and rigidity coefficients as we change the elasticity properties we obtain the various elastic models the metrics are invariant under reparametrizations of the curves and induce metrics on shape space analysis of the geometry of the space of elastic strings and path spaces of elastic curves enables us to develop a computational model and algorithms for the estimation of geodesics and geodesic distances based on energy minimization we also investigate a curve registration procedure that is employed in the estimation of shape distances and can be used as a general method for matching the geometric features of a family of curves several examples of geodesics are given and experiments are carried out to demonstrate the discriminative quality of the elastic metrics

an optimal transform trellis code with applications to speech this paper presents a rate distortion derived transform trellis coding ttc scheme with applications to gaussian ar sources and speech data the optimal encoder consists of a karhunen loeve transform klt on the source output followed by a search on a trellis structured random code where the decoder is a time variant nonlinear filter the scheme is implementable and applicable to stationary gaussian sources with a bounded and continuous power spectrum and the squared error distortion measure the code construction is based on the power or eigenvalue spectrum of the source with no restriction on the coding rate the ttc scheme is first applied to encode a gaussian ar source often used to model speech simulations were conducted at several rates using an optimal klt and the suboptimal discrete cosine transform dct results demonstrate that the dct performs as well as the klt and both yield average distortions very close to the distortion rate function for speech data an adaptive version of the dct ttc scheme is applied to encode two speech sentences at several coding rates the adaptation is controlled by an estimate of the short term eigenvalue spectrum which is transmitted as side information to the receiver the proposed scheme is a very efficient speech waveform coder that provides reconstructed speech with very high signal to noise ratio values and very good perceptual quality at low bit rates

correlated logistic model with elastic net regularization for multilabel image classification in this paper we present correlated logistic corrlog model for multilabel image classification corrlog extends conventional logistic regression model into multilabel cases via explicitly modeling the pairwise correlation between labels in addition we propose to learn the model parameters of corrlog with elastic net regularization which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification corrlog can be efficiently learned though approximately by regularized maximum pseudo likelihood estimation and it enjoys a satisfying generalization bound that is independent of the number of labels corrlog performs competitively for multilabel image classification on benchmark data sets mulan scene mit outdoor scene pascal voc 2007 and pascal voc 2012 compared with the state of the art multilabel classification algorithms

a general approach to connected component labeling for arbitrary image representations an improved and general approach to connected component labeling of images is presented the algorithm presented in this paper processes images in predetermined order which means that the processing order depends only on the image representation scheme and not on specific properties of the image the algorithm handles a wide variety of image representation schemes rasters run lengths quadrees bintrees etc how to adapt the standard union find algorithm to permit reuse of temporary labels is shown this is done using a technique called age balancing in which when two labels are merged the older label becomes the father of the younger label this technique can be made to coexist with the more conventional rule of weight balancing in which the label with more descendants becomes the father of the label with fewer descendants various image scanning orders are examined and classified it is also shown that when the algorithm is specialized to a pixel array scanned in raster order the total processing time is linear in the number of pixels the linear time processing time follows from a special property of the union find algorithm which may be of independent interest this property states that under certain restrictions on the input union find runs in time linear in the number of find and union operations under these restrictions linear time performance can be achieved without resorting to the more complicated gabow tarjan algorithm for disjoint set union

lagrangian relative equilibria for a gyrostat in the three body problem bifurcations and stability in this paper we consider the non canonical hamiltonian dynamics of a gyrostat in the frame of the three body problem using geometric mechanic methods we study the approximate dynamics of the truncated legendre series representation of the potential of an arbitrary order working in the reduced problem we study the existence of relative equilibria that we refer to as lagrange type following the analogy with the standard techniques we provide necessary and sufficient conditions for the linear stability of lagrangian relative equilibria if the gyrostat morphology form is close to a sphere thus we generalize the classical results on equilibria of the three body problem and many results on them obtained by the classic approach for the case of rigid bodies

