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

a note on maqusi s proofs and truncation error bounds in the dyadic walsh sampling theorem in 1 and 2 bounds for the truncation error are presented which arises when a sequency limited signal function is reconstructed by finite walsh sampling sums the knowledge of the properties that sequency limited functions have leads to an immediate proof of the dyadic sampling theorem and truncation turns out to be a trivial affair

network based robust h_infty filtering for uncertain linear systems the problem of network based robust hinfin filtering for uncertain linear systems is investigated different from the design of the traditional filter the effects of the network induced delay and data dropout on the performance of a filtering error system are considered the derived criteria for hinfin performance analysis of the filtering error system and filter design are expressed as a set of linear matrix inequalities which can be solved by using convex optimization method numerical examples show the effectiveness of the design method

nonfragile h_ infty filtering of continuous time fuzzy systems this paper is concerned with the problem of nonfragile h filtering for a class of continuous time fuzzy systems attention is focused on the design of a filter such that the filtering error system preserves a prescribed h performance where the filter to be designed is assumed to have gain variations by using the quadratic lyapunov function approach and adding slack matrix variables sufficient conditions for designing the nonfragile h filter are proposed in terms of solutions to a set of linear matrix inequalities lmis finally a simulation example will be given to show the efficiency of the proposed design methods

h_ infty positive filtering for positive linear discrete time systems an augmentation approach in this note we address the reduced order positive filtering problem of positive discrete time systems under the h performance commonly employed approaches such as linear transformation and elimination technique may not be applicable in general due to the positivity constraint of the filter to cope with the difficulty we first represent the filtering error system as a singular system by means of the system augmentation approach which will facilitate the consideration of the positivity constraint two necessary and sufficient conditions are obtained in terms of matrix inequalities under which the filtering error system has a prescribed h performance then a necessary and sufficient condition is proposed for the existence of the desired positive filters and an iterative linear matrix inequality lmi algorithm is presented to compute the filtering matrices which can be easily checked by standard software finally a numerical example to illustrate the effectiveness of the proposed design procedures is presented

mode independent cal h _ infty filters for markovian jump linear systems this note addresses the problem of hinfin filtering for continuous time linear systems with markovian jumping parameters the main contribution of the note is to provide a method for designing an asymptotically stable linear time invariant hinfin filter for systems where the jumping parameter is not accessible the cases where the transition rate matrix of the markov process is either exactly known or unknown but belongs to a given polytope are treated the robust hinfin filtering problem for systems with polytopic uncertain matrices is also considered and a filter design method based on a lyapunov function that depends on the uncertain parameters is developed the proposed filter designs are given in terms of linear matrix inequalities

using selective correlation coefficient for robust image registration a new method is proposed for robust image registration named selective correlation coefficient in order to search images under ill conditioned illumination or partial occlusion a correlation mask image is generated for selecting pixels of an image before matching the mask image can be derived from a binary coded increment sign image defined from any object image and the template the mask rate of occluded regions is theoretically expected to be 0 5 while unoccluded regions have much lower rate than 0 5 robustness for ill conditioned environment can be realized since inconsistent brightness of occluded regions can be omitted from the mask operation furthermore the mask enhancement procedure is proposed to get more stable robustness the effectiveness of masking increases by the procedure resulting in the rate around 0 7 for masking of occluded regions this paper includes a theoretical modeling and analysis of the proposed method and some experimental results with real images

ordinal measures for image correspondence we present ordinal measures of association for image correspondence in the context of stereo linear correspondence measures like correlation and the sum of squared difference between intensity distributions are known to be fragile ordinal measures which are based on relative ordering of intensity values in windows rank permutations have demonstrable robustness by using distance metrics between two rank permutations ordinal measures are defined these measures are independent of absolute intensity scale and invariant to monotone transformations of intensity values like gamma variation between images we have developed simple algorithms for their efficient implementation experiments suggest the superiority of ordinal measures over existing techniques under nonideal conditions these measures serve as a general tool for image matching that are applicable to other vision problems such as motion estimation and texture based image retrieval

