noise robust image edge detection based upon the automatic anisotropic gaussian kernels this paper presents a novel noise robust edge detector based upon the automatic anisotropic gaussian kernels angks which also addresses the current problem that the seminal canny edge detector may miss some obvious crossing edge details firstly automatic angks are designed according to the noise suppression edge resolution and localization precision which also conciliate the conflict between them secondly reasons why cross edge points are missing from canny detector results using isotropic gaussian kernel are analyzed thirdly the automatic angks are used to smooth image and a revised edge extraction method is used to extract edges finally the aggregate test receiver operating characteristic roc curves and pratt s figure of merit fom are used to evaluate the proposed detector against state of the art edge detectors the experiment results show that the proposed algorithm can obtain better performance for noise free and noisy images

minimax entropy principle and its application to texture modeling this article proposes a general theory and methodology called the minimax entropy principle for building statistical models for images or signals in a variety of applications this principle consists of two parts the first is the maximum entropy principle for feature binding or fusion for a given set of observed feature statistics a distribution can be built to bind these feature statistics together by maximizing the entropy over all distributions that reproduce them the second part is the minimum entropy principle for feature selection among all plausible sets of feature statistics we choose the set whose maximum entropy distribution has the minimum entropy computational and inferential issues in both parts are addressed in particular a feature pursuit procedure is proposed for approximately selecting the optimal set of features the minimax entropy principle is then corrected by considering the sample variation in the observed feature statistics and an information criterion for feature pursuit is derived the minimax entropy principle is applied to texture modeling where a novel markov random field mrf model called frame filter random field and minimax entropy is derived and encouraging results are obtained in experiments on a variety of texture images the relationship between our theory and the mechanisms of neural computation is also discussed

robust h sup spl infin filtering for uncertain markovian jump systems with mode dependent time delays this paper considers the problem of robust h sup spl infin filtering for uncertain markovian jump linear systems with time delays which are time varying and depend on the system mode the parameter uncertainties are time varying norm bounded the aim of this problem is to design a markovian jump linear filter that ensures robust exponential mean square stability of the filtering error system and a prescribed l sub 2 induced gain from the noise signals to the estimation error for all admissible uncertainties a sufficient condition for the solvability of this problem is obtained the desired filter can be constructed by solving a set of linear matrix inequalities an illustrative numerical example is provided to demonstrate the effectiveness of the proposed approach

asynchronous l 2 l filtering for discrete time stochastic markov jump systems with randomly occurred sensor nonlinearities this paper is concerned with the problem of asynchronous l 2 l filtering for discrete time stochastic markov jump systems with sensor nonlinearity the sensor nonlinearity is assumed to occur randomly according to a stochastic variable satisfying the bernoulli distribution a sufficient condition is first given such that the resultant filtering error system which is a kind of nonhomogeneous markov jump system is stochastically stable with a guaranteed l 2 l performance index then the existence criterion of the desired asynchronous filter with piecewise homogeneous markov chain is proposed in terms of a set of linear matrix inequalities a numerical example is given to show the effectiveness and potential of the developed theoretical results

technical communique sliding mode control of singular stochastic hybrid systems this paper is concerned with the sliding mode control smc of nonlinear singular stochastic systems with markovian switching an integral sliding surface function is designed and the resulting sliding mode dynamics is a full order markovian jump singular stochastic system by introducing some specified matrices a new sufficient condition is proposed in terms of strict linear matrix inequality lmi which guarantees the stochastic stability of the sliding mode dynamics then a smc law is synthesized for reaching motion moreover when there exists an external disturbance the 2 disturbance attenuation performance is analyzed for the sliding mode dynamics some related sufficient conditions are also established

robust h observer design of linear time delay systems with parametric uncertainty abstract this paper deals with the problem of h observer design for a class of uncertain linear systems with delayed state and parameter uncertainties this problem aims at designing the linear state observers such that for all admissible parameter uncertainties the observation process remains robustly stable and the transfer function from exogenous disturbances to error state outputs meets the prespecified h norm upper bound constraint independently of the time delay the time delay is assumed to be unknown and the parameter uncertainties are allowed to be norm bounded and appear in all the matrices of the state space model an effective matrix inequality methodology is developed to solve the proposed problem we derive the conditions for the existence of the desired robust h observers and then characterize the analytical expression of these observers in terms of some free parameters a numerical example demonstrates the validity and applicability of the present approach