a trellis code construction and coding theorem for stationary gaussian sources corresp for stationary discrete time gaussian sources and the squared error distortion measure a trellis source code is constructed the encoder consists of a karhunen loeve transform on the source output followed by a search on a trellis structured code where the decoder is a time variant nonlinear filter the corresponding code theorem is proved using the random coding argument the proof technique follows that of viterbi and omura who proved the trellis coding theorem for memoryless sources the resultant coding scheme is implementable and applicable at any nonzero rate to a stationary gaussian source with a bounded and continuous power spectrum therefore for stationary sources it is more general than berger s tree coding scheme which is restricted to autoregressive gaussian sources in a region of high rate low distortion

application of affine invariant fourier descriptors to recognition of 3 d objects the method of fourier descriptors is extended to produce a set of normalized coefficients which are invariant under any affine transformation translation rotation scaling and shearing the method is based on a parameterized boundary description which is transformed to the fourier domain and normalized there to eliminate dependencies on the affine transformation and on the starting point invariance to affine transforms allows considerable robustness when applied to images of objects which rotate in all three dimensions as is demonstrated by processing silhouettes of aircraft maneuvering in three space

snapshots a novel local surface descriptor and matching algorithm for robust 3d surface alignment in this paper a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3d object the descriptor is based on taking snapshots of the surface over each point using a virtual camera oriented perpendicularly to the surface this representation has the advantage of imposing minimal loss of information be robust to self occlusions and also be very efficient to compute then we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap

distributions of the two dimensional dct coefficients for images for a two dimensional discrete cosine transform dct image coding system there have been different assumptions concerning the distributions of the transform coefficients this paper presents results of distribution tests that indicate that for many images the statistics of the coefficients are best approximated by a gaussian distribution for the dc coefficient and a laplacian distribution for the other coefficients furthermore from a simulation of the dct coding system it is shown that the assumption that the coefficients are laplacian yields a higher actual output signal to noise ratio and a much better agreement between theory and simulation than the gaussian assumption

one shot detection with laplacian object and fast matrix cosine similarity one shot generic object detection involves searching for a single query object in a larger target image relevant approaches have benefited from features that typically model the local similarity patterns in this paper we combine local similarity encoded by local descriptors with a global context i e a graph structure of pairwise affinities among the local descriptors embedding the query descriptors into a low dimensional but discriminatory subspace unlike principal components that preserve global structure of feature space we actually seek a linear approximation to the laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection obviating the computationally expensive sliding window search we leverage the power of fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses for pose estimation within a reasonably short time our approach to one shot detection is training free and experiments on the standard data sets confirm the efficacy of our model besides low computation cost of the proposed codebook free object detector facilitates rather straightforward query detection in large data sets including movie videos

relative equilibria and bifurcations in the generalized van der waals 4d oscillator abstract a uniparametric 4 dof family of perturbed hamiltonian oscillators in 1 1 1 1 resonance is studied as a generalization for several models for perturbed keplerian systems normalization by lie transforms only first order is considered here as well as geometric reduction related to the invariants associated to the symmetries is used based on the previous work of the authors a description is given for the lower dimensional relative equilibria in such normalized systems for which we introduce in this context the new concept of moment polytope in addition bifurcations of relative equilibria corresponding to 3d tori are studied in some particular cases where we focus on hamiltonian hopf bifurcations and bifurcations in the 3d van der waals and zeeman systems

