robust recovery of shapes with unknown topology from the dual space in this paper we address the problem of reconstructing an object surface from silhouettes previous works by other authors have shown that based on the principle of duality surface points can be recovered theoretically as the dual to the tangent plane space of the object in practice however the identification of tangent basis in the tangent plane space is not trivial given a set of discretely sampled data this problem is further complicated by the existence of bitangents to the object surface the key contribution of this paper is the introduction of epipolar parameterization in identifying a well defined local tangent basis this extends the applicability of existing dual space reconstruction methods to fairly complicated shapes without making any explicit assumption on the object topology we verify our approach with both synthetic and real world data and compare it both qualitatively and quantitatively with other popular reconstruction algorithms experimental results demonstrate that our proposed approach produces more accurate estimation while maintaining reasonable robustness toward shapes with complex topologies

hybrid adaptive fuzzy controllers with application to robotic systems this paper presents a hybrid adaptive fuzzy control hafc design methodology where the nonlinear system is controlled by a state feedback controller and an adaptive fuzzy controller in the adaptive fuzzy controller an adaptive law is developed to tune a robust gain of the sliding mode controller smc so as to cope with the uncertainties and model errors the proposed design method is applied to investigate the position and tracking control of a two link robot a comparison between the proportional derivative control smc adaptive control and the proposed hafc for a two link robot is made the simulation results demonstrate that by using the hafc the system performances is improved and the system also exhibits stability and robustness

active contours without edges we propose a new model for active contours to detect objects in a given image based on techniques of curve evolution mumford shah 1989 functional for segmentation and level sets our model can detect objects whose boundaries are not necessarily defined by the gradient we minimize an energy which can be seen as a particular case of the minimal partition problem in the level set formulation the problem becomes a mean curvature flow like evolving the active contour which will stop on the desired boundary however the stopping term does not depend on the gradient of the image as in the classical active contour models but is instead related to a particular segmentation of the image we give a numerical algorithm using finite differences finally we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable also the initial curve can be anywhere in the image and interior contours are automatically detected

new design and stability analysis of fuzzy proportional derivative control systems this paper describes the design principle tracking performance and stability analysis of a fuzzy proportional derivative pd controller first the fuzzy pd controller is derived from the conventional continuous time linear pd controller then the fuzzification control rule base and defuzzification in the design of the fuzzy pd controller are discussed in detail the resulting controller is a discrete time fuzzy version of the conventional pd controller which has the same linear structure in the proportional and the derivative parts but has nonconstant gains both the proportional and derivative gains are nonlinear functions of the input signals the new fuzzy pd controller thus preserves the simple linear structure of the conventional pd controller yet enhances its self tuning control capability computer simulation results have demonstrated this advantage of the fuzzy pd controller particularly when the process to be controlled is nonlinear after a detailed stability analysis where a simple and realistic sufficient condition for the bounded input bounded output stability of the overall feedback control system was derived several computer simulation results are compared with the conventional pd controller although the conventional and fuzzy pd controllers are not exactly comparable the authors compare them in order to have a sense of how well the fuzzy pd controller performs for this reason in the simulations several first order and second order linear systems with or without time delays are first used to test the performance of the fuzzy pd controller for step reference inputs the fuzzy pd control systems show remarkable performance as well as if not better than the conventional pd control systems moreover the fuzzy pd controller is compared to the conventional pd controller for a particular second order linear system showing the advantage of the fuzzy pd controller over the conventional one in the sense that in order to obtain the same control performance the conventional pd controller has to employ an extremely large gain while the fuzzy controller uses a reasonably small gain finally in the case of nonlinear systems the authors provide some examples to show that the fuzzy pd controller can track the set points satisfactorily but the conventional pd controller cannot

a robust self tuning scheme for pi and pd type fuzzy controllers proposes a simple but robust model independent self tuning scheme for fuzzy logic controllers flcs here the output scaling factor sf is adjusted online by fuzzy rules according to the current trend of the controlled process the rule base for tuning the output sf is defined on error e and change of error spl delta e of the controlled variable using the most natural and unbiased membership functions mfs the proposed self tuning technique is applied to both pi and pd type flcs to conduct simulation analysis for a wide range of different linear and nonlinear second order processes including a marginally stable system where even the well known ziegler nichols tuned conventional pi or pid controllers fail to provide an acceptable performance due to excessively large overshoot performances of the proposed self tuning flcs are compared with those of their corresponding conventional flcs in terms of several performance measures such as peak overshoot settling time rise time integral absolute error and integral of time multiplied absolute error in addition to the responses due to step set point change and load disturbance and in each case the proposed scheme shows a remarkably improved performance over its conventional counterpart

epipolar plane image analysis an approach to determining structure from motion we present a technique for building a three dimensional description of a static scene from a dense sequence of images these images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial continuity in an individual image the technique utilizes knowledge of the camera motion to form and analyze slices of this solid these slices directly encode not only the three dimensional positions of objects but also such spatiotemporal events as the occlusion of one object by another for straight line camera motions these slices have a simple linear structure that makes them easier to analyze the analysis computes the three dimensional positions of object features marks occlusion boundaries on the objects and builds a three dimensional map of free space in our article we first describe the application of this technique to a simple camera motion and then show how projective duality is used to extend the analysis to a wider class of camera motions and object types that include curved and moving objects

free form object reconstruction from silhouettes occluding edges and texture edges a unified and robust operator based on duality in this paper the duality in differential form is developed between a 3d primal surface and its dual manifold formed by the surface s tangent planes that is each tangent plane of the primal surface is represented as a four dimensional vector that constitutes a point on the dual manifold the iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3d surface that is the dual of the dual goes back to the primal this theorem can be directly used to reconstruct 3d surface from image edges by estimating the dual manifold from these edges in this paper we further develop the work in our original conference papers resulting in the robust differential dual operator we argue that the operator makes good use of the information available in the image data by using both points of intensity discontinuity and their edge directions we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy our algorithm operates on all edges in the images including silhouette edges self occlusion edges and texture edges without distinguishing their types thus resulting in improved accuracy and handling locally concave surface estimation if texture edges are present the algorithm automatically handles various degeneracies and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images evaluating and using the algorithm s sensitivity to noise to determine the accuracy of an estimated 3d point experiments with both synthetic and real images demonstrate that the operator is accurate robust to degeneracies and noise and general for reconstructing free form objects from occluding edges and texture edges detected in calibrated images or video sequences

generalised epipolar constraints in this paper we will discuss structure and motion problems for curved surfaces these will be studied using the silhouettes or apparent contours in the images the problem of determining camera motion from the apparent contours of curved three dimensional surfaces is studied it will be shown how special points called epipolar tangency points or frontier points can be used to solve this problem a generalised epipolar constraint is introduced which applies to points curves as well as to apparent contours of surfaces the theory is developed for both continuous and discrete motion known and unknown orientation calibrated and uncalibrated perspective weak perspective and orthographic cameras results of an iterative scheme to recover the epipolar line structure from real image sequences using only the outlines of curved surfaces is presented a statistical evaluation is performed to estimate the stability of the solution it is also shown how the motion of the camera from a sequence of images can be obtained from the relative motion between image pairs