gradient estimation using wide support operators one of the fastest methods of localizing edges in images is based on small gradient kernels such as sobel prewitt and roberts although small gradient kernels provide a fast way of computing the gradients they have little control over noise edge location and edge orientation they are known to be only sensitive to step edges and fail to detect smooth boundaries on the other hand large kernels provide superior noise suppression characteristics but they suffer from wide response area around edges they cause edges of neighboring objects to merge due to their wide support problems associated with large gradient kernels prevent their widespread usage this paper presents a fuzzy topology based method to facilitate the use of larger gradient kernels the new method effectively limits the response area around the edge and prevents neighboring objects to affect each other synthetic images are used to show the superior noise suppression properties and response characteristics to both step and ramp edges natural images are also used to assess the performance of the newly proposed topological gradient estimation qualitatively

design of sliding mode control subject to packet losses this technical note investigates the design of sliding mode control subject to packet losses it is assumed that there exists a communication network in the feedback loop and the dropout of data packet may occur first an estimation method is proposed to compensate the packet dropout subsequently a discrete time integral sliding surface involving dropout probability is introduced and a sliding mode controller is designed by using the stochastic lyapunov method the state trajectories are shown to enter into in mean square a neighborhood of the specified sliding surface meanwhile the stability of sliding mode dynamics is also ensured finally numerical simulation example is provided

brief paper sliding mode control for ito stochastic systems with markovian switching this paper deals with the problem of sliding mode control smc for a class of nonlinear uncertain stochastic systems with markovian switching by introducing some specified matrices the connections among the designed sliding surfaces corresponding to every mode are established furthermore the present sliding mode controller including the transition rates of modes can cope with the effect of markovian switching by means of linear matrix inequalities lmis with equality constraint sufficient conditions are derived such that the sliding motions on the specified sliding surfaces are stochastically stable with c disturbance attenuation level finally a numerical example is given to illustrate the applicability of the present method

technical communique sliding mode control with bounded l 2 gain performance of markovian jump singular time delay systems in this paper we investigate the problem of sliding mode control smc of markovian jump singular time delay systems the aim is to consider the bounded 2 gain performance in the analysis of sliding mode dynamics thus to improve the transient performance of the smc system firstly a delay dependent bounded real lemma is proposed for the underlying system to be stochastically admissible while achieving the prescribed bounded 2 gain performance condition an integral type switching surface function is designed by taking the singular matrix into account thus the resulting sliding mode dynamics is a full order singular markovian jump time delay system then the sliding mode dynamics is analyzed and the solvability condition for the desired switching surface function is derived moreover an smc law is synthesized to drive the system trajectories onto the predefined switching surface in a finite time finally a numerical example is provided to illustrate the effectiveness of the proposed techniques

fundamental limits of bayesian inference order parameters and phase transitions for road tracking there is a growing interest in formulating vision problems in terms of bayesian inference and in particular the maximum a posteriori map estimator in this paper we consider the special case of detecting roads from aerial images and demonstrate that analysis of this ensemble enables us to determine fundamental bounds on the performance of the map estimate we demonstrate that there is a phase transition at a critical value of the order parameter below this phase transition it is impossible to detect the road by any algorithm we derive closely related order parameters which determine the time and memory complexity of search and the accuracy of the solution using the n search strategy our approach can be applied to other vision problems and we briefly summarize the results when the model uses the wrong prior we comment on how our work relates to studies of the complexity of visual search and the critical behaviour in the computational cost of solving np complete problems

julia sets and bifurcation diagrams for exponential maps on decrit certaines des bifurcations qui apparaissent dans la famille des applications entieres e z exp z pour 1 on sait que j e c on montre qu il y a beaucoup d autres valeurs pour lesquelles ceci arrive

retinal vessel centerline extraction using multiscale matched filters confidence and edge measures motivated by the goals of improving detection of low contrast and narrow vessels and eliminating false detections at nonvascular structures a new technique is presented for extracting vessels in retinal images the core of the technique is a new likelihood ratio test that combines matched filter responses confidence measures and vessel boundary measures matched filter responses are derived in scale space to extract vessels of widely varying widths a vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile vessel boundary measures and associated confidences are computed at potential vessel boundaries combined these responses form a six dimensional measurement vector at each pixel a training technique is used to develop a mapping of this vector to a likelihood ratio that measures the vesselness at each pixel results comparing this vesselness measure to matched filters alone and to measures based on the hessian of intensities show substantial improvements both qualitatively and quantitatively the hessian can be used in place of the matched filter to obtain similar but less substantial improvements or to steer the matched filter by preselecting kernel orientations finally the new vesselness likelihood ratio is embedded into a vessel tracing framework resulting in an efficient and effective vessel centerline extraction algorithm

