generalizing the hough transform to detect arbitrary shapes abstract the hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve the initial work showed how to detect both analytic curves 1 2 and non analytic curves 3 but these methods were restricted to binary edge images this work was generalized to the detection of some analytic curves in grey level images specifically lines 4 circles 5 and parabolas 6 the line detection case is the best known of these and has been ingeniously exploited in several applications 7 8 9 we show how the boundaries of an arbitrary non analytic shape can be used to construct a mapping between image space and hough transform space such a mapping can be exploited to detect instances of that particular shape in an image furthermore variations in the shape such as rotations scale changes or figure ground reversals correspond to straightforward transformations of this mapping however the most remarkable property is that such mappings can be composed to build mappings for complex shapes from the mappings of simpler component shapes this makes the generalized hough transform a kind of universal transform which can be used to find arbitrarily complex shapes

fuzzy filter design for ito stochastic systems with application to sensor fault detection the paper deals with the robust fault detection problem for takagi sugeno t s fuzzy ito stochastic systems our aim is to develop a robust fault detection approach to the t s fuzzy systems with brownian motion by using a general observer based fault detection filter as a residual generator the robust fault detection is formulated as a filtering problem attention is focused on the design of both the fuzzy rule independent and the fuzzy rule dependent fault detection filters guaranteeing a prescribed noise attenuation level in an hinfin sense sufficient conditions are proposed to guarantee the mean square asymptotic stability with an hinfin performance for the fault detection system the corresponding solvability conditions for the desired fuzzy rule independent and fuzzy rule dependent fault detection filters are also established finally a numerical example is provided to illustrate the effectiveness of the proposed theory

on the analysis of the 1 1 evolutionary algorithm many experimental results are reported on all types of evolutionary algorithms but only few results have been proved a step towards a theory on evolutionary algorithms in particular the so called 1 1 evolutionary algorithm is performed linear functions are proved to be optimized in expected time o nlnn but only mutation rates of size 1 n can ensure this behavior for some polynomial of degree 2 the optimization needs exponential time the same is proved for a unimodal function both results were not expected by several other authors finally a hierarchy result is proved moreover methods are presented to analyze the behavior of the 1 1 evolutionary algorithm

structure from motion using line correspondences a theory is presented for the computation of three dimensional motion and structure from dynamic imagery using only line correspondences the traditional approach of corresponding microfeatures interesting points highlights corners high curvature points etc is reviewed and its shortcomings are discussed then a theory is presented that describes a closed form solution to the motion and structure determination problem from line correspondences in three views the theory is compared with previous ones that are based on nonlinear equations and iterative methods

adaptive reliable h_ infty filtering against sensor failures this paper is concerned with the adaptive reliable hinfin filtering problem against sensor failures for continuous time linear systems an adaptive hinfin performance index is defined to describe the disturbance attenuation performance of systems with time varying parameter estimations by combining the linear matrix inequality lmi approach for hinfin filter design and adaptive method a method of designing adaptive reliable hinfin filters is proposed where the filter parameter matrices are updated automatically to compensate the sensor failures effects on systems based on the online estimations of eventual faults it is shown that the design condition for the newly proposed adaptive hinfin filtering is more relaxed than that for the traditional fixed gain filter design based on lmi approach an illustrative example and its simulation results are given to illustrate the effectiveness of the proposed design method

black box complexities of combinatorial problems black box complexity counting the number of queries needed to find the optimum of a problem without having access to an explicit problem description was introduced by droste jansen and wegener s droste t jansen i wegener upper and lower bounds for randomized search heuristics in black box optimization theory of computing systems 39 2006 525 544 to measure the difficulty of solving an optimization problem via generic search heuristics such as evolutionary algorithms simulated annealing or ant colony optimization since then a number of similar complexity notions were introduced however so far these new notions were only analyzed for artificial test problems in this paper we move a step forward and analyze the different black box complexity notions for two classic combinatorial problems namely the minimum spanning tree and the single source shortest path problem besides proving bounds for their black box complexities our work reveals that the choice of how to model the optimization problem has a significant influence on its black box complexity in addition when regarding the unbiased symmetry invariant black box complexity of combinatorial problems it is important to choose a meaningful definition of unbiasedness

finding line segments by stick growing a method is described for extracting lineal features from an image using extended local information to provide robustness and sensitivity the method utilizes both gradient magnitude and direction information and incorporates explicit lineal and end stop terms these terms are combined nonlinearly to produce an energy landscape in which local minima correspond to lineal features called sticks that can be represented as line segments a hill climbing stick growing process is used to find these minima the method is compared to two others and found to have improved gap crossing characteristics

