modified curvature motion for image smoothing and enhancement we formulate a general modified mean curvature based equation for image smoothing and enhancement the key idea is to consider the image as a graph in some r sup 72 and apply a mean curvature type motion to the graph we consider some special cases relevant to grey scale and color images

dti segmentation using an information theoretic tensor dissimilarity measure in recent years diffusion tensor imaging dti has become a popular in vivo diagnostic imaging technique in radiological sciences in order for this imaging technique to be more effective proper image analysis techniques suited for analyzing these high dimensional data need to be developed in this paper we present a novel definition of tensor distance grounded in concepts from information theory and incorporate it in the segmentation of dti in a dti the symmetric positive definite spd diffusion tensor at each voxel can be interpreted as the covariance matrix of a local gaussian distribution thus a natural measure of dissimilarity between spd tensors would be the kullback leibler kl divergence or its relative we propose the square root of the j divergence symmetrized kl between two gaussian distributions corresponding to the diffusion tensors being compared and this leads to a novel closed form expression for the distance as well as the mean value of a dti unlike the traditional frobenius norm based tensor distance our distance is affine invariant a desirable property in segmentation and many other applications we then incorporate this new tensor distance in a region based active contour model for dti segmentation synthetic and real data experiments are shown to depict the performance of the proposed model

color level sets a multi phase method for structural topology optimization with multiple materials in this paper we address the problem of structural shape and topology optimization in a multi material domain a level set method is employed as an alternative approach to the popular homogenization based methods of rule of mixtures for multi material modeling a multi phase level set model is adapted for material and topology representation this model eliminates the need for a material interpolation or phase mixing scheme it only requires m level set functions to represent a structure of n 2m different material phases in a principle similar to combining colors from the three primary colors therefore this multi phase model may be referred to as a color level set representation which has its unique benefits it is flexible to handle complex topologies it substantially reduces the number of model functions when n 3 it automatically avoids the problem of overlap between material phases of a conventional partitioning approach we describe numerical techniques for efficient and robust implementation of the method by embedding a rectilinear grid in a fixed finite element mesh defined on a reference design domain this would separate the issues of accuracy in numerical calculations of the physical equation and in the level set model propagation a gradient projection method is described for incorporating multiple constraints in the problem finally the benefits and the advantages of the developed method are illustrated with several 2d examples of mean compliance minimization of multi material structures

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

sobolev active contours all previous geometric active contour models that have been formulated as gradient flows of various energies use the same l 2 type inner product to define the notion of gradient recent work has shown that this inner product induces a pathological riemannian metric on the space of smooth curves however there are also undesirable features associated with the gradient flows that this inner product induces in this paper we reformulate the generic geometric active contour model by redefining the notion of gradient in accordance with sobolev type inner products we call the resulting flows sobolev active contours sobolev metrics induce favorable regularity properties in their gradient flows in addition sobolev active contours favor global translations but are not restricted to such motions they are also less susceptible to certain types of local minima in contrast to traditional active contours these properties are particularly useful in tracking applications we demonstrate the general methodology by reformulating some standard edge based and region based active contour models as sobolev active contours and show the substantial improvements gained in segmentation

scale space and edge detection using anisotropic diffusion a new definition of scale space is suggested and a class of algorithms used to realize a diffusion process is introduced the diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing it is shown that the no new maxima should be generated at coarse scales property of conventional scale space is preserved as the region boundaries in the approach remain sharp a high quality edge detector which successfully exploits global information is obtained experimental results are shown on a number of images parallel hardware implementations are made feasible because the algorithm involves elementary local operations replicated over the image

variational bayesian strategies for high dimensional stochastic design problems this paper is concerned with a lesser studied problem in the context of model based uncertainty quantification uq that of optimization design control under uncertainty the solution of such problems is hindered not only by the usual difficulties encountered in uq tasks e g the high computational cost of each forward simulation the large number of random variables but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints we propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks to that end we propose a variational bayesian vb formulation and an iterative vb expectation maximization scheme that is capable of identifying a local maximum as well as a low dimensional set of directions in the design space along which the objective exhibits the largest sensitivity we demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables in all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less the accuracy of the approximations provided is assessed by information theoretic metrics

