noncommutative fiber products and lattice models abstract we establish a connection between the representation theory of certain noncommutative singular varieties and two dimensional lattice models specifically we consider noncommutative biparametric deformations of the fiber product of two kleinian singularities of type a special examples are closely related to the schrodinger algebra s 1 the affine lie algebra a 1 1 and a finite w algebra associated to sl 4 the algebras depend on two scalars and two polynomials that must satisfy the mazorchuk turowska equation mte which we re interpret as a quantization of the ice rule local current conservation in statistical mechanics solutions to the mte previously classified by the author and d rosso can accordingly be expressed in terms of multisets of higher spin vertex configurations on a twisted cylinder we first reduce the problem of describing the category of weight modules to the case of a single configuration l secondly we classify all simple weight modules over the corresponding algebras a l in terms of the connected components of the cylinder minus l lastly we prove that a l are crystalline graded rings as defined by nauwelaerts and van oystaeyen and describe the center of a l explicitly in terms of l along the way we prove several new results about twisted generalized weyl algebras and their simple weight modules

partial linear gaussian models for tracking in image sequences using sequential monte carlo methods the recent development of sequential monte carlo methods also called particle filters has enabled the definition of efficient algorithms for tracking applications in image sequences the efficiency of these approaches depends on the quality of the state space exploration which may be inefficient due to a crude choice of the function used to sample in the associated probability space a careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear gaussian models such models are characterized by a non linear dynamic equation a linear measurement equation and additive gaussian noises they allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter and enable building a relevant approximation of a validation gate despite of these potential advantages partial linear gaussian models have not been investigated the aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions ambiguities due to cluttered backgrounds and large state space three instances of these models are proposed after a theoretical analysis their significance is demonstrated by their performance for tracking points and planar objects in challenging real world image sequences

locally finite simple weight modules over twisted generalized weyl algebras abstract we present methods and explicit formulas for describing simple weight modules over twisted generalized weyl algebras when a certain commutative subalgebra is finitely generated over an algebraically closed field we obtain a classification of a class of locally finite simple weight modules as those induced from simple modules over a subalgebra isomorphic to a tensor product of noncommutative tori as an application we describe simple weight modules over the quantized weyl algebra

approximate bayesian multibody tracking visual tracking of multiple targets is a challenging problem especially when efficiency is an issue occlusions if not properly handled are a major source of failure solutions supporting principled occlusion reasoning have been proposed but are yet unpractical for online applications this paper presents a new solution which effectively manages the trade off between reliable modeling and computational efficiency the hybrid joint separable hjs filter is derived from a joint bayesian formulation of the problem and shown to be efficient while optimal in terms of compact belief representation computational efficiency is achieved by employing a markov random field approximation to joint dynamics and an incremental algorithm for posterior update with an appearance likelihood that implements a physically based model of the occlusion process a particle filter implementation is proposed which achieves accurate tracking during partial occlusions while in cases of complete occlusion tracking hypotheses are bound to estimated occlusion volumes experiments show that the proposed algorithm is efficient robust and able to resolve long term occlusions between targets with identical appearance

dependent multiple cue integration for robust tracking we propose a new technique for fusing multiple cues to robustly segment an object from its background in video sequences that suffer from abrupt changes of both illumination and position of the target robustness is achieved by the integration of appearance and geometric object features and by their estimation using bayesian filters such as kalman or particle filters in particular each filter estimates the state of a specific object feature conditionally dependent on another feature estimated by a distinct filter this dependence provides improved target representations permitting us to segment it out from the background even in nonstationary sequences considering that the procedure of the bayesian filters may be described by a hypotheses generation hypotheses correction strategy the major novelty of our methodology compared to previous approaches is that the mutual dependence between filters is considered during the feature observation that is into the hypotheses correction stage instead of considering it when generating the hypotheses this proves to be much more effective in terms of accuracy and reliability the proposed method is analytically justified and applied to develop a robust tracking system that adapts online and simultaneously the color space where the image points are represented the color distributions the contour of the object and its bounding box results with synthetic data and real video sequences demonstrate the robustness and versatility of our method

maximal graded orders over crystalline graded rings abstract crystalline graded rings are generalizations of certain classes of rings like generalized twisted group rings generalized weyl algebras and generalized skew crossed products under certain conditions in particular the group is finite it is proven that the global dimension of a crystalline graded ring coincides with the global dimension of its base ring when in addition the base ring is a commutative dedekind domain two constructions are given for producing maximal graded orders on the way a new concept appears so called spectrally twisted group some general properties of it are studied at the end of the paper several examples are considered