what s next an interactive next best view approach shape from silhouettes algorithms use either a surface or a volumetric approach a problem with the volumetric approach is that in general we know neither the accuracy of the reconstruction nor where to locate new viewpoints for improving the accuracy in this paper we present a general approach to interactive object specific volumetric algorithms based on a necessary condition for the best possible reconstruction to have been performed the outlined approach can be applied to any class of objects as an example of this approach an interactive algorithm has been implemented for convex polyhedra

uniqueness and estimation of three dimensional motion parameters of rigid objects with curved surfaces two main results are established in this paper first we show that seven point correspondences are sufficient to uniquely determine from two perspective views the three dimensional motion parameters within a scale factor for the translations of a rigid object with curved surfaces the seven points should not be traversed by two planes with one plane containing the origin nor by a cone containing the origin second a set of essential parameters are introduced which uniquely determine the motion parameters up to a scale factor for the translations and can be estimated by solving a set of linear equations which are derived from the correspondences of eight image points the actual motion parameters can subsequently be determined by computing the singular value decomposition svd of a 3 3 matrix containing the essential parameters

three dimensional elastic matching of volumes registering volumes that have been deformed with respect to each other involves recovery of the deformation a 3 d elastic matching algorithm has been developed to use surface information for registering volumes surface extraction is performed in two steps extraction of contours in 2 d image planes using active contours and forming triangular patch surface models from the stack of 2 d contours one volume is modeled as being deformed with respect to another goal volume correspondences between surfaces in the two image volumes are used to warp the deformed volume towards its goal this process of contour extraction surface formation and matching and warping is repeated a number of times with decreasing image volume stiffness as the iterations continue the stretched volume is refined towards its goal volume registration examples of deformed volumes are presented

signal matching through scale space given a collection of similar signals that have been deformed with respect to each other the general signal matching problem is to recover the deformation we formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term the minimization reduces to a dynamic system governed by a set of coupled first order differential equations the dynamic system finds an optimal solution at a coarse scale and then tracks it continuously to a fine scale among the major themes in recent work on visual signal matching have been the notions of matching as constrained optimization of variational surface reconstruction and of coarse to fine matching our solution captures these in a precise succinct and unified form results are presented for one dimensional signals a motion sequence and a stereo pair

affine reconstruction of curved surfaces from uncalibrated views of apparent contours in this paper we consider uncalibrated reconstruction of curved surfaces from apparent contours since apparent contours are not fixed features viewpoint independent we cannot directly apply the recent results of the uncalibrated reconstruction from fixed features we show that nonetheless curved surfaces can be reconstructed up to an affine ambiguity from their apparent contours viewed from uncalibrated cameras with unknown linear translations furthermore we show that even if the reconstruction is nonmetric non euclidean we can still extract useful information for many computer vision applications just from the apparent contours we first show that if the camera motion is linear translation but arbitrary direction and magnitude the epipolar geometry can be recovered from the apparent contours without using any optimization process the extracted epipolar geometry is next used for reconstructing curved surfaces from the deformations of the apparent contours viewed from uncalibrated cameras the result is applied to distinguishing curved surfaces from fixed features in images it is also shown that the time to contact to the curved surfaces can be computed from simple measurements of the apparent contours

parametric reconstruction of generalized cylinders from limb edges the three dimensional 3 d reconstruction of generalized cylinders gcs is an important research field in computer vision one of the main difficulties is that some contour features in images cannot be reconstructed by traditional stereovision because they do not correspond to reflectance discontinuities of surface in space in this paper we present a novel parametric approach for the 3 d reconstruction of circular generalized cylinders cgcs only from the limb edges of cgcs in two images instead of exploiting the invariant and quasiinvariant properties of some specific subclasses of gcs in projections our reconstruction is achieved by some general assumptions on gcs and can therefore be applied to a broader subclass of gcs in order to improve robustness we perform the extraction and labeling of the limb edge interactively and estimate the epipolar geometry between two images by an optimal algorithm then for different types of gcs three kinds of symmetries parallel symmetry skew symmetry and local smooth symmetry are employed to compute the symmetry of limb edges the surface points corresponding to limb edges in images are reconstructed by integrating the recovered epipolar geometry and the properties induced from the assumptions that we make on the gcs finally a homography based method is exploited to further refine the 3 d description of the gc with a coplanar curved axis

facial age estimation based on label sensitive learning and age oriented regression this paper provides a new age estimation approach which distinguishes itself with the following three contributions first we combine distance metric learning and dimensionality reduction to better explore the connections between facial features and age labels second to exploit the intrinsic ordinal relationship among human ages and overcome the potential data imbalance problem a label sensitive concept and several imbalance treatments are introduced in the system training phase finally an age oriented local regression is presented to capture the complicated facial aging process for age determination the simulation results show that our approach achieves the lowest estimation error against existing methods

projective visual hulls this article presents a novel method for computing the visual hull of a solid bounded by a smooth surface and observed by a finite set of cameras the visual hull is the intersection of the visual cones formed by back projecting the silhouettes found in the corresponding images we characterize its surface as a generalized polyhedron whose faces are visual cone patches edges are intersection curves between two viewing cones and vertices are frontier points where the intersection of two cones is singular or intersection points where triples of cones meet we use the mathematical framework of oriented projective differential geometry to develop an image based algorithm for computing the visual hull this algorithm works in a weakly calibrated setting that is it only requires projective camera matrices or equivalently fundamental matrices for each pair of cameras the promise of the proposed algorithm is demonstrated with experiments on several challenging data sets and a comparison to another state of the art method

higher order fuzzy control structure for higher order or time delay systems attempts to develop new structures for higher order fuzzy logic control flc systems which could handle the higher order process and the time delay system as well two different approaches are proposed to construct the higher order flc namely the hierarchical structure and the distributed structure both structures can be designed via the theory of variable structure control vsc the complexity of the structure depends on the order of the process the necessary and sufficient stability condition provided by the vsc theory facilitates the design of the nominal scaling gains the difficulty of the design is to estimate the upper bound of the undesirable dynamics practically this upper bound can be approximated by the peak value of the undesirable dynamics in the steady state period the simulation shows that this approximation is feasible and nominal scaling gains designed can lead to a stable and reasonably good performance the fine tuning can be more easily carried out to provide a better performance from these nominal values instead of from scratch the proposed higher order structure can also be used to control the time delayed process if the delay time is known the structure configuration depends on the approximation of the delay compensation factor a first order approximation is usually accurate enough when the delay is not very long a partially known delay will result in either overcompensation or undercompensation the overcompensation seems better than the undercompensation due to the former contribution to the pid effects the simulation shows the effectiveness of the proposed structure to both the higher order process and the time delay system