joint ndt image restoration and segmentation using gauss markov potts prior models and variational bayesian computation in this paper we propose a method to simultaneously restore and to segment piecewise homogeneous images degraded by a known point spread function psf and additive noise for this purpose we propose a family of nonhomogeneous gauss markov fields with potts region labels model for images to be used in a bayesian estimation framework the joint posterior law of all the unknowns the unknown image its segmentation hidden variable and all the hyperparameters is approximated by a separable probability law via the variational bayes technique this approximation gives the possibility to obtain practically implemented joint restoration and segmentation algorithm we will present some preliminary results and comparison with a mcmc gibbs sampling based algorithm we may note that the prior models proposed in this work are particularly appropriate for the images of the scenes or objects that are composed of a finite set of homogeneous materials this is the case of many images obtained in nondestructive testing ndt applications

constraints on deformable models recovering 3d shape and nongrid motion abstract inferring the 3d structures of nonrigidly moving objects from images is a difficult yet basic problem in computational vision our approach makes use of dynamic elastically deformable object models that offer the geometric flexibility to satisfy a diversity of real world visual constraints we specialize these models to include intrinsic forces inducing a preference for axisymmetry image based constraints are applied as extrinsic forces that mold the symmetry seeking model into shapes consistent with image data we describe an extrinsic force that applies constraints derived from profiles of monocularly viewed objects we generalize this constraint force to incorporate profile information from multiple views and use it to exploit binocular image data for time varying images the force becomes dynamic and the model is able to infer not only depth but nonrigid motion as well we demonstrate the recovery of 3d shape and nonrigid motion from natural imagery

linear estimation of boundary value stochastic processes part i the role and construction of complementary models this paper presents a substantial extension of the method of complementary models for minimum variance linear estimation introduced by weinert and desai in their important paper 1 specifically the method of complementary models is extended to solve estimation problems for both discrete and continuous parameter linear boundary value stochastic processes in one and higher dimensions a major contribution of this paper is an application of green s identity in deriving a differential operator representation of the estimator to clarify the development and to illustrate the range of applications of our approach two brief examples are provided one is a 1 d discrete two point boundary value process and the other is a 2 d process governed by poisson s equation on the unit disk

an alternative perspective on adaptive independent component analysis algorithms this article develops an extended independent component analysis algorithm for mixtures of arbitrary subgaussian and supergaussian sources the gaussian mixture model of pearson is employed in deriving a closedform generic score function for strictly subgaussian sources this is combined with the score function for a unimodal supergaussian density to provide a computationally simple yet powerful algorithm for performing independent component analysis on arbitrary mixtures of nongaussian sources

a geometric model for active contours in image processing we propose a new model for active contours based on a geometric partial differential equation our model is intrinsec stable satisfies the maximum principle and permits a rigorous mathematical analysis it enables us to extract smooth shapes we cannot retrieve angles and it can be adapted to find several contours simultaneously moreover as a consequence of the stability we can design robust algorithms which can be engineed with no parameters in applications numerical experiments are presented

a general framework for low level vision we introduce a new geometrical framework based on which natural flows for image scale space and enhancement are presented we consider intensity images as surfaces in the x i space the image is thereby a two dimensional 2 d surface in three dimensional 3 d space for gray level images and 2 d surfaces in five dimensions for color images the new formulation unifies many classical schemes and algorithms via a simple scaling of the intensity contrast and results in new and efficient schemes extensions to multidimensional signals become natural and lead to powerful denoising and scale space algorithms

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

curve evolution implementation of the mumford shah functional for image segmentation denoising interpolation and magnification we first address the problem of simultaneous image segmentation and smoothing by approaching the mumford shah 1989 paradigm from a curve evolution perspective in particular we let a set of deformable contours define the boundaries between regions in an image where we model the data via piecewise smooth functions and employ a gradient flow to evolve these contours each gradient step involves solving an optimal estimation problem for the data within each region connecting curve evolution and the mumford shah functional with the theory of boundary value stochastic processes the resulting active contour model offers a tractable implementation of the original mumford shah model i e without resorting to elliptic approximations which have traditionally been favored for greater ease in implementation to simultaneously segment and smoothly reconstruct the data within a given image in a coupled manner various implementations of this algorithm are introduced to increase its speed of convergence we also outline a hierarchical implementation of this algorithm to handle important image features such as triple points and other multiple junctions next by generalizing the data fidelity term of the original mumford shah functional to incorporate a spatially varying penalty we extend our method to problems in which data quality varies across the image and to images in which sets of pixel measurements are missing this more general model leads us to a novel pde based approach for simultaneous image magnification segmentation and smoothing thereby extending the traditional applications of the mumford shah functional which only considers simultaneous segmentation and smoothing