c ondensation conditional density propagation forvisual tracking the problem of tracking curves in dense visual clutter is challenging kalman filtering is inadequate because it is based on gaussian densities which being unimo dal cannot represent simultaneous alternative hypotheses the condensation algorithm uses factored sampling previously applied to the interpretation of static images in which the probability distribution of possible interpretations is represented by a randomly generated set condensation uses learned dynamical models together with visual observations to propagate the random set over time the result is highly robust tracking of agile motion notwithstanding the use of stochastic methods the algorithm runs in near real time

incremental tensor subspace learning and its applications to foreground segmentation and tracking appearance modeling is very important for background modeling and object tracking subspace learning based algorithms have been used to model the appearances of objects or scenes current vector subspace based algorithms cannot effectively represent spatial correlations between pixel values current tensor subspace based algorithms construct an offline representation of image ensembles and current online tensor subspace learning algorithms cannot be applied to background modeling and object tracking in this paper we propose an online tensor subspace learning algorithm which models appearance changes by incrementally learning a tensor subspace representation through adaptively updating the sample mean and an eigenbasis for each unfolding matrix of the tensor the proposed incremental tensor subspace learning algorithm is applied to foreground segmentation and object tracking for grayscale and color image sequences the new background models capture the intrinsic spatiotemporal characteristics of scenes the new tracking algorithm captures the appearance characteristics of an object during tracking and uses a particle filter to estimate the optimal object state experimental evaluations against state of the art algorithms demonstrate the promise and effectiveness of the proposed incremental tensor subspace learning algorithm and its applications to foreground segmentation and object tracking

snakes active contour models a snake is an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges snakes are active contour models they lock onto nearby edges localizing them accurately scale space continuation can be used to enlarge the capture region surrounding a feature snakes provide a unified account of a number of visual problems including detection of edges lines and subjective contours motion tracking and stereo matching we have used snakes successfully for interactive interpretation in which user imposed constraint forces guide the snake near features of interest

tracking in low frame rate video a cascade particle filter with discriminative observers of different life spans tracking object in low frame rate video or with abrupt motion poses two main difficulties which most conventional tracking methods can hardly handle 1 poor motion continuity and increased search space 2 fast appearance variation of target and more background clutter due to increased search space in this paper we address the problem from a view which integrates conventional tracking and detection and present a temporal probabilistic combination of discriminative observers of different lifespans each observer is learned from different ranges of samples with different subsets of features to achieve varying level of discriminative power at varying cost an efficient fusion and temporal inference is then done by a cascade particle filter which consists of multiple stages of importance sampling experiments show significantly improved accuracy of the proposed approach in comparison with existing tracking methods under the condition of low frame rate data and abrupt motion of both target and camera

isomorphism problems of noncommutative deformations of type d kleinian singularities we construct all possible noncommutative deformations of a kleinian singularity c 2 gamma of type d n in terms of generators and relations and solve the isomorphism problem for the associative algebras thus constructed we prove that in our parametrization all isomorphisms arise from the action of the normalizer n sl 2 gamma on c gamma we deduce that the moduli space of isomorphism classes of noncommutative deformations in type d n is isomorphic to a vector space of dimension n

approximate smoothing and parameter estimation in high dimensional state space models we present approximate algorithms for performing smoothing in a class of high dimensional state space models via sequential monte carlo methods particle filters in high dimensions a prohibitively large number of monte carlo samples particles growing exponentially in the dimension of the state space are usually required to obtain a useful smoother employing blocking approximations we exploit the spatial ergodicity properties of the model to circumvent this curse of dimensionality we thus obtain approximate smoothers that can be computed recursively in time and parallel in space first we show that the bias of our blocked smoother is bounded uniformly in the time horizon and in the model dimension we then approximate the blocked smoother with particles and derive the asymptotic variance of idealized versions of our blocked particle smoother to show that variance is no longer adversely effected by the dimension of the model finally we employ our method to successfully perform maximum likelihood estimation via stochastic gradient ascent and stochastic expectation maximization algorithms in a 100 dimensional state space model

