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

deformable contours modeling and extraction this paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images we propose a global contour model based on a stable and regenerative shape matrix which is invariant and unique under rigid motions combined with markov random field to model local deformations this yields prior distribution that exerts influence over a global model while allowing for deformations we then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model we discuss pertinent issues in shape training energy minimization line search strategies minimax regularization and initialization by generalized hough transform finally we present experimental results and compare its performance to rigid template matching

dynamical quantities and their numerical analysis by saddle periodic orbits we study estimators for dynamical quantities such as the topological entropy and the topological pressure which are based on numerical computations on periodic orbits for the particular case of the henon family for some parameter ranges we find a reasonable convergence of the entropy the pressure and birkhoff averages of test functions however pointing out possible pitfalls of such an analysis we show that the evaluation by means of saddle orbits alone can be misleading if for example chaotic saddles and attractors coexist

local and cluster critical dynamics of the 3d random site ising model abstract we present the results of monte carlo simulations for the critical dynamics of the three dimensional site diluted quenched ising model three different dynamics are considered these correspond to the local update metropolis scheme as well as to the swendsen wang and wolff cluster algorithms the lattice sizes of l 10 96 are analysed by a finite size scaling technique the site dilution concentration p 0 85 was chosen to minimize the correction to scaling effects we calculate numerical values of the dynamical critical exponents for the integrated and exponential autocorrelation times for energy and magnetization as expected cluster algorithms are characterized by lower values of dynamical critical exponent than the local one also in the case of dilution critical slowing down is more pronounced for the metropolis algorithm however the striking feature of our estimates is that they suggest that dilution leads to decrease of the dynamical critical exponent for the cluster algorithms this phenomenon is quite opposite to the local dynamics where dilution enhances critical slowing down

cellular automata as microcanonical simulators the deterministic q2r cellular automation has been tested by many authors as a fast algorithm to simulate the ising model in the microcanonical ensemble however the magnetic susceptibility curve measured from the fluctuations of the magnetization is found to be far below the expected results inside the ordered ising phase the non ergodicity degree of this automaton seems to be inadequate to simulate the ising dynamics in this work the authors introduce two modified automata and test their performance for the square lattice for the first one they found improved results concerning the ising transition for the second modified automaton the ising transition does not occur the magnetization vanishing even for energies far below the normal critical threshold on the other hand another transition appears at a different critical energy value that seems to be the same as that of the periodic chaotic transition already observed in the normal q2r dynamics the critical indices of this new transition are measured from finite size scaling the results indicating that it is at the same universality class as the ising transition

using dynamic programming for solving variational problems in vision dynamic programming is discussed as an approach to solving variational problems in vision dynamic programming ensures global optimality of the solution is numerically stable and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure as a specific example of the approach s efficacy applying dynamic programming to the energy minimizing active contours is described the optimization problem is set up as a discrete multistage decision process and is solved by a time delayed discrete dynamic programming algorithm a parallel procedure for decreasing computational costs is discussed

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

object tracking in image sequences using point features this paper presents an object tracking technique based on the bayesian multiple hypothesis tracking mht approach two algorithms both based on the mht technique are combined to generate an object tracker the first mht algorithm is employed for contour segmentation the segmentation of contours is based on an edge map the segmented contours are then merged to form recognisable objects the second mht algorithm is used in the temporal tracking of a selected object from the initial frame an object is represented by key feature points that are extracted from it the key points mostly corner points are detected using information obtained from the edge map these key points are then tracked through the sequence to confirm the correctness of the tracked key points the location of the key points on the trajectory are verified against the segmented object identified in each frame if an acceptable number of key points lie on or near the contour of the object in a particular frame n th frame we conclude that the selected object has been tracked identified successfully in frame n

physically based active shape models initialization and optimization in this paper we describe a new approach for 2 d object segmentation using an automatic method applied on images with problems as partial information overlapping objects many objects in a single scene severe noise conditions and locating objects with a very high degree of deformation we use a physically based shape model to obtain a deformable template which is defined on a canonical orthogonal coordinate system the proposed methodology works starting from the output of an edge detector which is processed to automatically obtain an approximation of the shape the final estimation of the shapes is obtained fitting a deformable template model which is defined on a learned surface of deformation results from biological images are presented