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

prior learning and gibbs reaction diffusion this article addresses two important themes in early visual computation it presents a novel theory for learning the universal statistics of natural images and it proposes a general framework of designing reaction diffusion equations for image processing we studied the statistics of natural images including the scale invariant properties then generic prior models were learned to duplicate the observed statistics based on minimax entropy theory the resulting gibbs distributions have potentials of the form u i spl lambda s spl sigma sub spl alpha 1 sup k spl sigma sub x y spl lambda sup spl alpha f sup spl alpha i x y with s f sup 1 f sup 2 f sup k being a set of filters and spl lambda spl lambda sup 1 spl lambda sup 2 spl lambda sup k the potential functions the learned gibbs distributions confirm and improve the form of existing prior models such as line process but in contrast to all previous models inverted potentials were found to be necessary we find that the partial differential equations given by gradient descent on u i spl lambda s are essentially reaction diffusion equations where the usual energy terms produce anisotropic diffusion while the inverted energy terms produce reaction associated with pattern formation enhancing preferred image features we illustrate how these models can be used for texture pattern rendering denoising image enhancement and clutter removal by careful choice of both prior and data models of this type incorporating the appropriate features

noise robust edge detector combining isotropic and anisotropic gaussian kernels a new noise robust edge detector is proposed which combines a small scaled isotropic gaussian kernel and large scaled anisotropic gaussian kernels angks to obtain edge maps of images its main advantage is that noise reduction is attained while maintaining high edge resolution from the angks anisotropic directional derivatives andds are derived to capture the locally directional variation of an image the andd based edge strength map esm is constructed its noise robustness is determined by the scale alone and its edge resolution by the ratio of the scale to the anisotropic factor moreover the edge stretch effect in anisotropic smoothing is revealed the andd based esm and the gradient based esm with a small scaled isotropic gaussian kernel are fused into a noise robust esm with high edge resolution and little edge stretch embedding the fused esm into the routine of canny detector a noise robust edge detector is developed which includes two additional modifications contrast equalization and noise dependent lower threshold the aggregate test receiver operating characteristic roc curves and the pratt s figure of merit fom are used to evaluate the proposed detector by abundant experiments the experimental results show that the proposed detector can obtain high quality edge maps for noise free and noisy images

brief on robust stabilization of markovian jump systems with uncertain switching probabilities this brief paper is concerned with the robust stabilization problem for a class of markovian jump linear systems with uncertain switching probabilities the uncertain markovian jump system under consideration involves parameter uncertainties both in the system matrices and in the mode transition rate matrix first a new criterion for testing the robust stability of such systems is established in terms of linear matrix inequalities then a sufficient condition is proposed for the design of robust state feedback controllers a globally convergent algorithm involving convex optimization is also presented to help construct such controllers effectively finally a numerical simulation is used to illustrate the developed theory

laplacian operator based edge detectors laplacian operator is a second derivative operator often used in edge detection compared with the first derivative based edge detectors such as sobel operator the laplacian operator may yield better results in edge localization unfortunately the laplacian operator is very sensitive to noise in this paper based on the laplacian operator a model is introduced for making some edge detectors also the optimal threshold is introduced for obtaining a maximum a posteriori map estimate of edges

probabilistic framework for the characterization of surfaces and edges in range images with application to edge detection we develop a powerful probabilistic framework for the local characterization of surfaces and edges in range images we use the geometrical nature of the data to derive an analytic expression for the joint probability density function pdf for the random variables used to model the ranges of a set of pixels in a local neighborhood of an image we decompose this joint pdf by considering independently the cases where two real world points corresponding to two neighboring pixels are locally on the same real world surface or not in particular we show that this joint pdf is linked to the voigt pdf and not to the gaussian pdf as it is assumed in some applications we apply our framework to edge detection and develop a locally adaptive algorithm that is based on a probabilistic decision rule we show in an objective evaluation that this new edge detector performs better than prior art edge detectors this proves the benefits of the probabilistic characterization of the local neighborhood as a tool to improve applications that involve range images