a method for detecting damage induced nonlinearities in structures using information theory abstract in this work a new approach is presented for detecting the presence of damage induced nonlinearities in structures from measurements of structural dynamics two different information theoretic it measures the time delayed mutual information and the time delayed transfer entropy are used to provide a probabilistic measure of the coupling between structural components these measures may be used to capture both linear and nonlinear relationships among time series data the formula for both quantities is derived for a linear five degree of freedom system subject to gaussian excitation an algorithm is then described for computing the it metrics from time series data and results are shown to agree with theory we then show that as the coupling between the structure s components changes from linear to nonlinear the information flow can be used to indicate the degree of nonlinearity deviations from a linear model are quantified statistically by generating surrogate data sets that by construction possess only linear second order correlations we then apply the proposed algorithms to both the original data and the surrogates differences in the results are shown to be proportional to the degree of nonlinearity this result is shown to be independent of global changes in stiffness and is therefore unaffected by certain models of environmental variability furthermore the method provides an absolute measure of nonlinearity and therefore does not require a baseline data set for making comparisons this approach is discussed in the context of structural health monitoring where damage is often associated with structural nonlinearity

acquiring 3 d models from sequences of contours this paper explores shape from contour for acquiring 3 d graphics models in this method a continuous sequence of images is taken as an object rotates a smooth convex shape can be estimated instantaneously from its contour and by the first derivative of contour movement trace of contour or contour distribution with time we also analyze shapes that do not satisfy the conditions of smoothness and visibility which are indispensable for modeling an object a region that does not expose as contour yields a nonsmoothness in the tracked contour movement we can thus detect such a region by contour distribution filtering and extract its accurate location by computing the left and right derivatives of the distribution this has not been studied previously these unknown regions are obtained for further investigation using other visual cues a general approach for building a geometrical object model using contours is then described the entire process from silhouettes to a 3 d model is based local computation this is promising for producing shapes in real time our direct goal is to establish 3 d graphics models of human faces for the growing needs of visual communications we have obtained some good results

interpreting line drawings as three dimensional surfaces understanding how line drawings convey tri dimensionality is of fundamental importance in explaining surface perception when photometry is either uninformative or too complex to model analytically we put forward here a computational model for interpreting line drawings as three dimensional surfaces based on constraints on local surface orientation along extremal and discontinuity boundaries specific techniques are described for two key processes recovering the three dimensional conformation of a space curve e g a surface boundary from its two dimensional projection in an image and interpolating smooth surfaces from orientation constraints along extremal boundaries the relevance of the model to a general theory of low level vision is discussed

learning to track the visual motion of contours abstract a development of a method for tracking visual contours is described given an untrained tracker a training motion of an object can be observed over some extended time and stored as an image sequence the image sequence is used to learn parameters in a stochastic differential equation model these are used in turn to build a tracker whose predictor imitates the motion in the training set tests show that the resulting trackers can be markedly tuned to desired curve shapes and classes of motions

a variational approach for the segmentation of the left ventricle in cardiac image analysis in this paper we propose a level set method to segment mr cardiac images our approach is based on a coupled propagation of two cardiac contours and integrates visual information with anatomical constraints the visual information is expressed through a gradient vector flow based boundary component and a region term that aims at best separating the cardiac contours regions according to their global intensity properties in order to deal with misleading visual support an anatomical constraint is considered that couples the propagation of the cardiac contours according to their relative distance the resulting motion equations are implemented using a level set approach and a fast and stable numerical approximation scheme the additive operator splitting encouraging experimental results are provided using real data

eigenfaces vs fisherfaces recognition using class specific linear projection we develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression taking a pattern classification approach we consider each pixel in an image as a coordinate in a high dimensional space we take advantage of the observation that the images of a particular face under varying illumination but fixed pose lie in a 3d linear subspace of the high dimensional image space if the face is a lambertian surface without shadowing however since faces are not truly lambertian surfaces and do indeed produce self shadowing images will deviate from this linear subspace rather than explicitly modeling this deviation we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation our projection method is based on fisher s linear discriminant and produces well separated classes in a low dimensional subspace even under severe variation in lighting and facial expressions the eigenface technique another method based on linearly projecting the image space to a low dimensional subspace has similar computational requirements yet extensive experimental results demonstrate that the proposed fisherface method has error rates that are lower than those of the eigenface technique for tests on the harvard and yale face databases

visually guided ranging from observations of points lines and curves via an identifier based nonlinear observer abstract in this paper we consider the problem of estimating the range information of features on an affine plane in r 3 by observing its image with the aid of a ccd camera wherein we assume that the camera is undergoing a known motion the features considered are points lines and planar curves located on planar surfaces of static objects the dynamics of the moving projections of the features on the image plane have been described as a suitable differential equation on an appropriate feature space this dynamics is used to estimate feature parameters from which the range information is readily available in this paper the proposed identification has been carried out via a newly introduced identifier based observer performance of the observer has been studied via simulation

epipolar geometry from profiles under circular motion addresses the problem of motion estimation from profiles apparent contours of an object rotating on a turntable in front of a single camera a practical and accurate technique for solving this problem from profiles alone is developed it is precise enough to reconstruct the shape of the object no correspondences between points or lines are necessary symmetry of the surface of revolution swept out by the rotating object is exploited to obtain the image of the rotation axis and the homography relating epipolar lines in two views robustly and elegantly these together with geometric constraints for images of rotating objects are used to obtain first the image of the horizon which is the projection of the plane that contains the camera centers and then the epipoles thus fully determining the epipolar geometry of the image sequence the estimation of this geometry by this sequential approach avoids many of the problems found in other algorithms the search for the epipoles by far the most critical step is carried out as a simple 1d optimization parameter initialization is trivial and completely automatic at all stages after the estimation of the epipolar geometry the euclidean motion is recovered using the fixed intrinsic parameters of the camera obtained either from a calibration grid or from self calibration techniques finally the spinning object is reconstructed from its profiles using the motion estimated in the previous stage results from real data are presented demonstrating the efficiency and usefulness of the proposed methods

regularization of inverse visual problems involving discontinuities inverse problems such as the reconstruction problems that arise in early vision tend to be mathematically ill posed through regularization they may be reformulated as well posed variational principles whose solutions are computable standard regularization theory employs quadratic stabilizing functionals that impose global smoothness constraints on possible solutions discontinuities present serious difficulties to standard regularization however since their reconstruction requires a precise spatial control over the smoothing properties of stabilizers this paper proposes a general class of controlled continuity stabilizers which provide the necessary control over smoothness these nonquadratic stabilizing functionals comprise multiple generalized spline kernels combined with noncontinuous continuity control functions in the context of computational vision they may be thought of as controlled continuity constraints these generic constraints are applicable to visual reconstruction problems that involve both continuous regions and discontinuities for which global smoothness constraints fail

self calibration of turntable sequences from silhouettes this paper addresses the problem of recovering both the intrinsic and extrinsic parameters of a camera from the silhouettes of an object in a turntable sequence previous silhouette based approaches have exploited correspondences induced by epipolar tangents to estimate the image invariants under turntable motion and achieved a weak calibration of the cameras it is known that the fundamental matrix relating any two views in a turntable sequence can be expressed explicitly in terms of the image invariants the rotation angle and a fixed scalar it will be shown that the imaged circular points for the turntable plane can also be formulated in terms of the same image invariants and fixed scalar this allows the imaged circular points to be recovered directly from the estimated image invariants and provide constraints for the estimation of the imaged absolute conic the camera calibration matrix can thus be recovered a robust method for estimating the fixed scalar from image triplets is introduced and a method for recovering the rotation angles using the estimated imaged circular points and epipoles is presented using the estimated camera intrinsics and extrinsics a euclidean reconstruction can be obtained experimental results on real data sequences are presented which demonstrate the high precision achieved by the proposed method