analysis of least squares finite element methods for the navier stokes equations in this paper we study finite element methods of least squares type for the stationary incompressible navier stokes equations in two and three dimensions we consider methods based on velocity vorticity pressure form of the navier stokes equations augmented with several nonstandard boundary conditions least squares minimization principles for these boundary value problems are developed with the aid of the agmon douglis nirenberg adn elliptic theory among the main results of this paper are optimal error estimates for conforming finite element approximations and analysis of some nonstandard boundary conditions results of several computational experiments with least squares methods which illustrate among other things the optimal convergence rates are also reported

a kalman filter approach for denoising and deblurring 3 d microscopy images this paper proposes a new method for removing noise and blurring from 3d microscopy images the main contribution is the definition of a space variant generating model of a 3 d signal which is capable to stochastically describe a wide class of 3 d images unlike other approaches the space variant structure allows the model to consider the information on edge locations if available a suitable description of the image acquisition process including blurring and noise is then associated to the model a state space realization is finally derived which is amenable to the application of standard kalman filter as an image restoration algorithm the so obtained method is able to remove at each spatial step both blur and noise via a linear minimum variance recursive one shot procedure which does not require the simultaneous processing of the whole image numerical results on synthetic and real microscopy images confirm the merit of the approach

a mesostate space model for eeg and meg abstract we present a multi scale generative model for eeg that entails a minimum number of assumptions about evoked brain responses namely 1 bioelectric activity is generated by a set of distributed sources 2 the dynamics of these sources can be modelled as random fluctuations about a small number of mesostates 3 mesostates evolve in a temporal structured way and are functionally connected i e influence each other and 4 the number of mesostates engaged by a cognitive task is small e g between one and a few a variational bayesian learning scheme is described that furnishes the posterior density on the models parameters and its evidence since the number of meso sources specifies the model the model evidence can be used to compare models and find the optimum number of meso sources in addition to estimating the dynamics at each cortical dipole the mesostate space model and its inversion provide a description of brain activity at the level of the mesostates i e in terms of the dynamics of meso sources that are distributed over dipoles the inclusion of a mesostate level allows one to compute posterior probability maps of each dipole being active i e belonging to an active mesostate critically this model accommodates constraints on the number of meso sources while retaining the flexibility of distributed source models in explaining data in short it bridges the gap between standard distributed and equivalent current dipole models furthermore because it is explicitly spatiotemporal the model can embed any stochastic dynamical causal model e g a neural mass model as a markov process prior on the mesostate dynamics the approach is evaluated and compared to standard inverse eeg techniques using synthetic data and real data the results demonstrate the added value of the mesostate space model and its variational inversion

projection pursuit mixture density estimation in this paper we seek a gaussian mixture model gmm of an n variate probability density function usually the parameters of gmms are determined in the original n dimensional space by optimizing a maximum likelihood ml criterion a practical deficiency of this method of fitting gmms is its poor performance when dealing with high dimensional data since a large sample size is needed to match the accuracy that is possible in low dimensions we propose a method for fitting the gmm based on the projection pursuit strategy this gmm is highly constrained and hence its ability to model structure in subspaces is enhanced compared to a direct ml fitting of a gmm in high dimensions our method is closely related to recently developed independent factor analysis ifa mixture models the comparisons with ml fitting of gmm in n dimensions and ifa mixtures show that the proposed method is an attractive choice for fitting gmms using small sizes of training sets

motion of multiple junctions a level set approach abstract a coupled level set method for the motion of multiple junctions is proposed the new method extends the hamilton jacobi level set formulation of osher and sethian it retains the feature of tracking fronts by following level sets and allows the specification of arbitrary velocities on each front the diffusion equation is shown to generate curvature dependent motion and this is used to develop an algorithm to move multiple junctions with curvature dependent speed systems of reaction diffusion equations are shown to possess inherent properties which prohibit efficient numerical solutions when applied to curvature dependent motion

ica mixture models for unsupervised classification of non gaussian classes and automatic context switching in blind signal separation an unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent non gaussian densities the algorithm estimates the density of each class and is able to model class distributions with non gaussian structure the new algorithm can improve classification accuracy compared with standard gaussian mixture models when applied to blind source separation in nonstationary environments the method can switch automatically between classes which correspond to contexts with different mixing properties the algorithm can learn efficient codes for images containing both natural scenes and text this method shows promise for modeling non gaussian structure in high dimensional data and has many potential applications