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

an efficient earth mover s distance algorithm for robust histogram comparison we propose emd l1 a fast and exact algorithm for computing the earth mover s distance emd between a pair of histograms the efficiency of the new algorithm enables its application to problems that were previously prohibitive due to high time complexities the proposed emd l1 significantly simplifies the original linear programming formulation of emd exploiting the l1 metric structure the number of unknown variables in emd l1 is reduced to o n from o n2 of the original emd for a histogram with n bins in addition the number of constraints is reduced by half and the objective function of the linear program is simplified formally without any approximation we prove that the emd l1 formulation is equivalent to the original emd with a l1 ground distance to perform the emd l1 computation we propose an efficient tree based algorithm tree emd tree emd exploits the fact that a basic feasible solution of the simplex algorithm based solver forms a spanning tree when we interpret emd l1 as a network flow optimization problem we empirically show that this new algorithm has an average time complexity of o n2 which significantly improves the best reported supercubic complexity of the original emd the accuracy of the proposed methods is evaluated by experiments for two computation intensive problems shape recognition and interest point matching using multidimensional histogram based local features for shape recognition emd l1 is applied to compare shape contexts on the widely tested mpeg7 shape data set as well as an articulated shape data set for interest point matching sift shape context and spin image are tested on both synthetic and real image pairs with large geometrical deformation illumination change and heavy intensity noise the results demonstrate that our emd l1 based solutions outperform previously reported state of the art features and distance measures in solving the two tasks

joint image clustering and labeling by matrix factorization we propose a novel algorithm to cluster and annotate a set of input images jointly where the images are clustered into several discriminative groups and each group is identified with representative labels automatically for these purposes each input image is first represented by a distribution of candidate labels based on its similarity to images in a labeled reference image database a set of these label based representations are then refined collectively through a non negative matrix factorization with sparsity and orthogonality constraints the refined representations are employed to cluster and annotate the input images jointly the proposed approach demonstrates performance improvements in image clustering over existing techniques and illustrates competitive image labeling accuracy in both quantitative and qualitative evaluation in addition we extend our joint clustering and labeling framework to solving the weakly supervised image classification problem and obtain promising results

gibbs random fields cooccurrences and texture modeling gibbs random field grf models and features from cooccurrence matrices are typically considered as separate but useful tools for texture discrimination the authors show an explicit relationship between cooccurrences and a large class of grf s this result comes from a new framework based on a set theoretic concept called the aura set and on measures of this set aura measures this framework is also shown to be useful for relating different texture analysis tools the authors show how the aura set can be constructed with morphological dilation how its measure yields cooccurrences and how it can be applied to characterizing the behavior of the gibbs model for texture in particular they show how the aura measure generalizes to any number of gray levels and neighborhood order some properties previously known for just the binary nearest neighbor grf finally the authors illustrate how these properties can guide one s intuition about the types of grf patterns which are most likely to form

the stability of the lagrange triangular point and a theorem of arnold deprit and deprit bartholome 5 computed the normal form for the hamiltonian of the planar restricted three body problem at the lagrange equilateral triangle equilibrium point up to terms of order 4 this normalization was carried out for all mass ratios p smaller than routh s critical mass ratio p1 i 1 j 9 except for two values p2 and pi at p2 and u the ratio of the linearized frequencies is 1 2 and 1 3 respectively and so low order resonance terms appear this normalization was carried out in order to apply the kam theory to prove the stability of this equilibrium point from the coefficients of the normal form they computed a quantity d defined below which when non zero establishes the stability of the equilibrium point they found that d 0 for 0 cp p u p2 p and p the value u does not correspond to resonance but is simply a point where the quantity d4 changes sign in this paper we shall establish the full stability of the lagrange equilibrium point in the planar restricted three body problem even in the case when u p we do this by computing by machine the normal form up to terms of order 6 and then applying a theorem of arnold 3 which establishes the stability of the equilibrium even in degenerate cases russman lo announced a theorem which implies that the equilibrium is isoenergetic stable when p p by isoenergetic stable he means that the system is stabie only on the energy surface defined by the energy at the equilibrium itself unfortunately there is not even an indication of the proof arnold announced his theorem in a doklady note with only a sketch of a proof the sketch indicates that his proof requires completely redoing all

generative models for similarity based classification a maximum entropy approach to generative similarity based classifiers model is proposed first a descriptive set of similarity statistics is assumed to be sufficient for classification then the class conditional distributions of these descriptive statistics are estimated as the maximum entropy distributions subject to empirical moment constraints the resulting exponential class conditional distributions are used in a maximum a posteriori decision rule forming the similarity discriminant analysis sda classifier simulated and real data experiments compare performance to the k nearest neighbor classifier the nearest centroid classifier and the potential support vector machine psvm