a binary level set model and some applications to mumford shah image segmentation in this paper we propose a pde based level set method traditionally interfaces are represented by the zero level set of continuous level set functions instead we let the interfaces be represented by discontinuities of piecewise constant level set functions each level set function can at convergence only take two values i e it can only be 1 or 1 thus our method is related to phase field methods some of the properties of standard level set methods are preserved in the proposed method while others are not using this new method for interface problems we need to minimize a smooth convex functional under a quadratic constraint the level set functions are discontinuous at convergence but the minimization functional is smooth we show numerical results using the method for segmentation of digital images

stabilization of parametric active contours using a tangential redistribution term depending on implementation active contours have been classified as geometric or parametric active contours parametric contours irrespective of representation are known to suffer from the problem of irregular bunching and spacing out of curve points during the curve evolution in a spline based implementation of active contours this leads to occasional formation of loops locally and subsequently the curve blows up due to instabilities in this paper we analyze the reason for this problem and propose a solution to alleviate the same we propose an ordinary differential equation ode for controlling the curve parametrization during evolution by including a tangential force we show that the solution of the proposed ode is bounded we demonstrate the effectiveness of the proposed method for segmentation and tracking tasks on closed as well as open contours

boundary vector field for parametric active contours snakes are widely used in the fields of computer vision and image processing there are a number of shortcomings of the traditional snakes including the capture range problem and the concave object extraction problem gradient vector flow snake enhances the concave object extraction capability however it suffers from high computational requirement in this paper we present a new external force which is generated by a novel interpolation scheme which reduces the computational requirement significantly and at the same time improves the capture range and concave object extraction capability

efficient polyhedral modeling from silhouettes modeling from silhouettes is a popular and useful topic in computer vision many methods exist to compute the surface of the visual hull from silhouettes but few address the problem of ensuring good topological properties of the surface such as manifoldness this article provides an efficient algorithm to compute such a surface in the form of a polyhedral mesh it relies on a small number of geometric operations to compute a visual hull polyhedron in a single pass such simplicity enables the algorithm to combine the advantages of being fast producing pixel exact surfaces and repeatably yield manifold and watertight polyhedra in general experimental conditions with real data as verified with all datasets tested the algorithm is fully described its complexity analyzed and modeling results given

the local projective shape of smooth surfaces and their outlines this article examines projectively invariant local geometric properties of smooth curves and surfaces oriented projective differential geometry is proposed as a general framework for establishing such invariants and characterizing the local projective shape of surfaces and their outlines it is applied to two problems 1 the projective generalization of koenderink s famous characterization of convexities concavities and inflections of the apparent contours of solids bounded by smooth surfaces and 2 the image based construction of rim meshes which provide a combinatorial description of the arrangement induced on the surface of an object by the contour generators associated with multiple cameras observing it

b spline snakes a flexible tool for parametric contour detection we present a novel formulation for b spline snakes that can be used as a tool for fast and intuitive contour outlining we start with a theoretical argument in favor of splines in the traditional formulation by showing that the optimal curvature constrained snake is a cubic spline irrespective of the form of the external energy field unfortunately such regularized snakes suffer from slow convergence speed because of a large number of control points as well as from difficulties in determining the weight factors associated to the internal energies of the curve we therefore propose an alternative formulation in which the intrinsic scale of the spline model is adjusted a priori this leads to a reduction of the number of parameters to be optimized and eliminates the need for internal energies i e the regularization term in other words we are now controlling the elasticity of the spline implicitly and rather intuitively by varying the spacing between the spline knots the theory is embedded into a multiresolution formulation demonstrating improved stability in noisy image environments validation results are presented comparing the traditional snake using internal energies and the proposed approach without internal energies showing the similar performance of the latter several biomedical examples of applications are included to illustrate the versatility of the method

geometrically induced force interaction for three dimensional deformable models in this paper we propose a novel 3 d deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions this external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient the evolution of the deformable model is solved using the level set method so that topological changes are handled automatically the relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves the geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries and it gives the deformable model a high invariancy in initialization configurations the voxel interactions across the whole image domain provide a global view of the object boundary representation giving the external force a long attraction range the bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross boundary initializations and facilitates the handling of weak edges and broken boundaries in addition we show that by enhancing the geometrical interaction field with a nonlocal edge preserving algorithm the new deformable model can effectively overcome image noise we provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images and show that the proposed method achieves significant improvements against existing image gradient techniques

damage identification of nonlinear structural systems the present investigation develops methods for the identie cation of highly localized structural damages in nonlinear structures the damage is dee ned as either a reduction of stiffness or a change of restoring force characteristics from linear undamaged state to weak nonlinear damaged state one method for identifying both the location and type of damages is the location vector method lvm the other method is for quantifying the damage the lvm requires only the modal data from the e rst few fundamental modes the second method is based on fast fourier transform fft and the least squares method under the assumptions that the location of the damages can be identie ed and their responses can be measured by testing without loss of generality the methods are illustrated by a e ve degree of freedom dufe ng s nonlinear system measurement data are simulated in the time domain and in the frequency domain by using the runge kutta method and fft respectively the robustness and effectiveness of the methods are examined by using a simulated output time history contaminated by a 5 white noise which represents more realistic levels of measurement errors

robust shape recovery from occluding contours using a linear smoother recovering the shape of an object from two views fails at occluding contours of smooth objects because the extremal contours are view dependent for three or more views shape recovery is possible and several algorithms have recently been developed for this purpose we present a new approach to the multiframe stereo problem that does not depend on differential measurements in the image which may be noise sensitive instead we use a linear smoother to optimally combine all of the measurements available at the contours and other edges in all of the images this allows us to extract a robust and reasonably dense estimate of surface shape and to integrate shape information from both surface markings and occluding contours results are presented which empirically show that in the presence of noise smoothing over more than three views reduces the error even when the epipolar curve is nonplanar

new methodology for analytical and optimal design of fuzzy pid controllers describes a methodology for the systematic design of fuzzy pid controllers based on theoretical fuzzy analysis and genetic based optimization an important feature of the proposed controller is its simple structure it uses a one input fuzzy inference with three rules and at most six tuning parameters a closed form solution for the control action is defined in terms of the nonlinear tuning parameters the nonlinear proportional gain is explicitly derived in the error domain a conservative design strategy is proposed for realizing a guaranteed pid performance gpp fuzzy controller this strategy suggests that a fuzzy pid controller should be able to produce a linear function from its nonlinearity tuning of the system the proposed pid system is able to produce a close approximation of a linear function for approximating the gpp system this gpp system incorporated with a genetic solver for the optimization will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria two indexes linearity approximation index lai and nonlinearity variation index nvi are suggested for evaluating the nonlinear design of fuzzy controllers the proposed control system has been applied to several first order second order and fifth order processes simulation results show that the proposed fuzzy pid controller produces superior control performance to the conventional pid controllers particularly in handling nonlinearities due to time delay and saturation