statistical edge detection learning and evaluating edge cues we formulate edge detection as statistical inference this statistical edge detection is data driven unlike standard methods for edge detection which are model based for any set of edge detection filters implementing local edge cues we use presegmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge edge detection is formulated as a discrimination task specified by a likelihood ratio test on the filter responses this approach emphasizes the necessity of modeling the image background the off edges we represent the conditional probability distributions nonparametrically and illustrate them on two different data sets of 100 sowerby and 50 south florida images multiple edges cues including chrominance and multiple scale are combined by using their joint distributions hence this cue combination is optimal in the statistical sense we evaluate the effectiveness of different visual cues using the chernoff information and receiver operator characteristic roc curves this shows that our approach gives quantitatively better results than the canny edge detector when the image background contains significant clutter in addition it enables us to determine the effectiveness of different edge cues and gives quantitative measures for the advantages of multilevel processing for the use of chrominance and for the relative effectiveness of different detectors furthermore we show that we can learn these conditional distributions on one data set and adapt them to the other with only slight degradation of performance without knowing the ground truth on the second data set this shows that our results are not purely domain specific we apply the same approach to the spatial grouping of edge cues and obtain analogies to nonmaximal suppression and hysteresis

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

the bayesian operating point of the canny edge detector we have investigated the operating point of the canny edge detector which minimizes the bayes risk of misclassification by considering each of the sequential stages which constitute the canny algorithm we conclude that the linear filtering stage of canny without postprocessing performs very poorly by any standard in pattern recognition and achieves error rates which are almost indistinguishable from a priori classification we demonstrate that the edge detection performance of the canny detector is due almost entirely to the postprocessing stages of nonmaximal suppression and hysteresis thresholding

state estimation and sliding mode control of markovian jump singular systems this paper is concerned with the state estimation and sliding mode control problems for continuous time markovian jump singular systems with unmeasured states firstly a new necessary and sufficient condition is proposed in terms of strict linear matrix inequality lmi which guarantees the stochastic admissibility of the unforced markovian jump singular system then the sliding mode control problem is considered by designing an integral sliding surface function an observer is designed to estimate the system states and a sliding mode control scheme is synthesized for the reaching motion based on the state estimates it is shown that the sliding mode in the estimation space can be attained in a finite time some conditions for the stochastic admissibility of the overall closed loop system are derived finally a numerical example is provided to illustrate the effectiveness of the proposed theory

brief paper stability and stabilization of markovian jump linear systems with partly unknown transition probabilities in this paper the stability and stabilization problems of a class of continuous time and discrete time markovian jump linear system mjls with partly unknown transition probabilities are investigated the system under consideration is more general which covers the systems with completely known and completely unknown transition probabilities as two special cases the latter is hereby the switched linear systems under arbitrary switching moreover in contrast with the uncertain transition probabilities studied recently the concept of partly unknown transition probabilities proposed in this paper does not require any knowledge of the unknown elements the sufficient conditions for stochastic stability and stabilization of the underlying systems are derived via lmis formulation and the relation between the stability criteria currently obtained for the usual mjls and switched linear systems under arbitrary switching are exposed by the proposed class of hybrid systems two numerical examples are given to show the validity and potential of the developed results

on designing of sliding mode control for stochastic jump systems in this note we consider the problems of stochastic stability and sliding mode control for a class of linear continuous time systems with stochastic jumps in which the jumping parameters are modeled as a continuous time discrete state homogeneous markov process with right continuous trajectories taking values in a finite set by using linear matrix inequalities lmis approach sufficient conditions are proposed to guarantee the stochastic stability of the underlying system then a reaching motion controller is designed such that the resulting closed loop system can be driven onto the desired sliding surface in a limited time it has been shown that the sliding mode control problem for the markovian jump systems is solvable if a set of coupled lmis have solutions a numerical example is given to show the potential of the proposed techniques

stabilization of singular t s fuzzy markovian jump system with mode dependent derivative term coefficient via sliding mode control abstract this paper introduces sliding mode control for singular takagi sugeno t s fuzzy markovian jump system with mode dependent derivative term coefficient firstly a new continuous switching function is designed then sufficient conditions about admissibility of sliding motion under the case of completely known transition rate and uncertain transition rate are presented respectively the obtained conditions are strict linear matrix inequalities furthermore a sliding mode controller is presented finally an example is given to illustrate the effectiveness of the obtained results in this paper