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

a probabilistic exclusion principle for tracking multiple objects tracking multiple targets is a challenging problem especially when the targets are identical in the sense that the same model is used to describe each target in this case simply instantiating several independent 1 body trackers is not an adequate solution because the independent trackers tend to coalesce onto the best fitting target this paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle exclusion arises naturally from a systematic derivation of the observation density without relying on heuristics another important contribution of the paper is the presentation of partitioned sampling a new sampling method for multiple object tracking partitioned sampling avoids the high computational load associated with fully coupled trackers while retaining the desirable properties of coupling

tracking deforming objects using particle filtering for geometric active contours tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time tracking algorithms using kalman filters or particle filters have been proposed for finite dimensional representations of shape but these are dependent on the chosen parametrization and cannot handle changes in curve topology geometric active contours provide a framework which is parametrization independent and allow for changes in topology in the present work we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects to the best of our knowledge this is the first attempt to implement an approximate particle filtering algorithm for tracking on a theoretically infinite dimensional state space

three dimensional flops and noncommutative rings limburgs univ ctr dept math b 3590 diepenbeek belgium van den bergh m limburgs univ ctr dept math univ campus b 3590 diepenbeek belgium vdbergh luc ac be

sequential imputations and bayesian missing data problems abstract for missing data problems tanner and wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors their method of constructing the complete data sets is closely related to the gibbs sampler both required iterations and similar to the em algorithm convergence can be slow we introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights in many applications this new procedure works very well without the need for iterations sensitivity analysis influence analysis and updating with new data can be performed cheaply bayesian prediction and model selection can also be incorporated examples taken from a wide range of applications are used for illustration

a generalized statistical burgers equation to predict the evolution of the power spectral density of high intensity noise in atmosphere the present work is a theoretical numerical investigation of the combined effect of nonlinearity geometrical spreading and atmospheric absorption on the evolution of the power spectral density of a noise field when only the power spectral density is known at source not the signal itself this is often the case in aircraft noise measurements the method presented here is based on and extends previous work p menounou and d t blackstock j acoust soc am 115 567 580 2004 where a recursion equation statistical burgers equation sbe describing the evolution of the joint moments of the noise source was derived the sbe is restricted to plane waves thermoviscous fluids and short propagation distances preshock region in the present work the sbe is extended to include the effects of geometrical spreading and arbitrary absorption in order to be applicable to propagation of high intensity noise through atmosphere a new equation is derived and termed generalized sbe and a method for its numerical implementation is presented results are in good agreement with time domain calculations for propagation in atmosphere of i sinusoidal signals benchmark case and ii gaussian processes with known power spectral densities at source

kernel based object tracking a new approach toward target representation and localization the central component in visual tracking of nonrigid objects is proposed the feature histogram based target representations are regularized by spatial masking with an isotropic kernel the masking induces spatially smooth similarity functions suitable for gradient based optimization hence the target localization problem can be formulated using the basin of attraction of the local maxima we employ a metric derived from the bhattacharyya coefficient as similarity measure and use the mean shift procedure to perform the optimization in the presented tracking examples the new method successfully coped with camera motion partial occlusions clutter and target scale variations integration with motion filters and data association techniques is also discussed we describe only a few of the potential applications exploitation of background information kalman tracking using motion models and face tracking

a new method to predict the evolution of the power spectral density for a finite amplitude sound wave a method to predict the effect of nonlinearity on the power spectral density of a plane wave traveling in a thermoviscous fluid is presented as opposed to time domain methods the method presented here is based directly on the power spectral density of the signal not the signal itself the burgers equation is employed for the mathematical description of the combined effects of nonlinearity and dissipation the burgers equation is transformed into an infinite set of linear equations that describe the evolution of the joint moments of the signal a method for solving this system of equations is presented only a finite number of equations is appropriately selected and solved by numerical means for the method to be applied all appropriate joint moments must be known at the source if the source condition has gaussian characteristics it is a gaussian noise signal or a gaussian stationary and ergodic stochastic process then all the joint moments can be computed from the power spectral density of the signal at the source numerical results from the presented method are shown to be in good agreement with known analytical solutions in the preshock region for two benchmark cases i sinusoidal source signal and ii a gaussian stochastic process as the source condition

noncommutative deformations of type a kleinian singularities abstract the type a kleinian singularities are the surfaces of the form c 2 g where g is a finite cyclic subgroup of sl2 c the standard noncommutative analog is the fixed ring of the weyl algebra a1 c under the induced action of g these fixed rings are however only part of a large family of algebras whose associated graded ring is the coordinate ring of such a singularity the structure of these algebras is studied with reference to the structure of the associated singularity and to the structure of primitive factor rings of u s l 2 c