optimal ramp edge detection using expansion matching in practical images ideal step edges are actually transformed into ramp edges due to the general low pass filtering nature of imaging systems this paper discusses the application of the expansion matching exm method for optimal ramp edge detection exm optimizes a novel matching criterion called discriminative signal to noise ratio dsnr and has been shown to robustly recognize templates under conditions of noise severe occlusion and superposition we show that our ramp edge detector performs better than the ramp detector obtained from canny s criteria in terms of dsnr and is relatively easier to derive for various noise levels and slopes

elastic model based segmentation of 3 d neuroradiological data sets this paper presents a new technique for the automatic model based segmentation of three dimensional 3 d objects from volumetric image data the development closely follows the seminal work of taylor and cootes on active shape models but is based on a hierarchical parametric object description rather than a point distribution model the segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of shape models geometric models are derived from a sample set of image data which have been segmented by experts the surfaces of these binary objects are converted into parametric surface representations which are normalized to get an invariant object centered coordinate system surface representations are expanded into series of spherical harmonics which provide parametric descriptions of object shapes it is shown that invariant object surface parametrization provides a good approximation to automatically determine object homology in terms of sets of corresponding sets of surface points gray level information near object boundaries is represented by 1 d intensity profiles normal to the surface considering automatic segmentation of brain structures as their driving application the authors choice of coordinates for object alignment was the well accepted stereotactic coordinate system major variation of object shapes around the mean shape also referred to as shape eigenmodes are calculated in shape parameter space rather than the feature space of point coordinates segmentation makes use of the object shape statistics by restricting possible elastic deformations into the range of the training shapes the mean shapes are initialized in a new data set by specifying the landmarks of the stereotactic coordinate system the model elastically deforms driven by the displacement forces across the object s surface which are generated by matching local intensity profiles elastical deformations are limited by setting bounds for the maximum variations in eigenmode space the technique has been applied to automatically segment left and right hippocampus thalamus putamen and globus pallidus from volumetric magnetic resonance scans taken from schizophrenia studies the results have been validated by comparison of automatic segmentation with the results obtained by interactive expert segmentation

global minimum for active contour models a minimal path approach a new boundary detection approach for shape modeling is presented it detects the global minimum of an active contour model s energy between two end points initialization is made easier and the curve is not trapped at a local minimum by spurious edges we modify the snake energy by including the internal regularization term in the external potential term our method is based on finding a path of minimal length in a riemannian metric we then make use of a new efficient numerical method to find this shortest path it is shown that the proposed energy though based only on a potential integrated along the curve imposes a regularization effect like snakes we explore the relation between the maximum curvature along the resulting contour and the potential generated from the image the method is capable to close contours given only one point on the objects boundary by using a topology based saddle search routine we show examples of our method applied to real aerial and medical images

detection and segmentation of generic shapes based on affine modeling of energy in eigenspace this paper presents a novel approach for detection and segmentation of man made generic shapes in cluttered images the set of shapes to be detected are members of affine transformed versions of basic geometric shapes such as rectangles circles etc the shape set is represented by its vectorial edge map transformed over a wide range of affine parameters we use vectorial boundary instead of regular boundary to improve the robustness to noise background clutter and partial occlusion our approach consists of a detection stage and a verification stage in the detection stage we first derive the energy from the principal eigenvectors of the set next an a posteriori probability map of energy distribution is computed from the projection of the edge map representation in a vectorial eigen space local peaks of the posterior probability map are located and indicate candidate detections we use energy probability based detection since we find that the underlying distribution is not gaussian and resembles a hypertoroid in the verification stage each candidate is verified using a fast search algorithm based on a novel representation in angle space and the corresponding pose information of the detected shape is obtained the angular representation used in the verification stage yields better results than a euclidean distance representation experiments are performed in various interfering distortions and robust detection and segmentation are achieved

surface shape from the deformation of apparent contours the spatiotemporal analysis of deforming silhouettes apparent contours is here extended using the mathematics of perspective projections and tools from differential geometry analysis of the image motion of a silhouette or apparent contour enables computation of local surface curvature along the corresponding contour generator on the surface assuming viewer motion is known to perform the analysis a spatiotemporal parameterization of image curve motion is needed but is underconstrained a manifestation of the well known aperture problem it is shown that an epipolar parameterization is most naturally matched to the recovery of surface curvature

automatic model construction and pose estimation from photographs using triangular splines this paper addresses the automatic construction of complex spline object models from a few photographs our approach combines silhouettes from registered images to construct a g sup 1 continuous triangular spline approximation of an object with unknown topology we apply a similar optimization procedure to estimate the pose of a modeled object from a single image experimental examples of model construction and pose estimation are presented for several complex objects

on methods for improving performance of pi type fuzzy logic controllers to improve limitations of fuzzy pi controller especially when applied to high order systems we propose two types of fuzzy logic controllers that take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional fuzzy pi controllers the structures of the proposed controller were motivated by the problems of fuzzy pi controllers that they generally give inevitable overshoot when one tries to reduce rise time of response especially when a system of order higher than one is under consideration since the undesirable characteristics of the fuzzy pi controller are caused by integrating operation of the controller even though the integrator itself is introduced to to overcome steady state error in response we propose two fuzzy controllers that fuzzily clear out integrated quantities according to situation the first controller determines the fuzzy resetting rate by situations described fuzzily by error and error rate and the second one by error and control input the two structures both give reduced rise time as well as small overshoot

threshold dynamics for the piecewise constant mumford shah functional we propose an efficient algorithm for minimizing the piecewise constant mumford shah functional of image segmentation it is based on the threshold dynamics of merriman bence and osher for evolving an interface by its mean curvature we show that a very fast minimization can be achieved by alternating the solution of a linear parabolic partial differential equation and simple thresholding

camera calibration from images of spheres this paper introduces a novel approach for solving the problem of camera calibration from spheres by exploiting the relationship between the dual images of spheres and the dual image of the absolute conic iac it is shown that the common pole and polar with regard to the conic images of two spheres are also the pole and polar with regard to the iac this provides two constraints for estimating the iac and hence allows a camera to be calibrated from an image of at least three spheres experimental results show the feasibility of the proposed approach

design of a single input fuzzy logic controller and its properties abstract we suggest a simple but powerful flc fuzzy logic controller design method using a single fuzzy input variable which is equivalent to the pseudo sliding mode controller input variables of conventional flcs are mostly the error e and the change of error dot e regardless of the complexity of controlled plants a rule table is then constructed in a two dimensional input space the output of fuzzy inference is applied to the plant as the control input u or the change of control input u this scheme came from concepts of linear pd proportional derivative and pi proportional integral controllers we found that rule tables of most flcs have skew symmetry property and the absolute magnitude of the control input vbuvb or vb uvb is proportional to the distance from its main diagonal line in the normalized input space considering these facts we derive a new variable called the signed distance which is a sole fuzzy input variable in our simple flc called single input flc s flc the s flc has many advantages the total number of rules is greatly reduced compared to two dimensional flcs and hence generations and tuning of control rules are easy control performance is nearly the same as that of conventional flcs we also show that this s flc is equivalent to the pseudo smc sliding mode controller and hence the stability is guaranteed using the lyapunov stability the performance of s flc is revealed via computer simulations using a nonlinear plant