a metric for line segments this correspondence presents a metric for describing line segments this metric measures how well two line segments can be replaced by a single longer one this depends for example on collinearity and nearness of the line segments the metric is constructed using a new technique using so called neighborhood functions the behavior of the metric depends on the neighborhood function chosen in this correspondence an appropriate choice for the case of line segments is presented the quality of the metric is verified by using it in a simple clustering algorithm that groups line segments found by an edge detection algorithm in an image the fact that the clustering algorithm can detect long linear structures in an image shows that the metric is a good measure for the groupability of line segments

a new fault detection scheme for networked control systems subject to uncertain time varying delay fault detection of networked control systems ncs subject to uncertain time varying delay is studied in this paper for the convenience of residual generator design influence caused by network induced delay is first transformed into time varying polytopic uncertainty which greatly facilitates further manipulation then design of the optimal residual generator is formulated as a model matching problem i e to design a residual generator best matching the optimal residual generator of ncs in the delay free case this procedure is assisted by parameter dependent lyapunov function matrix based bounded real lemma which has been proved to be much better than single constant lyapunov function matrix based results this approach not only can be used in conditions that the variation part of the delay is less than one sampling period but also can be applied to scenarios where the variation part of the delay is larger than one sampling period simulation results are also given to illustrate effectiveness of the proposed method

fault detection for t s fuzzy systems with unknown membership functions this paper is concerned with the fault detection fd problem for takagi sugeno t s fuzzy systems with unknown membership functions if the membership functions are unknown the linear fd filter designs with fixed gains have been considered in the literature to reduce the conservatism of the existing results a switching mechanism that depends on the lower and upper bounds of the unknown membership functions is provided to construct an fd filter with varying gains it is shown that the switching type fd filter with varying gains can achieve a better fd performance than the linear fd filter with fixed gains in addition based on some time domain inequalities a novel weighting matrix design approach is introduced to transform the fault sensitivity specification into an h constraint finally two examples are given to show the advantages of the proposed fd method

a ground truth based vanishing point detection algorithm a bayesian probability based vanishing point detection algorithm is presented which introduces the use of multiple features and training with ground truth data to determine vanishing point locations the vanishing points of 352 images were manually identified to create ground truth data each intersection is assigned a probability of being coincident with a ground truth vanishing point based upon conditional probabilities of a number of features the results of this algorithm are demonstrated to be superior to the results of a similar algorithm where each intersection is considered to be of equal importance the advantage of this algorithm is that multiple features derived from ground truth training are used to determine vanishing point location

using angular dispersion of gradient direction for detecting edge ribbons edges in a scene generally project to smooth continuous curves nearly everywhere in the image which results in low angular deviation of the intensity gradients in small neighborhoods straddling edges angular deviation is shown to be a measure of snr a theoretical analysis of angular deviation arising due to independent and identically distributed n 0 sigma sup 2 random noise is presented angular deviation thresholds for neighborhood sizes from 3 3 to 11 11 pixels are determined both from this analysis and numerical examples the proposed gradient angular dispersion detection algorithm detects edge elements by comparing the measured angular deviation with values computed for the minimum acceptable snr low values of deviation violate the no edge hypothesis the algorithm is shown to make good use of the limited dynamic range of the imaging system the sensitivity and selectivity of the strategy are both shown to be high

fuzzy adaptive output feedback fault tolerant tracking control of a class of uncertain nonlinear systems with nonaffine nonlinear faults this paper studies the fuzzy adaptive output feedback fault tolerant control ftc problem for a class of single input and single output uncertain nonlinear systems with time varying nonaffine nonlinear faults in strict feedback form in the design procedure filtered signals are adopted to circumvent algebraic loop problems on implementing the usual controllers by using fuzzy logic systems to approximate the unknown nonlinearity effects and changes in model dynamics due to faults a fuzzy state observer is first presented to estimate the unmeasured states based on the online estimating information from the adaptive mechanism an observer based dynamic output feedback fault tolerant controller is designed via the backstepping technique it is shown that the stability and tracking performances of the closed loop system can be achieved even in the presence of unknown nonlinear faults in comparison with the existing approaches the ftc scheme can handle the nonaffine nonlinear faults effectively finally a simulation example is included to validate the advantages of the proposed approaches

motion and structure from line correspondences closed form solution uniqueness and optimization this work discusses estimating motion and structure parameters from line correspondences of a rigid scene the authors present a closed form solution to motion and structure parameters from line correspondences through three monocular perspective views the algorithm makes use of redundancy in the data to improve the accuracy of the solutions the uniqueness of the solution is established and necessary and sufficient conditions for degenerate spatial line configurations are given optimization has been employed to further improve the accuracy of the estimates in the presence of noise simulations have shown that the errors of the optimized estimates are close to the theoretical lower error bound