bayesian shape model for facial feature extraction and recognition abstract a facial feature extraction algorithm using the bayesian shape model bsm is proposed in this paper a full face model consisting of the contour points and the control points is designed to describe the face patch using which the warping normalization of the extracted face patch can be performed efficiently first the bsm is utilized to match and extract the contour points of a face in bsm the prototype of the face contour can be adjusted adaptively according to its prior distribution moreover an affine invariant internal energy term is introduced to describe the local shape deformations between the prototype contour in the shape domain and the deformable contour in the image domain thus both global and local shape deformations can be tolerated then the control points are estimated from the matching result of the contour points based on the statistics of the full face model finally the face patch is extracted and normalized using the piece wise affine triangle warping algorithm experimental results based on real facial feature extraction demonstrate that the proposed bsm facial feature extraction algorithm is more accurate and effective as compared to that of the active shape model asm

a robust snake implementation a dual active contour a conventional active contour formulation suffers difficulty in appropriate choice of an initial contour and values of parameters recent approaches have aimed to resolve these problems but can compromise other performance aspects to relieve the problem in initialization we use a dual active contour which is combined with a local shape model to improve the parameterization one contour expands from inside the target feature the other contracts from the outside the two contours are interlinked to provide a balanced technique with an ability to reject weak local energy minima

on deformable models for visual pattern recognition this paper reviews model based methods for non rigid shape recognition these methods model match and classify non rigid shapes which are generally problematic for conventational algorithms using rigid models issues including model representation optimization criteria formulation model matching and classification are examined in detail with the objective to provide interested researchers a roadmap for exploring the field this paper emphasizes on 2d deformable models their potential applications and future research directions particularly on deformable pattern classification are discussed

a coarse to fine deformable contour optimization framework this paper introduces a novel coarse to fine deformable contour optimization framework which is composed of two main components the first component uses scale space and information theories to produce a coarser representation of the input image to be used in a coarse to fine optimization scheme the employment of information theory ensures that maximal image information is propagated to the coarse images and employment of scale spaces provides a mechanism to change the image coarseness locally based on the deformable contour model definition the second component of this framework uses a novel combination of dynamic programming and gradient descent methods to optimize the contour energy on coarser representations and then use the obtained coarse contour positions in finer optimizations the motivation in using a combination of dynamic programming and gradient descent method is to take advantage of each method s efficiency and avoid their drawbacks in order to verify the performance of this framework we constructed a deformable contour model for the spatiotemporal tracking of closed contours and optimized the model energy under this framework experiments on this system performed using synthetic images and real world echocardiographic sequences demonstrated the effectiveness and practicality of this framework

damage spreading and critical exponents for model a ising dynamics using damage spreading and heat bath dynamics we study the ising model in 2 and 3 dimensions with non conservative dynamics our algorithm differs in some important points from previous ones which makes it rather efficient we give estimates for the exponent z which seem to be the most precise published so far 2 172 0 006 for d 2 2 032 0 004 for d 3 we also give precise estimates of the exponent introduced by janssen et al z phys b 73 1989 539 and of analogous but in principle independent exponents we find surprisingly that some of the latter agree with and give an explanation for this

fast and accurate edge based segmentation with no contour smoothing in 2 d real images we propose an edge based segmentation algorithm built on a new type of active contour which is fast has a low computational complexity and does not introduce unwanted smoothing on the retrieved contours the contours are always returned as closed chains of points resulting in a very useful base for subsequent shape representation techniques

the dynamic critical exponent of the three dimensional ising model we measure the dynamic exponent of the three dimensional ising model using a damage spreading monte carlo approach as described by macisaac and jan we simulate systems froml 5 tol 60 at the critical temperature tc 4 5115 we report a dynamic exponent z 2 35 0 05 a value much larger than the consensus value of 2 02 whereas if we assume logarithmic corrections we find thatz 2 05 0 05