a numerical method for general finite amplitude wave propagation in two dimensions and its application to spark pulses the general equations of finite amplitude acoustics including classical absorption effects and second order nonlinear effects are written in a form suitable for two dimensional numerical solution a finite difference scheme then is applied to numerically solve the equations to demonstrate the method examples are given of spherical free field propagation normal plane reflection from a hard surface and oblique spherical reflection from a hard surface for spark pulses this method has an advantage over burgers equation methods one way wave equation methods and pestorius type algorithms in that it can predict the interaction of multiple finite amplitude acoustic waves at arbitrary propagation angles

multi target tracking by learning local to global trajectory models the multi target tracking problem is challenging when there exist occlusions tracking failures of the detector and severe interferences between detections in this paper we propose a novel detection based tracking method that links detections into tracklets and further forms long trajectories unlike many previous hierarchical frameworks which split the data association into two separate optimization problems linking detections locally and linking tracklets globally we introduce a unified algorithm that can automatically relearn the trajectory models from the local and global information for finding the joint optimal assignment in each temporal window the trajectory models are initialized by the local information to link those easy to connect detections into a set of tracklets then the trajectory models are updated by the reliable tracklets and reused to link separated tracklets into long trajectories we iteratively update the trajectory models by more information from more frames until the result converges the iterative process gradually improves the accuracy of the trajectory models which in turn improves the target id inferences for all detections by the mrf model experiment results revealed that our proposed method achieved state of the art multi target tracking performance highlightsa unified framework to online learn local to global trajectory models is proposed the iterative algorithm can alternately update the trajectory models we solve inferences of target ids for all the detections by using the mrf model our method has low complexity compared with most state of the art methods

multiparameter twisted weyl algebras abstract we introduce a new family of twisted generalized weyl algebras called multiparameter twisted weyl algebras for which we parametrize all simple quotients of a certain kind both jordan s simple localization of the multiparameter quantized weyl algebra and hayashi s q analog of the weyl algebra are special cases of this construction we classify all simple weight modules over any multiparameter twisted weyl algebra extending results by benkart and ondrus we also describe all whittaker pairs up to isomorphism over a class of twisted generalized weyl algebras which includes the multiparameter twisted weyl algebras

conditional mean estimation via jump diffusion processes in multiple target tracking recognition a new algorithm is presented for generating the conditional mean estimates of functions of target positions orientations and type in recognition and tracking of an unknown number of targets and target types taking a bayesian approach a posterior measure is defined on the tracking target parameter space by combining a narrowband sensor array manifold model with a high resolution imaging model and a prior based on airplane dynamics the newtonian force equations governing rigid body dynamics are utilized to form the prior density on airplane motion the conditional mean estimates are generated using a random sampling algorithm based on jump diffusion processes for empirically generating mmse estimates of functions of these random target positions orientations and type under the posterior measure results are presented on target tracking and identification from an implementation of the algorithm on a networked silicon graphics workstation and decmpp maspar parallel machine

tracking by affine kernel transformations using color and boundary cues kernel based trackers aggregate image features within the support of a kernel a mask regardless of their spatial structure these trackers spatially fit the kernel usually in location and in scale such that a function of the aggregate is optimized we propose a kernel based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary the tracker estimates the best affinity of a spatially aligned pair of kernels one of which is color related and the other of which is object boundary related in a sense this work extends previous kernel based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled these two extensions make for more precise target localization a more accurately localized target also facilitates safer updating of its reference color model further enhancing the tracker s robustness the improved tracking is demonstrated for several challenging image sequences

filtering via simulation auxiliary particle filters this article analyses the recently suggested particle approach to filtering time series we suggest that the algorithm is not robust to outliers for two reasons the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution here we tackle the first of these problems

classification of twisted generalized weyl algebras over polynomial rings abstract let r be a polynomial ring in m variables over a field of characteristic zero we classify all rank n twisted generalized weyl algebras over r up to z n graded isomorphisms in terms of higher spin 6 vertex configurations examples of such algebras include infinite dimensional primitive quotients of u g where g gl n sl n or sp 2 n algebras related to u sl 2 and a finite w algebra associated to sl 4 to accomplish this classification we first show that the problem is equivalent to classifying solutions to the binary and ternary consistency equations secondly we show that the latter problem can be reduced to the case n 2 which can be solved using methods from previous work by the authors 17 15 as a consequence we obtain the surprising fact that in the setting of the present paper the ternary consistency relation follows from the binary consistency relation