orientation diffusions diffusions are useful for image processing and computer vision because they provide a convenient way of smoothing noisy data analyzing images at multiple scales and enhancing discontinuities a number of diffusions of image brightness have been defined and studied so far they may be applied to scalar and vector valued quantities that are naturally associated with intervals of either the real line or other flat manifolds some quantities of interest in computer vision and other areas of engineering that deal with images are defined on curved manifolds typical examples are orientation and hue that are defined on the circle generalizing brightness diffusions to orientation is not straightforward especially in the case where a discrete implementation is sought an example of what may go wrong is presented a method is proposed to define diffusions of orientation like quantities first a definition in the continuum is discussed then a discrete orientation diffusion is proposed the behavior of such diffusions is explored both analytically and experimentally it is shown how such orientation diffusions contain a nonlinearity that is reminiscent of edge process and anisotropic diffusion a number of open questions are proposed

behavioral analysis of anisotropic diffusion in image processing in this paper we analyze the behavior of the anisotropic diffusion model of perona and malik 1990 the main idea is to express the anisotropic diffusion equation as coming from a certain optimization problem so its behavior can be analyzed based on the shape of the corresponding energy surface we show that anisotropic diffusion is the steepest descent method for solving an energy minimization problem it is demonstrated that an anisotropic diffusion is well posed when there exists a unique global minimum for the energy functional and that the ill posedness of a certain anisotropic diffusion is caused by the fact that its energy functional has an infinite number of global minima that are dense in the image space we give a sufficient condition for an anisotropic diffusion to be well posed and a sufficient and necessary condition for it to be ill posed due to the dense global minima the mechanism of smoothing and edge enhancement of anisotropic diffusion is illustrated through a particular orthogonal decomposition of the diffusion operator into two parts one that diffuses tangentially to the edges and therefore acts as an anisotropic smoothing operator and the other that flows normally to the edges and thus acts as an enhancement operator

a unifying information theoretic framework for independent component analysis abstract we show that different theories recently proposed for independent component analysis ica lead to the same iterative learning algorithm for blind separation of mixed independent sources we review those theories and suggest that information theory can be used to unify several lines of research pearlmutter and parra 1 and cardoso 2 showed that the infomax approach of bell and sejnowski 3 and the maximum likelihood estimation approach are equivalent we show that negentropy maximization also has equivalent properties and therefore all three approaches yield the same learning rule for a fixed nonlinearity girolami and fyfe 4 have shown that the nonlinear principal component analysis pca algorithm of karhunen and joutsensalo 5 and oja 6 can also be viewed from information theoretic principles since it minimizes the sum of squares of the fourth order marginal cumulants and therefore approximately minimizes the mutual information 7 lambert 8 has proposed different bussgang cost functions for multichannel blind deconvolution we show how the bussgang property relates to the infomax principle finally we discuss convergence and stability as well as future research issues in blind source separation

local or global minima flexible dual front active contours most variational active contour models are designed to find local minima of data dependent energy functionals with the hope that reasonable initial placement of the active contour will drive it toward a desirable local minimum as opposed to an undesirable configuration due to noise or complex image structure as such there has been much research into the design of complex region based energy functionals that are less likely to yield undesirable local minima when compared to simpler edge based energy functionals whose sensitivity to noise and texture is significantly worse unfortunately most of these more robust region based energy functionals are applicable to a much narrower class of imagery compared to typical edge based energies due to stronger global assumptions about the underlying image data devising new implementation algorithms for active contours that attempt to capture more global minimizers of already proposed image based energies would allow us to choose an energy that makes sense for a particular class of energy without concern over its sensitivity to local minima such implementations have been proposed for capturing global minima however sometimes the completely global minimum is just as undesirable as a minimum that is too local in this paper we propose a novel fast and flexible dual front implementation of active contours motivated by minimal path techniques and utilizing fast sweeping algorithms which is easily manipulated to yield minima with variable degrees of localness and globalness by simply adjusting the size of active regions the ability to gracefully move from capturing minima that are more local according to the initial placement of the active contour surface to minima that are more global allows this model to more easily obtain desirable minimizers which often are neither the most local nor the most global experiments on various 2d and 3d images and comparisons with some active contour models and region growing methods are also given to illustrate the properties of this model and its performance in a variety of segmentation applications