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

a visual shape descriptor using sectors and shape context of contour lines this paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching the proposed descriptor consists of two component feature vectors first the local region is separated into sectors and their gradient magnitude and orientation values are extracted a feature vector is then constructed from these values second local shape features are obtained using the shape context of contour lines another feature vector is then constructed from these contour lines the proposed approach calculates the local shape feature without needing to consider the edges this can overcome the difficulty associated with textured images and images with ill defined edges the combination of two component feature vectors makes the proposed descriptor more robust to image scale changes illumination variations and noise the proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy 14 525 better than sift 21 better than pca sift 11 86 better than gloh and 25 66 better than the shape context

real time object recognition using relational dependency based on graphical model this paper proposes a real time object recognition using the relational dependency among the objects that is represented by the graphical model when we recognize the objects it is effective to use the relational dependency in which several different objects co exist each other the relational dependency has been modeled by the transition matrix in the graphical model the transition matrix precisely represents the conditional probability of object s existence at time t given the existence of others at time t 1 we use a very fast cascaded adaboost detector in order to detect all object candidates in the image then the existence probability of the object from a given object candidate is estimated by a logistic regression using the softmax function the estimated existence probability is updated by the trained transition matrix to reflect the relational dependency of the objects the object s existence is determined by the threshold level experiment results validate that the proposed method is a very fast and effective way of recognizing the objects in terms of high recognition rate and low false alarm rate

nonlinear stability of the equilibria in a double bar rotating system we study the nonlinear stability of the equilibria corresponding to the motion of a particle orbiting around a two finite orthogonal straight segment the potential is a logarithmic function and may be considered as an approximation to the one generated by irregular celestial bodies using arnold s theorem for non definite quadratic forms we determine the nonlinear stability of the equilibria for all values of the parameter of the problem moreover the resonant cases are determined and the stability is investigated

morphometric tools for landmark data geometry and biology 1 introduction 2 preliminaries 3 landmarks 4 distance measures 5 shape coordinates 6 principal axes of shape change for triangles 7 features of shape comparison 8 retrospect and prospect

a novel density based fuzzy clustering algorithm for low dimensional feature space abstract in this paper we propose a novel density based fuzzy clustering algorithm based on active learning method alm which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern like images rather than numerical quantities the proposed clustering algorithm fuzzy unsupervised active learning method fualm is performed in two main phases first each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper as a result of this process densely connected ink patterns are formed that represent clusters in the second phase a fuzzifying process is applied in order to summarize the effects of all members of each cluster finding arbitrary shaped clusters noise robustness and proposing fuzzy clusters are some of the advantages of our proposed clustering algorithm the algorithm is described in full details and its performance is evaluated and compared with well known clustering algorithms on synthetic and real world datasets

using the particle filter approach to building partial correspondences between shapes constructing correspondences between points characterizing one shape with those characterizing another is crucial to understanding what the two shapes have in common these correspondences are the basis for most alignment processes and shape similarity measures in this paper we use particle filters to establish perceptually correct correspondences between point sets characterizing shapes local shape feature descriptors are used to establish the probability that a point on one shape corresponds to a point on the other shape global correspondence structures are calculated using additional constraints on domain knowledge domain knowledge is characterized by prior distributions which serve to characterize hypotheses about the global relationships between shapes these hypotheses are formulated online this means global constraints are learnt during the particle filtering process which makes the approach especially interesting for applications where global constraints are hard to define a priori as an example for such a case experiments demonstrate the performance of our approach on partial shape matching