robust image registration by increment sign correlation abstract a novel and robust statistic as a similarity measure for robust image registration is proposed the statistic is named as increment sign correlation because it is based on the average evaluation of incremental tendency of brightness in adjacent pixels it is formalized to be a binary distribution or a gaussian distribution for a large image size through statistical analysis and modeling by utilizing the proposed statistical model for example we can theoretically determine a reasonable value of threshold for verification of matching this sign correlation can also be proved to expectedly have the constant value 0 5 for any uncorrelated images to a template image and then the property of the constancy can be utilized to analyze the high robustness for occlusion the good performance for the case of saturation or highlight can also be proved through theoretical analysis and fundamental experiments a basic algorithm for image scanning search and registration over a large scene is represented with a technique for a fast version by the branch and bound approach many experimental evidences with real images are provided and discussed

on a rational transfer function based approach to cal h _ infty filtering design for time delay linear systems this paper introduces a new procedure for h filter design of time delay linear systems a finite order lti system called comparison system is defined in such a way that its h norm is proven to be strongly related to the one of the time delay system differently from what can be viewed as a common feature of filter design methods available in the literature to date the one presented here treats the filtering design of time delay systems with classical numerical routines based on riccati equation and h theory of lti systems the proposed algorithm is simple efficient and easy to implement illustrative examples are solved and discussed in order to put in evidence the most relevant properties of the theoretical results furthermore a practical application is presented

similarity measures for image matching despite occlusions in stereo vision in the context of computer vision matching can be done with similarity measures this paper presents the classification of these measures into five families in addition 18 measures based on robust statistics previously proposed in order to deal with the problem of occlusions are studied and compared to the state of the art a new evaluation protocol and new analyses are proposed and the results highlight the most efficient measures first near occlusions the smooth median powered deviation and second near discontinuities a non parametric transform based measure census

robust h_ infty filtering for switched stochastic system with missing measurements this paper considers the design of robust h infin filtering problem for switched stochastic discrete time delay system with missing measurements via switched lyapunov function approach attention is focused on the design of an exponentially stable filter guaranteeing a prescribed noise attenuation level in the h infin sense and missing measurements probability by using switched lyapunov functionals some sufficient conditions in terms of linear matrix inequalities lmis are obtained finally a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach

stabilization for markovian jump systems with partial information on transition probability based on free connection weighting matrices this paper focuses on stability and stabilization for a class of continuous time markovian jump systems with partial information on transition probability the free connection weighting matrix method is proposed to obtain a less conservative stability criterion of markovian jump systems with partly unknown transition probability or completely unknown transition probability as a result a sufficient condition for the state feedback controller design is derived in terms of linear matrix inequalities finally numerical examples are given to illustrate the effectiveness and the merits of the proposed method

dyadic calculus and sampling theorems for functions with multidimensional domain i general theory the theory of dyadic walsh analysis for functions of several variables is presented to give a rather complete presentation of dyadic analysis a multidimensional version of the dyadic derivative introduced by gibbs and butzer wagner is developed moreover using walsh fourier transform techniques connections between dyadic differentiability and smoothness conditions are established

network based h_ infty filtering for discrete time systems this correspondence is concerned with network based h filtering for discrete time systems the output signals of the system under consideration are transmitted to the filter through a constraint communication network which usually leads to network induced delays and packet dropouts by introducing a logic data packet processor to choose the newest data signal from the network to actuate the filter network induced delays and packet dropouts are modeled as a markov chain taking values in a finite set as a result the filter to be designed is modeled as a markov jumping linear filter by introducing some slack matrix variables in terms of prob ability identity a less conservative bounded real lemma brl is derived to ensure that the filtering error system is stochastically stable with a pre scribed h level based on this brl suitable h filters are designed by employing a cone complementary approach a practical example on the leslie model about some certain pest s structured population dynamics is given to show the effectiveness of the proposed approach