non linear propagation of broadband noise signals measurements of aircraft flyover noise have strongly suggested that non linear propagation effects can significantly modify the noise spectrum the data show that over ranges of 500 to 1000 m the attenuation in the range 5 to 10 khz may fall considerably short of that predicted on the basis of linear propagation if the noise is sufficiently intense such distances are comparable to those used in aircraft noise certification measurements and the frequencies involved have an important influence on the perceived noise level therefore a scheme is required for predicting the finite amplitude propagation of random signals over a wide range of source and atmospheric conditions taylor series solutions to a generalized burgers equation are generated for stationary noise signals the expansions are in powers of the range variable and any frequency dependence of attenuation and dispersion is allowed the limitations of such an approach are discussed and it is found that the number of terms needed increases rapidly with range for propagation over a long range when non linear effects are important it is recommended that one of the differential equations derived for the power spectrum be used together with a closure hypothesis individual terms in the series may be treated separately as local spectrum evolution equations valid at all points along the propagation path with appropriate modelling of the non linear terms such an equation can form a basis for aircraft noise prediction schemes

simplicity and maximal commutative subalgebras of twisted generalized weyl algebras in this paper we prove three theorems about twisted generalized weyl algebras tgwas first we show that each non zero ideal of a tgwa has non zero intersection with the centralizer of the distinguished subalgebra r this is analogous to earlier results known to hold for crystalline graded rings second we give necessary and sufficient conditions for the centralizer of r to be commutative hence maximal commutative generalizing a result by v mazorchuk and l turowska and third we generalize results by d a jordan and v bavula on generalized weyl algebras by giving necessary and sufficient conditions for certain tgwas to be simple in the case when r is commutative we illustrate our theorems by considering some special classes of tgwas and providing concrete examples we also discuss how simplicity of a tgwa is related to the maximal commutativity of r and the non existence of non trivial z n invariant ideals of r less

explaining activities as consistent groups of events we propose a method for disambiguating uncertain detections of events by seeking global explanations for activities given a noisy visual input and exploiting our knowledge of the activity and its constraints one can provide a consistent set of events explaining all the detections the paper presents a complete framework that starts with a general way to formalise the set of global explanations for a given activity using attribute multiset grammars amg an amg combines the event hierarchy with the necessary features for recognition and algebraic constraints defining allowable combinations of events and features parsing a set of detections by such a grammar finds a consistent set of events that satisfies the activity s constraints each parse tree has a posterior probability in a bayesian sense to find the best parse tree the grammar and a finite set of detections are mapped into a bayesian network the set of possible labellings of the bayesian network corresponds to the set of all parse trees for a given set of detections we compare greedy multiple hypotheses trees reversible jump mcmc and integer programming for finding the maximum a posteriori map solution over the space of explanations the framework is tested for two applications the activity in a bicycle rack and around a building entrance

intrackability characterizing video statistics and pursuing video representations videos of natural environments contain a wide variety of motion patterns of varying complexities which are represented by many different models in the vision literature in many situations a tracking algorithm is formulated as maximizing a posterior probability in this paper we propose to measure the video complexity by the entropy of the posterior probability called the intrackability to characterize the video statistics and pursue optimal video representations based on the definition of intrackability our study is aimed at three objectives firstly we characterize video clips of natural scenes by intrackability we calculate the intrackabilities of image points to measure the local inferential uncertainty and collect the histogram of the intrackabilities over the video in space and time as the global video statistics we find that a pca scatter plot based on the first two principle components of intrackability histograms can reflect the major variations i e image scaling and object density in natural video clips secondly we show that different video representations including deformable contours tracking kernels with various appearance features dense motion fields and dynamic texture models are connected by the change of intrackability and thus develop a simple criterion for model transition and for pursuing the optimal video representation thirdly we derive the connections between the intrackability measure and other criteria in the literature such as the shi tomasi texturedness measure conditional number and harris stephens r score and compare with the shi tomasi measure in tracking experiments

polynomial heisenberg algebras polynomial deformations of the heisenberg algebra are studied in detail some of their natural realizations are given by the higher order susy partners and not only by those of first order as is already known of the harmonic oscillator for even order polynomials here it is shown that the susy partners of the radial oscillator play a similar role when the order of the polynomial is odd moreover it will be proved that the general systems ruled by such kinds of algebras in the quadratic and cubic cases involve painleve transcendents of types iv and v respectively