performance analysis of randomised search heuristics operating with a fixed budget when for a difficult real world optimisation problem no good problem specific algorithm is available often randomised search heuristics are used they are hoped to deliver good solutions in acceptable time the theoretical analysis usually concentrates on the average time needed to find an optimal or approximately optimal solution this matches neither the application in practice nor the empirical analysis since usually optimal solutions are not known and even if found cannot be recognised more often the algorithms are stopped after some time this motivates a theoretical analysis to concentrate on the quality of the best solution obtained after a pre specified number of function evaluations called budget using this perspective two simple randomised search heuristics random local search and the 1 1 evolutionary algorithm are analysed on some well known example problems upper and lower bounds on the expected quality of a solution for a fixed budget of function evaluations are proven the analysis shows novel and challenging problems in the study of randomised search heuristics it demonstrates the potential of this shift in perspective from expected run time to expected solution quality

kronecker product graph matching abstract in this paper the interpolator based kronecker product graph matching ibkpgm algorithm for performing attributed graph matching is presented the ibkpgm algorithm is based on the kronecker product graph matching kpgm formulation this new formulation incorporates a general approach to a wide class of graph matching problems based on attributed graphs allowing the structure of the graphs to be based on multiple sets of attributes salient features of the ibkpgm algorithm are that no assumption is made about the adjacency structure of the graphs to be matched and that the explicit calculation of compatibility values between all vertices of the reference and input graphs as well as between all edges of the reference and input graphs are avoided

h sub spl infin fuzzy output feedback control design for nonlinear systems an lmi approach addresses the problem of stabilizing a class of nonlinear systems by using an h sub spl infin fuzzy output feedback controller first a class of nonlinear systems is approximated by a takagi sugeno ts fuzzy model then based on a well known lyapunov functional approach we develop a technique for designing an h sub spl infin fuzzy output feedback control law which guarantees the l sub 2 gain from an exogenous input to a regulated output is less or equal to a prescribed value a design algorithm for constructing an h sub spl infin fuzzy output feedback controller is given in contrast to the existing results the premise variables of the h sub spl infin fuzzy output feedback controller are not necessarily to be the same as the premise variables of the ts fuzzy model of the plant a numerical simulation example is presented to illustrate the theory development

robust filtering for discrete time systems with saturation and its application to transmultiplexers this paper considers the problem of robust filtering for discrete time linear systems subject to saturation a generalized dynamic filter architecture is proposed and a filter design method is developed our approach incorporates the conventional linear h sub 2 and h sub spl infin filtering as well as a regional l sub 2 gain filtering feature developed specially for the saturation nonlinearity and is applicable to the digital transmultiplexer systems for the purpose of separating filterbank design it turns out that our filter design can be carried out by solving a constrained optimization problem with linear matrix inequality lmi constraints simulations show that the resultant separating filters possess satisfactory reconstruction performance while working in the linear range and less degraded reconstruction performance in the presence of saturation

an algebraic method for detection and recognition of polyhedral objects from a single image abstract this paper proposes an algorithm to recognize rotated scaled or overlapping polyhedral objects using only one orthographic projection the procedure is not based on the numerical solution of nonlinear equations and does not require any a priori knowledge it is an algebraic method for testing whether a cluster of edges and vertices in a line drawing correspond to an orthographic view of a given 3d model it has the advantage of being based on simple algebraic equations which are easy to solve and do not require time consuming numerical procedures

3d object classification using multi object kohonen networks the problem of three dimensional planar faced opaque object recognition from one single perspective view is addressed in this paper a view parameterization based on hough transform is described classification is accomplished by multiple multi object kohonen networks a new object grouping criterion has been investigated to assign disjoint subsets of objects to each kohonen network of the system on the basis of input space topology recognition tests are presented on both synthetic and real world 3d objects even in the presence of partial occlusion

robust h filter design with past output measurements for uncertain discrete time systems this paper is concerned with the design of robust filters for uncertain discrete time linear systems the uncertainty is assumed to be time invariant belonging to a polytope an upper bound to the h norm of the transfer function from the system input to the filtering error is used as performance criterion the novelty is that the resulting robust filter has order greater than the order of the system being filtered by incorporating past output measurements the design conditions are expressed in terms of convex problems constrained by linear matrix inequalities performance is assessed through parameter dependent lyapunov functions the results generalize existing robust filtering procedures without bringing in any additional conservatism simple numerical examples illustrate the procedure

extracting straight lines this paper presents a new approach to the extraction of straight lines in intensity images pixels are grouped into line support regions of similar gradient orientation and then the structure of the associated intensity surface is used to determine the location and properties of the edge the resulting regions and extracted edge parameters form a low level representation of the intensity variations in the image that can be used for a variety of purposes the algorithm appears to be more effective than previous techniques for two key reasons 1 the gradient orientation rather than gradient magnitude is used as the initial organizing criterion prior to the extraction of straight lines and 2 the global context of the intensity variations associated with a straight line is determined prior to any local decisions about participating edge elements