full volumetric descriptions from three intensity images we address the problem of recovering high level volumetric and segmented or part based descriptions of objects from intensity images as input we use three closely spaced images of an object and recover descriptions based on generalized cylinders gcs we start by extracting a hierarchy of groups from contour images in the three views grouping is based on proximity parallelism and symmetry the groups in the three views are matched and their contours are labeled as true edges we then infer the gc axis its cross section and the scaling function the cross section is recovered if seen in the images else it is inferred using the visible surfaces and gc properties we consider groups with true edges limb edges or a combination of both the coarse volumetric descriptions obtained are refined to include surface details as seen in the intensity images we demonstrate results on real images of moderately complex objects with texture and shadows

epipolar parameterization for reconstructing 3d rigid curve abstract this paper describes a new method for reconstructing a 3d rigid curve from a sequence of uncalibrated images using 31 epipolar parameterization the approach can be divided into the following two steps first a nonlinear discrete method is presented for point by point reconstruction of the curve instead of whole curve reconstruction projective and euclidean geometric tools are used second the parametric representation of the curve is defined by 31 b spline curves a linear method is proposed to interpolate the reconstructed points to obtain a complete curve thus it is proved that the 31 curve interpolation is equivalent to determining a set of control points of 3d regularized b spline curves it is shown that the 3d epipolar parameterization is an efficient method for reconstructing a 31 curve from image sequences experimental results are presented for real data

robust structure and motion from outlines of smooth curved surfaces this paper addresses the problem of estimating the motion of a camera as it observes the outline or apparent contour of a solid bounded by a smooth surface in successive image frames in this context the surface points that project onto the outline of an object depend on the viewpoint and the only true correspondences between two outlines of the same object are the projections of frontier points where the viewing rays intersect in the tangent plane of the surface in turn the epipolar geometry is easily estimated once these correspondences have been identified given the apparent contours detected in an image sequence a robust procedure based on ransac and a voting strategy is proposed to simultaneously estimate the camera configurations and a consistent set of frontier point projections by enforcing the redundancy of multiview epipolar geometry the proposed approach is in principle applicable to orthographic weak perspective and affine projection models experiments with nine real image sequences are presented for the orthographic projection case including a quantitative comparison with the ground truth data for the six data sets for which the latter information is available sample visual hulls have been computed from all image sequences for qualitative evaluation

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

motion estimation via dynamic vision estimating the three dimensional motion of an object from a sequence of projections is of paramount importance in a variety of applications in control and robotics such as autonomous navigation manipulation servo tracking docking planning and surveillance although visual motion estimation is an old problem the first formulations date back to the beginning of the century only recently have tools from nonlinear systems estimation theory hinted at acceptable solutions in this paper the authors formulate the visual motion estimation problem in terms of identification of nonlinear implicit systems with parameters on a topological manifold and propose a dynamic solution either in the local coordinates or in the embedding space of the parameter manifold such a formulation has structural advantages over previous recursive schemes since the estimation of motion is decoupled from the estimation of the structure of the object being viewed and therefore it is possible to handle occlusions in a principled way

differential and numerically invariant signature curves applied to object recognition we introduce a new paradigm the differential invariant signature curve or manifold for the invariant recognition of visual objects a general theorem of e cartan implies that two curves are related by a group transformation if and only if their signature curves are identical the important examples of the euclidean and equi affine groups are discussed in detail secondly we show how a new approach to the numerical approximation of differential invariants based on suitable combination of joint invariants of the underlying group action allows one to numerically compute differential invariant signatures in a fully group invariant manner applications to a variety of fundamental issues in vision including detection of symmetries visual tracking and reconstruction of occlusions are discussed

a self tuning fuzzy pi controller abstract a robust self tuning pi type fuzzy logic controller flc is presented depending on the process trend the output scaling factor sf of the controller is modified on line by an updating factor the value of is determined from a rule base defined on error e and change of error e of the controlled variable the proposed self tuning controller is designed using a very simple control rule base and the most natural and unbiased membership functions mfs symmetric triangles with equal base and 50 overlap with neighboring mfs the proposed scheme is tested for a wide variety of processes including a marginally stable system with different values of dead time performance comparison between the conventional pi type and proposed self tuning flcs is made in terms of several performance criteria such as peak overshoot settling time rise time integral absolute error and integral of time multiplied absolute error in addition to the responses due to step input and load disturbance results for various processes show that the proposed flc outperforms its conventional counterpart in each case

design of a hybrid fuzzy logic proportional plus conventional integral derivative controller presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral derivative fuzzy p id controller in an incremental form this controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional pid controller by using the bounded input bounded output small gain theorem the sufficient condition for stability of this controller is derived based on the condition we modify the ziegler and nichols approach to design the fuzzy p id controller in this case the stability of a system remains unchanged after the pid controller is replaced by the fuzzy p id controller without modifying the original controller parameters when a plant can be described by any modeling method the fuzzy p id controller can be determined by an optimization technique finally this controller is used to control a nonlinear system numerical simulation results demonstrate the effectiveness of the fuzzy p id controller in comparison with the conventional pid controller especially when the controlled object operates under uncertainty or in the presence of a disturbance

symmetry seeking models and 3d object reconstruction we propose models of 3d shape which may be viewed as deformable bodies composed of simulated elastic material in contrast to traditional purely geometric models of shape deformable models are active their shapes change in response to externally applied forces we develop a deformable model for 3d shape which has a preference for axial symmetry symmetry is represented even though the model does not belong to a parametric shape family such as generalized cylinders rather a symmetry seeking property is designed into internal forces that constrain the deformations of the model we develop a framework for 3d object reconstruction based on symmetry seeking models instances of these models are formed from monocular image data through the action of external forces derived from the data the forces proposed in this paper deform the model in space so that the shape of its projection into the image plane is consistent with the 2d silhouette of an object of interest the effectiveness of our approach is demonstrated using natural images

a theory of shape by space carving in this paper we consider the problem of computing the 3d shape of an unknown arbitrarily shaped scene from multiple photographs taken at known but arbitrarily distributed viewpoints by studying the equivalence class of all 3d shapes that reproduce the input photographs we prove the existence of a special member of this class the photo hull that 1 can be computed directly from photographs of the scene and 2 subsumes all other members of this class we then give a provably correct algorithm called space carving for computing this shape and present experimental results on complex real world scenes the approach is designed to 1 capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints and 2 account for the complex interactions between occlusion parallax shading and their view dependent effects on scene appearance

global regularizing flows with topology preservation for active contours and polygons active contour and active polygon models have been used widely for image segmentation in some applications the topology of the object s to be detected from an image is known a priori despite a complex unknown geometry and it is important that the active contour or polygon maintain the desired topology in this work we construct a novel geometric flow that can be added to image based evolutions of active contours and polygons in order to preserve the topology of the initial contour or polygon we emphasize that unlike other methods for topology preservation the proposed geometric flow continually adjusts the geometry of the original evolution in a gradual and graceful manner so as to prevent a topology change long before the curve or polygon becomes close to topology change the flow also serves as a global regularity term for the evolving contour and has smoothness properties similar to curvature flow these properties of gradually adjusting the original flow and global regularization prevent geometrical inaccuracies common with simple discrete topology preservation schemes the proposed topology preserving geometric flow is the gradient flow arising from an energy that is based on electrostatic principles the evolution of a single point on the contour depends on all other points of the contour which is different from traditional curve evolutions in the computer vision literature

following cusps it is known that the deformation of the apparent contours of a surface under perspective projection and viewer motion enable the recovery of the geometry of the surface for example by utilising the epipolar parametrization these methods break down with apparent contours that are singular i e with cusps in this paper we study this situation and show how nevertheless the surface geometry including the gauss curvature and mean curvature of the surface can be recovered by following the cusps indeed the formulae are much simpler in this case and require lower spatio temporal derivatives than in the general case of nonsingular apparent contours we also show that following cusps does not by itself provide us with information on viewer motion