synchronous parallel kinetic monte carlo for continuum diffusion reaction systems a novel parallel kinetic monte carlo kmc algorithm formulated on the basis of perfect time synchronicity is presented the algorithm is intended as a generalization of the standard n fold kmc method and is trivially implemented in parallel architectures in its present form the algorithm is not rigorous in the sense that boundary conflicts are ignored we demonstrate however that in their absence or if they were correctly accounted for our algorithm solves the same master equation as the serial method we test the validity and parallel performance of the method by solving several pure diffusion problems i e with no particle interactions with known analytical solution we also study diffusion reaction systems with known asymptotic behavior and find that for large systems with interaction radii smaller than the typical diffusion length boundary conflicts are negligible and do not affect the global kinetic evolution which is seen to agree with the expected analytical behavior our method is a controlled approximation in the sense that the error incurred by ignoring boundary conflicts can be quantified intrinsically during the course of a simulation and decreased arbitrarily controlled by modifying a few problem dependent simulation parameters

billion atom synchronous parallel kinetic monte carlo simulations of critical 3d ising systems an extension of the synchronous parallel kinetic monte carlo spkmc algorithm developed by martinez et al j comp phys 227 2008 3804 to discrete lattices is presented the method solves the master equation synchronously by recourse to null events that keep all processors time clocks current in a global sense boundary conflicts are resolved by adopting a chessboard decomposition into non interacting sublattices we find that the bias introduced by the spatial correlations attendant to the sublattice decomposition is within the standard deviation of serial calculations which confirms the statistical validity of our algorithm we have analyzed the parallel efficiency of spkmc and find that it scales consistently with problem size and sublattice partition we apply the method to the calculation of scale dependent critical exponents in billion atom 3d ising systems with very good agreement with state of the art multispin simulations

a fast minimal path active contour model a new minimal path active contour model for boundary extraction is presented implementing the new approach requires four steps 1 users place some initial end points on or near the desired boundary through an interactive interface 2 a potential searching window is defined between two end points 3 a graph search method based on conic curves is used to search the boundary and 4 a wriggling procedure is used to calibrate the contour and reduce sensitivity of the search results on the selected initial end points the last three steps are performed automatically in the proposed approach the potential window systematically provides a new node connection for the later graph search which is different from the row by row and column by column methods used in the classical graph search furthermore this graph search also suggests ways to design a wriggling procedure to evolve the contour in the direction nearly perpendicular to itself by creating a list of displacement vectors in the potential window the proposed minimal path active contour model speeds up the search and reduces the metrication error frequently encountered in the classical graph search methods e g the dynamic programming minimal path dpmp method

on the sensitivity of the hough transform for object recognition object recognition from sensory data involves in part determining the pose of a model with respect to a scene a common method for finding an object s pose is the generalized hough transform which accumulates evidence for possible coordinate transformations in a parameter space whose axes are the quantized transformation parameters large clusters of similar transformations in that space are taken as evidence of a correct match a theoretical analysis of the behavior of such methods is presented the authors derive bounds on the set of transformations consistent with each pairing of data and model features in the presence of noise and occlusion in the image bounds are provided on the likelihood of false peaks in the parameter space as a function of noise occlusion and tessellation effects it is argued that haphazardly applying such methods to complex recognition tasks is risky as the probability of false positives can be very high

boundary finding with parametrically deformable models segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model based global shape information the authors apply flexible constraints in the form of a probabilistic deformable model to the problem of segmenting natural 2 d objects whose diversity and irregularity of shape make them poorly represented in terms of fixed features or form the parametric model is based on the elliptic fourier decomposition of the boundary probability distributions on the parameters of the representation bias the model to a particular overall shape while allowing for deformations boundary finding is formulated as an optimization problem using a maximum a posteriori objective function results of the method applied to real and synthetic images are presented including an evaluation of the dependence of the method on prior information and image quality