h filtering of discrete time fuzzy systems via basis dependent lyapunov function approach this paper is concerned with the h filtering problem for a class of discrete time fuzzy systems attention is focused on the design of a stable filter guaranteeing a prescribed noise attenuation level in the h sense by using basis dependent lyapunov functions sufficient conditions for the solvability of this problem are obtained it has been shown that the h filtering problem can be solved as a linear matrix inequality lmi optimization problem two examples are provided to demonstrate the applicability of the proposed approach

delay dependent guaranteed cost control for t s fuzzy systems with time delays this study introduces a guaranteed cost control method for nonlinear systems with time delays which can be represented by takagi sugeno t s fuzzy models with time delays the state feedback and generalized dynamic output feedback approaches are considered the generalized dynamic output feedback controller is presented by a new fuzzy controller architecture which is of dual indexed rule base it considers both the dynamic part and the output part of t s fuzzy model which guarantees that the controller without any delay information can stabilize time delay t s fuzzy systems based on delay dependent lyapunov functional approach some sufficient conditions for the existence of state feedback controller are provided via parallel distributed compensation pdc first second the corresponding conditions are extended into the generalized dynamic output feedback closed loop system via so called generalized pdc technique the upper bound of time delay can be obtained using convex optimization such that the system can be stabilized for all time delays whose sizes are not larger than the bound the minimizing method is also proposed to search the suboptimal upper bound of guaranteed cost function the effectiveness of the proposed method can be shown by the simulation examples

advanced hough transform using a multilayer fractional fourier method the hough transform ht is a commonly used technique for the identification of straight lines in an image the hough transform can be equivalently computed using the radon transform rt by performing line detection in the frequency domain through use of central slice theorem in this research an advanced radon transform is developed using a multilayer fractional fourier transform a cartesian to polar mapping and 1 d inverse fourier transforms followed by peak detection in the sinogram the multilayer fractional fourier transform achieves a more accurate sampling in the frequency domain and requires no zero padding at the stage of cartesian to polar coordinate mapping our experiments were conducted on mix shape images noisy images mixed thickness lines and a large data set consisting of 751 000 handwritten chinese characters the experimental results have shown that our proposed method outperforms all known representative line detection methods based on the standard hough transform or the fourier transform

edge detection and linear feature extraction using a 2 d random field model the edge detection problem is posed as one of detecting step discontinuities in the observed correlated image using directional derivatives estimated with a random field model specifically the method consists of representing the pixels in a local window by a 2 d causal autoregressive ar model whose parameters are adaptively estimated using a recursive least squares algorithm the directional derivatives are functions of parameter estimates an edge is detected if the second derivative in the direction of the estimated maximum gradient is negatively sloped and the first directional derivative and a local estimate of variance satisfy some conditions because the ordered edge detector may not detect edges of all orientations well the image scanned in four different directions and the union of the four edge images is taken as the final output the performance of the edge detector is illustrated using synthetic and real images comparisons to other edge detectors are given a linear feature extractor that operates on the edges produced by the ar model is presented

brief paper robust fault detection for networked systems with communication delay and data missing in this paper the robust fault detection problem is investigated for a class of discrete time networked systems with unknown input and multiple state delays a novel measurement model is utilized to represent both the random measurement delays and the stochastic data missing phenomenon which typically result from the limited capacity of the communication networks the network status is assumed to vary in a markovian fashion and its transition probability matrix is uncertain but resides in a known convex set of a polytopic type the main purpose of this paper is to design a robust fault detection filter such that for all unknown inputs possible parameter uncertainties and incomplete measurements the error between the residual signal and the fault signal is made as small as possible by casting the addressed robust fault detection problem into an auxiliary robust h filtering problem of a certain markovian jumping system a sufficient condition for the existence of the desired robust fault detection filter is established in terms of linear matrix inequalities a numerical example is provided to illustrate the effectiveness and applicability of the proposed technique

brief paper set membership filtering for systems with sensor saturation this paper addresses the set membership filtering problem for a class of discrete time varying systems with sensor saturation in the presence of unknown but bounded process and measurement noises a sufficient condition for the existence of set membership filter is derived a convex optimisation method is proposed to determine a state estimation ellipsoid that is a set of states compatible with sensor saturation and unknown but bounded process and measurement noises a recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state by solving a time varying linear matrix inequality simulation results are provided to demonstrate the effectiveness of the proposed method