filters random fields and maximum entropy frame towards a unified theory for texture modeling this article presents a statistical theory for texture modeling this theory combines filtering theory and markov random field modeling through the maximum entropy principle and interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of view our theory characterizes the ensemble of images i with the same texture appearance by a probability distribution f i on a random field and the objective of texture modeling is to make inference about f i given a set of observed texture examples in our theory texture modeling consists of two steps 1 a set of filters is selected from a general filter bank to capture features of the texture these filters are applied to observed texture images and the histograms of the filtered images are extracted these histograms are estimates of the marginal distributions of f i this step is called feature extraction 2 the maximum entropy principle is employed to derive a distribution p i which is restricted to have the same marginal distributions as those in 1 this p i is considered as an estimate of f i this step is called feature fusion a stepwise algorithm is proposed to choose filters from a general filter bank the resulting model called frame filters random fields and maximum entropy is a markov random field mrf model but with a much enriched vocabulary and hence much stronger descriptive ability than the previous mrf models used for texture modeling gibbs sampler is adopted to synthesize texture images by drawing typical samples from p i thus the model is verified by seeing whether the synthesized texture images have similar visual appearances to the texture images being modeled experiments on a variety of 1d and 2d textures are described to illustrate our theory and to show the performance of our algorithms these experiments demonstrate that many textures which are previously considered as from different categories can be modeled and synthesized in a common framework

robust tracking with discriminative ranking lists in this paper we propose a novel tracking algorithm i e the discriminative ranking list based tracker drltracker the drltracker models the target object and its local background by using ranking lists of patches of different scales within object bounding boxes the ranking list of each of such patches is its k nearest neighbors patches of the same scale with ranking lists of high purity values meaning high probabilities to be on the target object and some confusable background patches constitute the object model under that scale a pair of object models of two different scales collaborate to determine which patches may belong to the target object in the next frame the drltracker can effectively alleviate the distraction problem and its superior ability over several representative and state of the art trackers is demonstrated through extensive experiments

blind deconvolution via sequential imputations abstract the sequential imputation procedure is applied to adaptively and sequentially reconstruct discrete input signals that are blurred by an unknown linear moving average channel and contaminated by additive gaussian noises a problem known as blind deconvolution in digital communication a rejuvenation procedure for improving the efficiency of sequential imputation is introduced and theoretically justified the proposed method does not require the channel to be nonminimum phase and can be used in real time signal restoration two simulated systems are studied to illustrate the proposed method our result shows that the ideas of multiple imputations and flexible simulation techniques are as powerful in engineering as in survey sampling

a bayesian approach to tracking multiple targets using sensor arrays and particle filters we present a bayesian approach to tracking the direction of arrival doa of multiple moving targets using a passive sensor array the prior is a description of the dynamic behavior we expect for the targets which is modeled as constant velocity motion with a gaussian disturbance acting on the target s heading direction the likelihood function is arrived at by defining an uninformative prior for both the signals and noise variance and removing these parameters from the problem by marginalization advances in sequential monte carlo smc techniques specifically the particle filter algorithm allow us to model and track the posterior distribution defined by the bayesian model using a collection of target states that can be viewed as samples from the posterior of interest we describe two versions of this algorithm and finally present results obtained using synthetic data

robust visual tracking by integrating multiple cues based on co inference learning visual tracking can be treated as a parameter estimation problem that infers target states based on image observations from video sequences a richer target representation may incur better chances of successful tracking in cluttered and dynamic environments and thus enhance the robustness richer representations can be constructed by either specifying a detailed model of a single cue or combining a set of rough models of multiple cues both approaches increase the dimensionality of the state space which results in a dramatic increase of computation to investigate the integration of rough models from multiple cues and to explore computationally efficient algorithms this paper formulates the problem of multiple cue integration and tracking in a probabilistic framework based on a factorized graphical model structured variational analysis of such a graphical model factorizes different modalities and suggests a co inference process among these modalities based on the importance sampling technique a sequential monte carlo algorithm is proposed to provide an efficient simulation and approximation of the co inferencing of multiple cues this algorithm runs in real time at around 30 hz our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios the approach presented in this paper has the potential to solve other problems including sensor fusion problems