an extension of the dyadic calculus with fractional order derivatives general theory a new dyadic calculus is set up principally based upon that introduced by butzer and wagner 1972 1975 and zelin he 1983 the extended dyadic derivative of a function which is formulated for fractional orders is roughly the euler summation process applied to the fourier walsh series off after it has first been equipped with a certain multiplicative factor this extended calculus is not only applicable to piecewise constant functions as is the classical dyadic derivative but also to piecewise polynomials this paper presents the full general theory of this extended dyadic calculus introduction and justification of the dyadic derivative its fundamental properties and those of its eigenvalues the corresponding anti differentiation operator a type of counterpart of the fundamental theorem of the newton leibniz calculus in the frame of walsh analysis applications and further theory are dealt with in the second paper in the series

robust h_ infty filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts in this paper the robust h filtering problem is studied for a class of uncertain nonlinear networked systems with both multiple stochastic time varying communication delays and multiple packet dropouts a sequence of random variables all of which are mutually independent but obey bernoulli distribution are introduced to account for the randomly occurred communication delays the packet dropout phenomenon occurs in a random way and the occurrence probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval the discrete time system under consideration is also subject to parameter uncertainties state dependent stochastic disturbances and sector bounded nonlinearities we aim to design a linear full order filter such that the estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of the h performance index intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribed h performance for the overall filtering error dynamics in the presence of random delays random dropouts nonlinearities and the parameter uncertainties these conditions are characterized in terms of the feasibility of a set of linear matrix inequalities lmis and then the explicit expression is given for the desired filter parameters simulation results are employed to demonstrate the effectiveness of the proposed filter design technique in this paper

walsh spectral analysis in this paper we review recent work on the stochastic approach of walsh spectral analysis we are mainly interested in the properties of the finite walsh transform which in turn allow us to derive properties of the estimators of the walsh spectrum we further discuss linear dyadic filters and walsh characteristic functions a rather complete list of references is given

on image analysis by moments research has been performed investigating the use of moments for pattern recognition in recent years the basic problem of the influence of discretization and noise on moment accuracy as object descriptors has been barely investigated in this paper the detailed error analysis involved in the moment method is discussed several new techniques to increase the accuracy and efficiency of moment descriptor are proposed we utilize these results for the problem of image reconstruction from the orthogonal legendre moments computed from discrete and noisy data the automatic selection of an optimal number of moments is also discussed

least median of squares regression abstract classical least squares regression consists of minimizing the sum of the squared residuals many authors have produced more robust versions of this estimator by replacing the square by something else such as the absolute value in this article a different approach is introduced in which the sum is replaced by the median of the squared residuals the resulting estimator can resist the effect of nearly 50 of contamination in the data in the special case of simple regression it corresponds to finding the narrowest strip covering half of the observations generalizations are possible to multivariate location orthogonal regression and hypothesis testing in linear models

matching by monotonic tone mapping in this paper a novel dissimilarity measure called matching by monotonic tone mapping mmtm is proposed the mmtm technique allows matching under non linear monotonic tone mappings and can be computed efficiently when the tone mappings are approximated by piecewise constant or piecewise linear functions the proposed method is evaluated in various template matching scenarios involving simulated and real images and compared to other measures developed to be invariant to monotonic intensity transformations the results show that the mmtm technique is a highly competitive alternative of conventional measures in problems where possible tone mappings are close to monotonic

suboptimal reduced order filtering through an lmi based method this paper addresses the reduced order filtering design problem for continuous time linear systems the h2 and hinfin norms of the estimation error used as performance criteria are discussed and a new linear matrix inequalities lmi based method for reduced order filter design is proposed differently from the methods available in the literature to date the one presented here does not solve the associated nonconvex problem by means of an appropriate optimization procedure it is based on the a priori determination of a certain matrix that simultaneously rends convex the problem to be solved and reduces the suboptimality degree of the solution its efficiency is tested by means of two examples the first one borrowed from the literature allows the comparison of our method for the h2 norm case with three earlier techniques one of them being the well known balanced truncation the second one of higher order consists of the estimation of the displacement of a tapered bar using an hinfin norm criterion