otlines a novel line detection algorithm without the interference of smooth curves abstract we propose a novel line detection algorithm that avoids interpreting a smooth curve in an image as many straight segments the algorithm starts from a contiguous connected set of edge chains extracted by the edge drawing parameter free edpf algorithm such that a problem of line detection in the image space is transformed to a problem of line segments and circular arcs identified for each edge chain the core of our approach is the proposed orientation transformation ot algorithm based on principal component analysis hence our algorithm is named otlines with the ot algorithm each edge chain is not only decomposed into a series of straight line segments and smooth curve candidates at the estimated corners but is also mapped to a series of oblique lines and horizontal lines in a new space called the orientation space based on the results of the transformation line segments and circular arcs can be detected simultaneously in both the orientation and image spaces using principal component analysis the performance of our proposed algorithm is validated using a set of typical images we also compare our algorithm with existing algorithms our novel algorithm is very effective at segmenting the lines in synthetic and natural images no parameters in the proposed algorithm must be tuned during its application

fault detection and isolation for a class of uncertain state feedback fuzzy control systems this paper investigates the fault detection and isolation fdi problem for a class of nonlinear state feedback control systems with parameter uncertainties first the considered nonlinear systems are described by multiple t s fuzzy models with uncertain grades of membership and local nonlinear parts in normal case and faulty cases then a switching technique is introduced to address the uncertain grades of membership external disturbances local nonlinear parts faults and the coupling of them in a multiple model scheme a bank of fdi observers are constructed each of which is based on the t s model that describes the system in the presence of a particular fault such that one of them can track the current system state and the corresponding observer estimate error can converge exponentially to zero finally two examples are given to illustrate the effectiveness and advantages of the new results

h_ bm infty fuzzy filtering of nonlinear systems with intermittent measurements this paper is concerned with the problem of h infin fuzzy filtering of nonlinear systems with intermittent measurements the nonlinear plant is represented by a takagi sugeno t s fuzzy model the measurements transmission from the plant to the filter is assumed to be imperfect and a stochastic variable satisfying the bernoulli random binary distribution is utilized to model the phenomenon of the missing measurements attention is focused on the design of an h infin filter such that the filter error system is stochastically stable and preserves a guaranteed h infin performance a basis dependent lyapunov function approach is developed to design the h infin filter by introducing some slack matrix variables the coupling between the lyapunov matrix and the system matrices is eliminated which greatly facilitates the filter design procedure the developed theoretical results are in the form of linear matrix inequalities lmis finally an illustrative example is provided to show the effectiveness of the proposed approach

finite horizon estimation of randomly occurring faults for a class of nonlinear time varying systems this paper is concerned with the finite horizon estimation problem of randomly occurring faults for a class of nonlinear systems whose parameters are all time varying the faults are assumed to occur in a random way governed by two sets of bernoulli distributed white sequences the stochastic nonlinearities entering the system are described by statistical means that can cover several classes of well studied nonlinearities the aim of the problem is to estimate the random faults over a finite horizon such that the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by an h norm in the mean square sense by using the completing squares method and stochastic analysis techniques necessary and sufficient conditions are established for the existence of the desired finite horizon h fault estimator whose parameters are then obtained by solving coupled backward recursive riccati difference equations rdes a simulation example is utilized to illustrate the effectiveness of the proposed fault estimation method

brief paper optimal fault detection for linear discrete time varying systems this paper deals with the problem of observer based fault detection for linear discrete time varying ldtv systems a problem formulation is first proposed to address the optimization of the fault detection filter fdf design which is expressed in terms of maximizing a finite horizon h h or h h performance index this formulation can be applied to fdf design of ldtv systems subject to l 2 norm bounded unknown inputs or stochastic noise sequences it is shown that a unified optimal solution to the fdf can be obtained by solving the discrete time riccati equation and the optimal fdf is not unique a numerical example is given to illustrate the proposed method

from black box complexity to designing new genetic algorithms black box complexity theory recently produced several surprisingly fast black box optimization algorithms in this work we exhibit one possible reason these black box algorithms often profit from solutions inferior to the previous best in contrast evolutionary approaches guided by the survival of the fittest paradigm often ignore such solutions we use this insight to design a new crossover based genetic algorithm it uses mutation with a higher than usual mutation probability to increase the exploration speed and crossover with the parent to repair losses incurred by the more aggressive mutation a rigorous runtime analysis proves that our algorithm for many parameter settings is asymptotically faster on the onemax test function class than all what is known for classic evolutionary algorithms a fitness dependent choice of the offspring population size provably reduces the expected runtime further to linear in the dimension our experimental analysis on several test function classes shows advantages already for small problem sizes and broad parameter ranges also a simple self adaptive choice of these parameters gives surprisingly good results