region competition unifying snakes region growing and bayes mdl for multiband image segmentation we present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition this algorithm is derived by minimizing a generalized bayes minimum description length mdl criterion using the variational principle the algorithm is guaranteed to converge to a local minimum and combines aspects of snakes balloons and region growing the classic snakes balloons and region growing algorithms can be directly derived from our approach we provide theoretical analysis of region competition including accuracy of boundary location criteria for initial conditions and the relationship to edge detection using filters it is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images color images and texture images the novel color model allows us to eliminate intensity gradients and shadows thereby obtaining segmentation based on the albedos of objects it also helps detect highlight regions

statistics of natural images scaling in the woods we study the statistics of an ensemble of images taken in the woods distributions of local quantities such as contrast are scale invariant and have nearly exponential tails power spectra exhibit scaling with a nontrivial exponent these data limit the information content of natural images and point to the importance of gain control strategies in visual processing

kalman filtering for continuous time uncertain systems with markovian jumping parameters studies the problem of kalman filtering for a class of uncertain linear continuous time systems with markovian jumping parameters the system under consideration is subjected to time varying norm bounded parameter uncertainties in the state and measurement equations stochastic quadratic stability of the above system is analyzed a state estimator is designed such that the covariance of the estimation error is guaranteed to be within a certain bound for all admissible uncertainties which is in terms of solutions of two sets of coupled algebraic riccati equations

sliding mode control of mimo markovian jump systems this paper addresses the sliding mode control problem for uncertain mimo linear markovian jump systems firstly by using the linear matrix inequality approach sufficient conditions are proposed to guarantee the stochastically asymptotical stability of the system on the sliding surfaces secondly an equivalent control based sliding mode control is proposed such that the closed loop system can be driven onto the desired sliding surfaces in a finite time finally combining with multi step state transition probability the reaching and sliding probabilities are derived for situations where the control force may not be strong enough to ensure the fully asymptotical stability simulation results are presented to illustrate the effectiveness of the proposed design method

on sliding mode control of single input markovian jump systems in this paper an equivalent control based sliding mode control is proposed for single input linear markovian jump systems which guarantees the asymptotical stability furthermore by using the stochastic system theory a multi step state transition conditional probability function is introduced for the continuous markovian process which is used to define the reaching and sliding probabilities furthermore the formulas for calculating reaching and sliding probabilities are derived for situations where the control force may not be strong enough to ensure the fully asymptotical stability extensive simulations are conducted to validate the theoretical results and show the relationship between the control force and reaching and sliding probabilities

a new generalised α scale spaces quadrature filters abstract the scale spaces is a recent theory that open new possibilities of phase based image processing it is a parameterised class 0 1 of linear scale space representations which allows a continuous connection beyond the well known gaussian scale space 1 in this paper we make use of this unified representation to derive new families of band pass quadrature filters built from derivatives and difference of the scale space generating kernels this construction leads to generalised kernel filters including the commonly known families derived from the gaussian and the poisson kernels the properties of each family are first studied and then experiments on one and two dimensional signals are shown to exemplify how the suggested filters can be used for edge detection this work is complemented by an experimental evaluation which demonstrates that the new proposed filters are a good alternative to the commonly used log gabor filter

controllability stabilizability and continuous time markovian jump linear quadratic control consideration is given to the control of continuous time linear systems that possess randomly jumping parameters which can be described by finite state markov processes the relationship between appropriately defined controllability stabilizability properties and the solution of the infinite time jump linear quadratic jlq optimal control problems is also examined although the solution of the continuous time markov jlq problem with finite or infinite time horizons is known only sufficient conditions for the existence of finite cost constant stabilizing controls for the infinite time problem appear in the literature in this paper necessary and sufficient conditions are established these conditions are based on new definitions of controllability observability stabilizability and detectability that are appropriate for continuous time markovian jump linear systems these definitions play the same role for the jlq problem as the deterministic properties do for the linear quadratic regulator lqr problem