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

necessary conditions for some typical fuzzy systems as universal approximators abstract we investigate necessary conditions for general single input single output fuzzy systems and a class of typical multiple input single output fuzzy systems as universal approximators for continuous functions defined on compact domains with arbitrarily small uniform approximation error bounds considering the case where the only available data about the continuous function to be approximated are a finite set of its extrema we have established some necessary conditions for the fuzzy systems to be universal approximators of the function the conditions can be used practically to determine input fuzzy sets output fuzzy sets and fuzzy rules of the fuzzy systems furthermore these necessary conditions provide a basis for insightful analysis of the strengths as well as the limitations of the fuzzy systems the main strength is that only a small number of fuzzy rules may be needed to uniformly approximate continuous functions that have a complicated formulation but a relatively small number of extrema the limitation is that in order to approximate highly oscillatory continuous functions the number of fuzzy rules must be large 1997 elsevier science ltd

eigentracking robust matching and tracking of articulated objects using a view based representation this paper describes an approach for tracking rigid and articulated objects using a view based representation the approach builds on and extends work on eigenspace representations robust estimation techniques and parameterized optical flow estimation first we note that the least squares image reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation second we define a subspace constancy assumption that allows us to exploit techniques for parameterized optical flow estimation to simultaneously solve for the view of an object and the affine transformation between the eigenspace and the image to account for large affine transformations between the eigenspace and the image we define a multi scale eigenspace representation and a coarse to fine matching strategy finally we use these techniques to track objects over long image sequences in which the objects simultaneously undergo both affine image motions and changes of view in particular we use this eigentracking technique to track and recognize the gestures of a moving hand

global surface reconstruction by purposive control of observer motion abstract what viewpoint control strategies are important for performing global visual exploration tasks such as searching for specific surface markings building a global model of an arbitrary object or recognizing an object in this paper we consider the task of purposefully controlling the motion of an active monocular observer in order to recover a global description of a smooth arbitrarily shaped object we formulate global surface reconstruction as the task of controlling the motion of the observer so that the visible rim slides over the maximal connected reconstructible surface regions intersecting the visible rim at the initial viewpoint we show that these regions are bounded by a subset of the visual event curves defined on the surface by studying the epipolar parameterization we develop two basic strategies that allow reconstruction of a surface region around any point in a reconstructible surface region these strategies control viewpoint to achieve and maintain a well defined geometric relationship with the object s surface rely only on information extracted directly from images e g tangents to the occluding contour and are simple enough to be performed in real time we then show how global surface reconstruction can be provably achieved by 1 appropriately integrating these strategies to iteratively grow the reconstructed regions and 2 obeying four simple rules

interpreting line drawings of curved objects in this thesis we study the problem of interpreting line drawings of scenes composed or opaque regular solid objects bounded by piecewise smooth surfaces with no markings or texture on them it is assumed that the line drawing has been formed by orthographic projection of such a scene under general viewpoint that the line drawing is error free and that there are no lines due to shadows or specularities our definition implicitly excludes laminae wires and the apices of cones a major component of the interpretation of line drawings is line labelling by line labelling we mean a classification of each image curve as corresponding to either a depth or orientation discontinuity in the scene and b further subclassification of each kind of discontinuity for a depth discontinuity we determine whether it is a limb a locus of points on the surface where the line of sight is tangent to the surface or an occluding edge a tangent plane discontinuity of the surface for an orientation discontinuity we determine whether it corresponds to a convex or concave edge this thesis presents the first mathematically rigorous scheme for labelling line drawings of the class of scenes described by analysing the projection of the neighborhoods of different kinds of points on a piecewise smooth surface we are able to catalog all local labelling possibilities for the different types of junctions in a line drawing an algorithm is developed which utilizes this catalog to determine all legal labellings of the line drawing a local minimum complexity rule at each vertex select those labellings which correspond to the minimum number of faces meeting at the vertex is used in order to prune highly counter intuitive interpretations the labelling scheme was implemented and tested on a number of line drawings the labellings obtained are few and by and large in accordance with human interpretations

information theoretic active polygons for unsupervised texture segmentation curve evolution models used in image segmentation and based on image region information usually utilize simple statistics such as means and variances hence can not account for higher order nature of the textural characteristics of image regions in addition the object delineation by active contour methods results in a contour representation which still requires a substantial amount of data to be stored for subsequent multimedia applications such as visual information retrieval from databases polygonal approximations of the extracted continuous curves are required to reduce the amount of data since polygons are powerful approximators of shapes for use in later recognition stages such as shape matching and coding the key contribution of this paper is the development of a new active contour model which nicely ties the desirable polygonal representation of an object directly to the image segmentation process this model can robustly capture texture boundaries by way of higher order statistics of the data and using an information theoretic measure and with its nature of the ordinary differential equations this new variational texture segmentation model is unsupervised since no prior knowledge on the textural properties of image regions is used another contribution in this sequel is a new polygon regularizer algorithm which uses electrostatics principles this is a global regularizer and is more consistent than a local polygon regularization in preserving local features such as corners

probabilistic visual learning for object representation we present an unsupervised technique for visual learning which is based on density estimation in high dimensional spaces using an eigenspace decomposition two types of density estimates are derived for modeling the training data a multivariate gaussian for unimodal distributions and a mixture of gaussians model for multimodal distributions those probability densities are then used to formulate a maximum likelihood estimation framework for visual search and target detection for automatic object recognition and coding our learning technique is applied to the probabilistic visual modeling detection recognition and coding of human faces and nonrigid objects such as hands

motion estimation in image sequences using the deformation of apparent contours the problem of determining the camera motion from apparent contours or silhouettes of a priori unknown curved 3d surfaces is considered in a sequence of images it is shown how to use the generalized epipolar constraint on apparent contours one such constraint is obtained for each epipolar tangency point in each image pair an accurate algorithm for computing the motion is presented based on a maximum likelihood estimate it is shown how to generate initial estimates on the camera motion using only the tracked contours it is also shown that in theory the motion can be calculated from the deformation of a single contour the algorithm has been tested on several real image sequences for both euclidean and projective reconstruction the resulting motion estimate is compared to motion estimates calculated independently using standard feature based methods the motion estimate is also used to classify the silhouettes as curves or apparent contours the statistical evaluation shows that the technique gives accurate and stable results