new prospects in line detection by dynamic programming the detection of lines in satellite images has drawn a lot of attention within the last 15 years problems of resolution noise and image understanding are involved and one of the best methods developed so far is the f algorithm of fischler which achieves robustness rightness and rapidity like other methods of dynamic programming it consists of defining a cost which depends on local information then a summation minimization process in the image is performed the authors present herein a mathematical formalization of the f algorithm which allows them to extend the cost both to cliques of more than two points to deal with the contrast and to neighborhoods of size larger than one to take into account the curvature thus all the needed information contrast grey level curvature is synthesized in a unique cost function defined on the digital original image this cost is used to detect roads and valleys in satellite images spot

determining straight line correspondences from intensity images abstract this paper presents an algorithm for straight edge extraction from intensity images and an algorithm for straight line matching in the first part of the paper straight edge extraction is described image data are first processed by the operation of edge support focusing to remove unnecessary image details an edge support is formed in this process then straight edges are extracted from line support regions which are segmented from the edge support in the second part of this paper we describe straight line matching using a matching function which characterizes the similarity of edge lines of two images and is based on not only the geometrical relations of the lines but also the information from the intensity images a technique of two step matching is applied to reduce the cost of computation the motivation behind this paper is our work on motion estimation from line correspondences of sequential images where straight edge extraction and matching is an essential first step the output data of the algorithm can also be used for calibration of stereo camera setups the results of experiments using indoor and outdoor scene images are shown

structure and motion from straight line segments abstract a method to determine both the camera location and scene structure from image straight segment correspondences is presented the proposed method considers the finite segment length in order to use stronger constraints than do those that use the infinite line that supports the image segment the constraints between image segments involve a weak pairing between image segment midpoints this allows deviations of the midpoint only in the segment direction experimental results are presented of structure and motion computations from the image straight line segment matching using two real images

stabilization of nonlinear systems under variable sampling a fuzzy control approach this paper investigates the problem of stabilization for a takagi sugeno t s fuzzy system with nonuniform uncertain sampling the sampling is not required to be periodic and the only assumption is that the distance between any two consecutive sampling instants is less than a given bound by using the input delay approach the t s fuzzy system with variable uncertain sampling is transformed into a continuous time t s fuzzy system with a delay in the state though the resulting closed loop state delayed t s fuzzy system takes a standard form the existing results on delay t s fuzzy systems cannot be used for our purpose due to their restrictive assumptions on the derivative of state delay a new condition guaranteeing asymptotic stability of the closed loop sampled data system is derived by a lyapunov approach plus the free weighting matrix technique based on this stability condition two procedures for designing state feedback control laws are given one casts the controller design into a convex optimization by introducing some over design and the other utilizes the cone complementarity linearization idea to cast the controller design into a sequential minimization problem subject to linear matrix inequality constraints which can be readily solved using standard numerical software an illustrative example is provided to show the applicability and effectiveness of the proposed controller design methodology

three dimensional reconstruction of points and lines with unknown correspondence across images three dimensional reconstruction from a set of images is an important and difficult problem in computer vision in this paper we address the problem of determining image feature correspondences while simultaneously reconstructing the corresponding 3d features given the camera poses of disparate monocular views first two new affinity measures are presented that capture the degree to which candidate features from different images consistently represent the projection of the same 3d point or 3d line an affinity measure for point features in two different views is defined with respect to their distance from a hypothetical projected 3d pseudo intersection point similarly an affinity measure for 2d image line segments across three views is defined with respect to a 3d pseudo intersection line these affinity measures provide a foundation for determining unknown correspondences using weighted bipartite graphs representing candidate point and line matches across different images as a result of this graph representation a standard graph theoretic algorithm can provide an optimal simultaneous matching and triangulation of points across two views and lines across three views experimental results on synthetic and real data demonstrate the effectiveness of the approach

an error analysis for surface orientation from vanishing points given two parallel lines in a plane their images formed by central projection onto the viewing plane intersect at a vanishing point which provides a constraint on the orientation of the plane two such independent constraints define a vanishing line and thereby determine the orientation of the plane uniquely in order to effectively use this information an estimate of its accuracy must be incorporated in the approach suggested line directions and surface normal vectors are represented as points on a gaussian sphere and the error bounds are computed as regions on the sphere information from multiple vanishing points and surfaces is combined by intersecting the corresponding regions in addition constraints based on real world knowledge can be introduced to improve the accuracy some experimental results on natural images re presented to illustrate this method

h filtering with randomly occurring sensor saturations and missing measurements in chap 5 the h filtering problem is studied for a class of nonlinear systems with randomly occurring incomplete information where the considered incomplete information includes both the sensor saturations and the missing measurements a new phenomenon of sensor saturation namely randomly occurring sensor saturation ross is first put forward in order to better reflect the reality in a networked environment then a novel sensor model is established to account for both the ross and missing measurement in a unified representation based on this sensor model a regional h filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean square asymptotically stable and the h norm requirement is satisfied