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

a novel particle swarm optimisation approach to detecting continuous thin and smooth edges in noisy images detection of continuous edges is a hard problem and most edge detection algorithms produce jagged and thick edges particularly in noisy images this paper firstly presents a novel constrained optimisation model for detecting continuous thin and smooth edges in such images then two particle swarm optimisation based algorithms are applied to search for good solutions these two algorithms utilise two different constraint handling methods penalising and preservation the algorithms are examined and compared with a modified version of the canny algorithm as a gaussian filter based edge detector and the robust rank order rro based algorithm as a statistical based edge detector on two sets of images with different types and levels of noise pratt s figure of merit as a measure of localisation accuracy is used for the comparison of these algorithms experimental results show that the proposed edge detectors are more robust under noisy conditions and their performances are better than the canny and rro algorithms for the images corrupted by impulsive and gaussian noise the proposed algorithm based on the penalising method is faster than the algorithm using the preservation method to handle the constraints

beyond independence an extension of the a contrario decision procedure the a contrario approach is a principled method for making algorithmic decisions that has been applied successfully to many tasks in image analysis the method is based on a background model or null hypothesis for the image this model relies on independence assumptions and characterizes images in which no detection should be made it is often image dependent relying on statistics gathered from the image and therefore adaptive in this paper we propose a generalization for background models which relaxes the independence assumption and instead uses image dependent second order properties the second order properties are accounted for thanks to graphical models the modified a contrario technique is applied to two tasks line segment detection and part based object detection and its advantages are demonstrated in particular we show that the proposed method enables reasonably accurate prediction of the false detection rate with no need for training data

what does the retina know about natural scenes by examining the experimental data on the statistical properties of natural scenes together with retinal contrast sensitivity data we arrive at a first principle theoretical hypothesis for the purpose of retinal processing and its relationship to an animal s environment we argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex as suggested by barlow the extent of this whitening of the input is limited however by the need to suppress input noise our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance they predict an approximate weber law at low spatial frequencies and a de vries rose law at high frequencies assuming that the dominant source of noise is quantum we generate a family of contrast sensitivity curves as a function of mean luminance this family is compared to psychophysical data

the mean square stability probability of h_ infty control of continuous markovian jump systems in this technical note h infty control problem of continuous markovian jump systems is investigated a linear feedback control scheme combined with a state observer design is proposed in the form of linear matrix inequalities which can ensure the systems mean square stability with h infty performance then a multi step state transition conditional probability function is introduced for the continuous markovian process which is used to define the system s mean square stability probability furthermore the formulas for calculating the mean square stability probability are derived for situations where the control force may not be strong enough to ensure the full stability simulation results are presented to show the effectiveness of the theoretical results

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

salient closed boundary extraction with ratio contour we present ratio contour a novel graph based method for extracting salient closed boundaries from noisy images this method operates on a set of boundary fragments that are produced by edge detection boundary extraction identifies a subset of these fragments and connects them sequentially to form a closed boundary with the largest saliency we encode the gestalt laws of proximity and continuity in a novel boundary saliency measure based on the relative gap length and average curvature when connecting fragments to form a closed boundary this new measure attempts to remove a possible bias toward short boundaries we present a polynomial time algorithm for finding the most salient closed boundary we also present supplementary preprocessing steps that facilitate the application of ratio contour to real images we compare ratio contour to two closely related methods for extracting closed boundaries elder and zucker s method based on the shortest path algorithm and williams and thornber s method based on spectral analysis and a strongly connected components algorithm this comparison involves both theoretic analysis and experimental evaluation on both synthesized data and real images

julia sets of certain exponential functions abstract we characterize the julia sets of certain exponential functions we show that the julia sets j f n of f n z n e z n where n 0 is the whole plane c provided that lim k f k n 0 in particular this is true when n are real numbers such that n 1 ne 1 n on the other hand if 0 n 1 ne 1 n then j f n is nowhere dense in c and is the complement of the basin of attraction of the unique real attractive fixed point of f n we then prove similar results for the functions formula where i c 0 1 i n 1 a j 1 1 j n and m n 1

robust h sub spl infin control of uncertain markovian jump systems with time delay this paper is concerned with the robust stochastic stabilizability and robust h sub spl infin disturbance attenuation for a class of uncertain linear systems with time delay and randomly jumping parameters the transition of the jumping parameters is governed by a finite state markov process sufficient conditions on the existence of a robust stochastic stabilizing and spl gamma suboptimal h sub spl infin state feedback controller are presented using the lyapunov functional approach it is shown that a robust stochastically stabilizing h sub spl infin state feedback controller can be constructed through the numerical solution of a set of coupled linear matrix inequalities

output feedback control of markov jump linear systems in continuous time this paper addresses the dynamic output feedback control problem of continuous time markovian jump linear systems the fundamental point in the analysis is an lmi characterization comprising all dynamical compensators that stabilize the closed loop system in the mean square sense the h sub 2 and h sub spl infin norm control problems are studied and the h sub 2 and h sub spl infin filtering problems are solved as a by product