introducing a new problem shape from silhouette when the relative positions of the viewpoints is unknown 3d shapes can be reconstructed from 2d silhouettes by back projecting them from the corresponding viewpoints and intersecting the resulting solid cones however in many practical cases as observing an aircraft or an asteroid the positions of the viewpoints with respect to the object are not known in these cases the relative position of the solid cones is not known and the intersection cannot be performed the purpose of this paper is introducing and stating in a theoretical framework the problem of understanding 3d shapes from silhouettes when the relative positions of the viewpoints are unknown the results presented provide a first insight into the problem in particular the case of orthographic viewing directions parallel to the same plane is thoroughly discussed and sets of inequalities are presented which allow determining objects compatible with the silhouettes

on orbit damage assessment for large space structures the need for monitoring the dynamic characteristics of large structural systems for the purpose of assessing the potential degradation of structural properties has been established this paper develops a theory for assessing the occurrence location and extent of potential damage utilizing on orbit response measurements the feasibility of the method is demonstrated using a simple structural system as an example

implicit meshes for effective silhouette handling using silhouettes in uncontrolled environments typically requires handling occlusions as well as changing or cluttered backgrounds which limits the applicability of most silhouette based methods for the purpose of 3 d shape modeling we show that representing generic 3 d surfaces as implicit surfaces lets us effectively address these issues this desirable behavior is completely independent from the way the surface deformations are parame trized to show this we demonstrate our technique in three very different cases modeling the deformations of a piece of paper represented by an ordinary triangulated mesh reconstruction and tracking a person s shoulders whose deformations are expressed in terms of dirichlet free form deformations reconstructing the shape of a human face parametrized in terms of a principal component analysis model

curve and surface duals and the recognition of curved 3d objects from their silhouettes this article addresses the problem of recognizing a solid bounded by a smooth surface in a single image the proposed approach is based on a new representation for two and three dimensional shapes called their signature that exploits the close relationship between the dual of a surface and the dual of its silhouette in weak perspective images objects are modeled by rotating them in front of a camera without any knowledge of or constraints on their motion the signatures of their silhouettes are concatenated into a single object signature to recognize an object from novel viewpoint other than those used during modeling the signature of the contours extracted from a test photograph is matched to the signatures of all modeled objects signatures this approach has been implemented and recognition examples are presented

1d camera geometry and its application to the self calibration of circular motion sequences this paper proposes a novel method for robustly recovering the camera geometry of an uncalibrated image sequence taken under circular motion under circular motion all the camera centers lie on a circle and the mapping from the plane containing this circle to the horizon line observed in the image can be modelled as a 1d projection a 2times2 homography is introduced in this paper to relate the projections of the camera centers in two 1d views it is shown that the two imaged circular points of the motion plane and the rotation angle between the two views can be derived directly from such a homography this way of recovering the imaged circular points and rotation angles is intrinsically a multiple view approach as all the sequence geometry embedded in the epipoles is exploited in the estimation of the homography for each view pair this results in a more robust method compared to those computing the rotation angles using adjacent views only the proposed method has been applied to self calibrate turntable sequences using either point features or silhouettes and highly accurate results have been achieved

acquiring 3d models of non rigid moving objects from time and viewpoint varying image sequences a step toward left ventricle recovery this paper describes a method for the accurate recovery of time varying 3d shapes with known cycle from images with different viewpoints as well as times aiming at the recovery of the left ventricular shapes our recovery method is based on the integration of apparent contours from different viewpoints we perform direct fitting to a 4d closed surface model based on b splines so as to deal with fragmented contours such as extracted from x ray cineangiocardiograms the method is quantitatively evaluated using synthesized and real image sequences

a wavelet frame based image force model for active contouring algorithms this paper proposes a directional image force dif for active contouring dif is the inner product of the zero crossing strength zcs of wavelet frame coefficients and the normal of a snake by representing strength and orientation of edges at multiple resolution levels dif markedly improves the immunity of snakes to noise and convexity

a simple algorithm for nearest neighbor search in high dimensions the problem of finding the closest point in high dimensional spaces is common in pattern recognition unfortunately the complexity of most existing search algorithms such as k d tree and r tree grows exponentially with dimension making them impractical for dimensionality above 15 in nearly all applications the closest point is of interest only if it lies within a user specified distance spl epsiv we present a simple and practical algorithm to efficiently search for the nearest neighbor within euclidean distance spl epsiv the use of projection search combined with a novel data structure dramatically improves performance in high dimensions a complexity analysis is presented which helps to automatically determine spl epsiv in structured problems a comprehensive set of benchmarks clearly shows the superiority of the proposed algorithm for a variety of structured and unstructured search problems object recognition is demonstrated as an example application the simplicity of the algorithm makes it possible to construct an inexpensive hardware search engine which can be 100 times faster than its software equivalent a c implementation of our algorithm is available

pictorial recognition of objects employing affine invariance in the frequency domain describes an efficient approach to pose invariant pictorial object recognition employing spectral signatures of image patches that correspond to object surfaces which are roughly planar based on singular value decomposition svd the affine transform is decomposed into slant tilt swing scale and 2d translation unlike previous log polar representations which were not invariant to slant our log log sampling configuration in the frequency domain yields complete affine invariance the images are preprocessed by a novel model based segmentation scheme that detects and segments objects that are affine similar to members of a model set of basic geometric shapes the segmented objects are then recognized by their signatures using multidimensional indexing in a pictorial dataset represented in the frequency domain experimental results with a dataset of 26 models show 100 percent recognition rates in a wide range of 3d pose parameters and imaging degradations 0 360 spl deg swing and tilt 0 82 spl deg of slant more than three octaves in scale change window limited translation high noise levels 0 db and significantly reduced resolution 1 5

submatrix approach to stiffness matrix correction using modal test data an efficient method six stiffness matrix correction to match modal testing data is presented significant reduction of unknown parameters is achieved by grouping the elements of the same stiffness characteristics and representing them as a multiplication of a scaling factor and a submatrix a concise formulation to identify the scaling factors is found by utilizing a least squares solution the formulation also incorporates a capability of reducing finite element mass and stiffness matrices to test degrees of freedom for a direct analysis experiment correlation

development of an automated fuzzy logic based expert system for unmanned landing abstract fuzzy control is not just another buzzword nor is it a passing fashion in control theory but a promising novel branch of control theory which can and should open up new vistas of applications applied fuzzy logic allows the design of knowledge based controllers without requiring a precise model it provides us with highly flexible and adjustable mechanisms and may be very useful when accelerated development is required fuzzy logic provides approximate solutions to complicated control problems via the use of fuzzy rule base expert systems this paper focusses on the nonlinear control actions of such a fuzzy controller fc operating effectively in the automated landing of unmanned aircrafts the fuzzy controller controls successfully three dimensional flight path sink rate and angular altitudes to allow safe landing of the unmanned aircraft applied fuzzy logic has been used to design a high siq system intelligent quotient mechanism which combines elements of human pilot intelligence with landing knowledge the system was tested in simulations as well as in real life landings in which performance demonstrate clearly significant potential of fuzzy logic for the intelligent control of similar unmanned activities