optimal parameter choices via precise black box analysis abstract it has been observed that some working principles of evolutionary algorithms in particular the influence of the parameters cannot be understood from results on the asymptotic order of the runtime but only from more precise results in this work we complement the emerging topic of precise runtime analysis with a first precise complexity theoretic result our vision is that the interplay between algorithm analysis and complexity theory becomes a fruitful tool also for analyses more precise than asymptotic orders of magnitude as particular result we prove that the unary unbiased black box complexity of the onemax benchmark function class is n ln n c n o n for a constant c which is between 0 2539 and 0 2665 this runtime can be achieved with a simple 1 1 type algorithm using a fitness dependent mutation strength when translated into the fixed budget perspective our algorithm finds solutions which are roughly 13 closer to the optimum than those of the best previously known algorithms to prove our results we formulate several new versions of the variable drift theorems which also might be of independent interest

lsd a fast line segment detector with a false detection control we propose a linear time line segment detector that gives accurate results a controlled number of false detections and requires no parameter tuning this algorithm is tested and compared to state of the art algorithms on a wide set of natural images

correction to on testing trained vector quantizer codebooks this correspondence formally defines partial radon trans forms for functions of more than two dimensions it shows that a generalized projection slice theorem exists which connects planar and hy perplanar projections of a function to its fourier transform in addition a general theoretical framework is provided for carrying out dimensional backprojection reconstruction in a multistage fashion through the use of the partial radon transform

h h fault detection filter design for discrete time takagi sugeno fuzzy system abstract in this note a robust fault detection observer is designed for a t s takagi sugeno fuzzy model with sensor faults and unknown bounded disturbances the method applies the technique of descriptor systems by considering sensor faults as an auxiliary state variable the idea is to formulate the robust fault detection observer design as an h h problem based on nonquadratic lyapunov functions a solution of the considered problem is then given via a linear matrix inequality lmi formulation an example is proposed to illustrate the design conditions

und unite and divide method in fourier and radon domains for line segment detection in this paper we extend our previously proposed line detection method to line segmentation using a so called unite and divide und approach the methodology includes two phases namely the union of spectra in the frequency domain and the division of the sinogram in radon space in the union phase given an image its sinogram is obtained by parallel 2d multilayer fourier transforms cartesian to polar mapping and 1d inverse fourier transform in the division phase the edges of butterfly wings in the neighborhood of every sinogram peak are firstly specified with each neighborhood area corresponding to a window in image space by applying the separated sinogram of each such windowed image we can extract the line segments the division phase identifies the edges of butterfly wings in the neighborhood of every sinogram peak such that each neighborhood area corresponds to a window in image space line segments are extracted by applying the separated sinogram of each windowed image our experiments are conducted on benchmark images and the results reveal that the und method yields higher accuracy has lower computational cost and is more robust to noise compared to existing state of the art methods

robust h h fault detection observer design for descriptor lpv systems with unmeasurable gain scheduling functions this paper addresses a design of a fault detection observer fdo for descriptor linear parameter varying d lpv systems in contrast with the conventional approach for d lpv systems the gain scheduling functions gsf are considered unmeasurable the fdo is investigated in the h h index framework in order to minimise the effects of the disturbance and maximise the effect of faults on the residuals and minimise the error caused by the unmeasurable gsf the stability is guaranteed based on a lyapunov approach sufficient conditions for the existence of the fdo are established in terms of linear matrix inequalities the effectiveness of the proposed method is illustrated by means of a simulation example

a robust line extraction method by unsupervised line clustering abstract this paper describes a new method of extracting straight lines based on unsupervised line clustering it is assumed that each line support region lsr in an image is composed of pixels that share similar gradient orientation values therefore by an appropriate partitioning of gradient space the sets of parallel lines can be more easily extracted previous works on partitioning gradient space however relied on ad hoc methods and cannot be used as reliable tools for the extraction of the number of clusters in gradient space in order to handle such a clustering issue the bhattacharyya distance is introduced to define a measure for cluster separability and thereafter to estimate the number of inherent clusters subsequent to the clustering stage each extracted line support region undergoes a consistency test to evaluate its validity in terms of uncertainty descriptors for the consistency test an entropy based line selection scheme is formulated and a theory from robust statistics is adopted the feasibility of the proposed line extraction method is assessed by considering the issue of vanishing point detection