brief paper mode dependent h filtering for discrete time markovian jump linear systems with partly unknown transition probabilities in this paper the problem of h filtering for a class of discrete time markovian jump linear systems mjls with partly unknown transition probabilities is investigated the considered systems are more general which cover the mjls with completely known and completely unknown transition probabilities as two special cases a mode dependent full order filter is constructed and the bounded real lemma brl for the resulting filtering error system is derived via lmi formulation then an improved version of the brl is further given by introducing additional slack matrix variables to eliminate the cross coupling between system matrices and lyapunov matrices among different operation modes finally the existence criterion of the desired filter is obtained such that the corresponding filtering error system is stochastically stable with a guaranteed h performance index a numerical example is presented to illustrate the effectiveness and potential of the developed theoretical results

stabilization of networked control systems with a logic zoh the technical note is concerned with the stabilization problem of networked control systems a general framework is proposed firstly where the zero order hold has the logical capability of choosing the newest control input packet the continuous time process is discretized as a system with input delays then a sufficient condition for testing the stability of the discretized system and two sufficient conditions for designing a stabilizing controller are established based upon the lyapunov theory finally numerical examples and simulations are used to illustrate the developed theory

on image analysis by the methods of moments various types of moments have been used to recognize image patterns in a number of applications a number of moments are evaluated and some fundamental questions are addressed such as image representation ability noise sensitivity and information redundancy moments considered include regular moments legendre moments zernike moments pseudo zernike moments rotational moments and complex moments properties of these moments are examined in detail and the interrelationships among them are discussed both theoretical and experimental results are presented

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

focused color intersection with efficient searching for object extraction we propose focused color intersection with efficient searching for identifying and extracting the objects in a complex scene based on color similarity the method matches the models against different parts of a scene called focus regions using normalized color histogram intersection the best matching focus region is determined by an efficient search strategy employing upper bound pruning this search strategy called active search concentrates its effort on parts of the scene having high similarity with the object consequently it achieves a large reduction in computational effort without sacrificing accuracy an efficient algorithm for evaluating the color histogram intersection between a model and a focus region is also given experiments conducted demonstrate that multiple known objects in complex scenes can be extracted by this process the method is stable against scale changes two dimensional rotation moderate changes in shape and partial occlusion

robust regression methods for computer vision a review regression analysis fitting a model to noisy data is a basic technique in computer vision robust regression methods that remain reliable in the presence of various types of noise are therefore of considerable importance we review several robust estimation techniques and describe in detail the least median of squares lmeds method the method yields the correct result even when half of the data is severely corrupted its efficiency in the presence of gaussian noise can be improved by complementing it with a weighted least squares based procedure the high time complexity of the lmeds algorithm can be reduced by a monte carlo type speed up technique we discuss the relationship of lmeds with the ransac paradigm and its limitations in the presence of noise corrupting all the data and we compare its performance with the class of robust m estimators references to published applications of robust techniques in computer vision are also given

robust h filtering for networked systems with multiple state delays in this paper a new robust h filter design problem is studied for a class of networked systems with multiple state delays two kinds of incomplete measurements namely measurements with random delays and measurements with stochastic missing phenomenon are simultaneously considered such incomplete measurements are induced by the limited bandwidth of communication networks and are modelled as a linear function of a certain set of indicator functions that depend on the same stochastic variable attention is focused on the analysis and design problems of a full order robust h filter such that for all admissible parameter uncertainties and all possible incomplete measurements the filtering error dynamics is exponentially mean square stable and a prescribed h attenuation level is guaranteed some recently reported methodologies such as delay dependent and parameter dependent stability analysis approaches are employed to obtain less conservative results sufficient conditions which are dependent

on walsh differentiable dyadically stationary random processes some basic properties of dyadically stationary ds processes are introduced including continuity and spectral representation a sampling theorem based on the walsh functions is investigated for random signals that are not necessarily sequency limited by using the concept of a dyadic derivative the resulting aliasing error is calculated together with the speed of convergence an example gives a glimpse into the possibilities of applying the sampling theorem as well as the dyadic derivative