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

unified detection of skewed rotation reflection and translation symmetries from affine invariant contour features symmetry detection is significant for object detection and recognition since symmetries are salient cues for distinguishing geometrical structures from cluttered backgrounds this paper proposes a unified framework to detect rotation reflection and translation symmetries simultaneously on unsegmented real world images the detection is performed based on affine invariant contour features and is applicable under skewed imaging with distortions contours on a natural image are first matched to each other to find affine invariant matching pairs which are then classified into three groups using a sign change criterion corresponding to the three types of symmetries the three groups are used to vote for the corresponding symmetries the voting for rotation utilizes a scheme of short line segments the voting for reflection is based on a parameterization of axis equation and the voting for translation uses a cascade like approach experimental results of real world images are provided with quantitative evaluations validating that the proposed framework achieves desired performance

predictive vector quantization of images the purpose of this paper is to present new image coding schemes based on a predictive vector quantization pvq approach the predictive part of the encoder is used to partially remove redundancy and the vq part further removes the residual redundancy and selects good quantization levels for the global waveform two implementations of this coding approach have been devised namely sliding block pvq and block tree pvq simulations on real images show significant improvement over the conventional dpcm and tree codes using these new techniques the strong robustness property of these coding schemes is also experimentally demonstrated

worst case analysis of set union algorithms this paper analyzes the asymptotic worst case running time of a number of variants of the well known method of path compression for maintaining a collection of disjoint sets under union we show that two one pass methods proposed by van leeuwen and van der weide are asymptotically optimal whereas several other methods including one proposed by rein and advocated by dijkstra are slower than the best methods

equilibria stability and hamiltonian hopf bifurcation of a gyrostat in an incompressible ideal fluid abstract for a gyrostat in a incompressible ideal fluid by writing kirchhoff s equations as a lie poisson system and using a non canonical hamiltonian formulation we provide the expressions of the equilibria when the gyrostatic momentum is constant with the form l 0 0 l and present necessary and sufficient conditions for the stability of some of them via the energy casimir method and the study of the linearized equations of the motion finally using a recent geometric method introduced by hanssmann and van der meer we give a sufficient condition for the existence of a non degenerate hamiltonian hopf bifurcation at those equilibria when the gyrostat is symmetric

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

complex autoregressive model for shape recognition a complex autoregressive model for invariant feature extraction to recognize arbitrary shapes on a plane is presented a fast algorithm to calculate complex autoregressive coefficients and complex parcor coefficients of the model is also shown the coefficients are invariant to rotation around the origin and to choice of the starting point in tracing a boundary it is possible to make them invariant to scale and translation experimental results that the complicated shapes like nonconvex boundaries can be recognized in high accuracy even in the low order model it is seen that the complex parcor coefficients tend to provide more accurate classification than the complex ar coefficients

sledge sequential labeling of image edges for boundary detection our goal is to detect boundaries of objects or surfaces occurring in an arbitrary image we present a new approach that discovers boundaries by sequential labeling of a given set of image edges a visited edge is labeled as on or off a boundary based on the edge s photometric and geometric properties and evidence of its perceptual grouping with already identified boundaries we use both local gestalt cues e g proximity and good continuation and the global helmholtz principle of non accidental grouping a new formulation of the helmholtz principle is specified as the entropy of a layout of image edges for boundary discovery we formulate a new policy iteration algorithm called sledge training of sledge is iterative in each training image sledge labels a sequence of edges which induces loss with respect to the ground truth these sequences are then used as training examples for learning sledge in the next iteration such that the total loss is minimized for extracting image edges that are input to sledge we use our new low level detector it finds salient pixel sequences that separate distinct textures within the image on the benchmark berkeley segmentation datasets 300 and 500 our approach proves robust and effective we outperform the state of the art both in recall and precision for different input sets of image edges