a method for shift rotation and scale invariant pattern recognition using the form and arrangement of pattern specific features abstract a new computer based method for visual pattern recognition is described the process extracts shape features from binary edge images represented in either real valued or discrete coordinates both separate and overlapping features are extracted maximizing their completeness and continuity without using templates or mathematical feature descriptions these features and their arrangement are encoded in a shift rotation and scale invariant manner and used to recognize incoming patterns from a set of previously encoded patterns stored in memory new patterns may be automatically classified and stored in memory allowing the system to learn and generalize without operator intervention testing shows the system to be completely shift rotation and scale invariant and insensitive to local image distortion

a new design of delay dependent robust cal h _ bm infty filtering for discrete time t s fuzzy systems with time varying delay this paper investigates the problem of delay dependent robust h infin filtering design for a class of uncertain discrete time state delayed takagi sugeno t s fuzzy systems the state delay is assumed to be time varying and of an interval like type which means that both the lower and upper bounds of the time varying delay are available the parameter uncertainties are assumed to have a structured linear fractional form based on a novel fuzzy basis dependent lyapunov krasovskii functional combined with finsler s lemma and an improved free weighting matrix technique for delay dependent criteria a new sufficient condition for robust h infin performance analysis is first derived and then the filter synthesis is developed it is shown that by using a simple linearization technique incorporating a bounding inequality a unified framework can be developed such that both the full order and reduced order filters can be obtained by solving a set of linear matrix inequalities lmis which are numerically efficient with commercially available software finally simulation examples are provided to illustrate the advantages and less conservatism of the proposed approach

fault detection for t s fuzzy systems with past output measurements abstract this paper is concerned with the fault detection fd filter design problem for the discrete time nonlinear systems with past output measurements and sensor saturation the considered discrete time nonlinear systems are described by t s fuzzy models a decomposition method is used to deal with the characteristics of sensor saturation a new fd filter with past output measurements is firstly proposed by using the lyapunov functional method subsequently the design conditions are given in terms of convex problems constrained by linear matrix inequalities lmi moreover it is shown that the fd filter with past output measurements can achieve better fd performances than the existing ones with current output measurements finally some simulation results are given to illustrate the advantages of the proposed design method

structure from motion with line segments under relaxed endpoint constraints we present a novel structure from motion pipeline which estimates motion and wiry 3d structure from imaged line segments across multiple views although the position and orientation of line segments can be determined more accurately than point features the instability of their endpoints and the fact that lines are not constrained by epipolar geometry diverted most research focus away to point based methods in our approach we tackle the problem of instable endpoints by utilizing relaxed constraints on their positions both during matching and as well in the following bundle adjustment stage furthermore we gain efficiency in estimating trifocal image relations by decoupling rotation and translation to this end a novel linear solver for relative translation estimation given rotations from five line correspondences in three views is introduced extensive experiments on long image sequences show that our line based structure from motion pipeline advantageously complements point based methods giving more meaningful 3d representation for indoor scenarios

edge expression based on tree structure abstract it is a difficult problem to construct the edge structure of an object from its defective line drawing after the low level image processing phase a tree structure edge expression is proposed in which the tree expresses the contiguous line segments and all the trees constitute a forest for the object some medium level processing algorithms such as line connecting pruning and appending are derived by using the tree structure experiments are conducted which show the advantages of the expression and corresponding algorithms

how easy is matching 2d line models using local search local search is a well established and highly effective method for solving complex combinatorial optimization problems here local search is adapted to solve difficult geometric matching problems matching is posed as the problem of finding the optimal many to many correspondence mapping between a line segment model and image line segments image data is assumed to be fragmented noisy and cluttered the algorithms presented have been used for robot navigation photo interpretation and scene understanding this paper explores how local search performs as model complexity increases image clutter increases and additional model instances are added to the image data expected run times to find optimal matches with 95 percent confidence are determined for 48 distinct problems involving six models nonlinear regression is used to estimate run time growth as a function of problem size both polynomial and exponential growth models are fit to the run time data for problems with random clutter the polynomial model fits better and growth is comparable to that for tree search for problems involving symmetric models and multiple model instances where tree search is exponential the polynomial growth model is superior to the exponential growth model for one search algorithm and comparable for another

numerically efficient angle width offset and discontinuity determination of straight lines by the discrete fourier bilinear transformation algorithm we introduce a new method for determining the number of straight lines line angles offsets widths and discontinuities in complicated images in this method line angles are obtained by searching the peaks of a hybrid discrete fourier and bilinear transformed line angle spectrum numerical advantages and performance are demonstrated

early image representation using regions defined by maximum gradient paths between singular points abstract a representation for images is described that is intended to be interposed between the pixel data and marr s primal sketch the image is segmented into regions named slope districts which are defined by maximum gradient paths between singular points a rich data structure is formed containing the regions their boundary components overall attributes and relationships with their neighbours this representation provides a framework to simplify the interaction between bottom up and top down processes examples are given using simple grouping rules to construct more complex elements like those in the primal sketch