anisotropic diffusion of multivalued images with applications to color filtering a general framework for anisotropic diffusion of multivalued images is presented we propose an evolution equation where at each point in time the directions and magnitudes of the maximal and minimal rate of change in the vector image are first evaluated these are given by eigenvectors and eigenvalues of the first fundamental form in the given image metric then the image diffuses via a system of coupled differential equations in the direction of minimal change the diffusion strength is controlled by a function that measures the degree of dissimilarity between the eigenvalues we apply the proposed framework to the filtering of color images represented in cie l a b space

region based strategies for active contour models the variational method has been introduced by kass et al 1987 in the field of object contour modeling as an alternative to the more traditional edge detection edge thinning edge sorting sequence since the method is based on a pre processing of the image to yield an edge map it shares the limitations of the edge detectors it uses in this paper we propose a modified variational scheme for contour modeling which uses no edge detection step but local computations instead only around contour neighborhoods as well as an anticipating strategy that enhances the modeling activity of deformable contour curves many of the concepts used were originally introduced to study the local structure of discontinuity in a theoretical and formal statement by leclerc zucker 1987 but never in a practical situation such as this one the first part of the paper introduces a region based energy criterion for active contours and gives an examination of its implications as compared to the gradient edge map energy of snakes then a simplified optimization scheme is presented accounting for internal and external energy in separate steps this leads to a complete treatment which is described in the last sections of the paper 4 and 5 the optimization technique used here is mostly heuristic and is thus presented without a formal proof but is believed to fill a gap between snakes and other useful image representations such as split and merge regions or mixed line labels image fields

the computational complexity of probabilistic inference using bayesian belief networks research note abstract bayesian belief networks provide a natural efficient method for representing probabilistic dependencies among a set of variables for these reasons numerous researchers are exploring the use of belief networks as a knowledge representation in artificial intelligence algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks more general classes of belief networks however have eluded efforts to develop efficient inference algorithms we show that probabilistic inference using belief networks is np hard therefore it seems unlikely that an exact algorithm can be developed to perform probabilistic inference efficiently over all classes of belief networks this result suggests that research should be directed away from the search for a general efficient probabilistic inference algorithm and toward the design of efficient special case average case and approximation algorithms

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

a general procedure for learning mixtures of independent component analyzers this paper presents a new procedure for learning mixtures of independent component analyzers the procedure includes non parametric estimation of the source densities supervised unsupervised learning of the model parameters incorporation of any independent component analysis ica algorithm into the learning of the ica mixtures and estimation of residual dependencies after training for correction of the posterior probability of every class to the testing observation vector we demonstrate the performance of the procedure in the classification of ica mixtures of two three and four classes of synthetic data and in the classification of defective materials consisting of 3d finite element models and lab specimens in non destructive testing using the impact echo technique the application of the proposed posterior probability correction demonstrates an improvement in the classification accuracy semi supervised learning shows that unlabeled data can degrade the performance of the classifier when they do not fit the generative model comparative results of the proposed method and standard ica algorithms for blind source separation in one and multiple ica data mixtures show the suitability of the non parametric ica mixture based method for data modeling

minimum entropy data partitioning using reversible jump markov chain monte carlo problems in data analysis often require the unsupervised partitioning of a data set into classes several methods exist for such partitioning but many have the weakness of being formulated via strict parametric models e g each class is modeled by a single gaussian or being computationally intensive in high dimensional data spaces we reconsider the notion of such cluster analysis in information theoretic terms and show that an efficient partitioning may be given via a minimization of partition entropy a reversible jump sampling is introduced to explore the variable dimension space of partition models

mixtures of probabilistic principal component analyzers principal component analysis pca is one of the most popular techniques for processing compressing and visualizing data although its effectiveness is limited by its global linearity while nonlinear variants of pca have been proposed an alternative paradigm is to capture data complexity by a combination of local linear pca projections however conventional pca does not correspond to a probability density and so there is no unique way to combine pca models therefore previous attempts to formulate mixture models for pca have been ad hoc to some extent in this article pca is formulated within a maximum likelihood framework based on a specific form of gaussian latent variable model this leads to a well defined mixture model for probabilistic principal component analyzers whose parameters can be determined using an expectationmaximization algorithm we discuss the advantages of this model in the context of clustering density modeling and local dimensionality reduction and we demonstrate its application to image compression and handwritten digit recognition

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

optimal approximations by piecewise smooth functions and associated variational problems abstract this reprint will introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision in computer vision a fundamental problem is to appropriately decompose the domain r of a function g x y of two variables this problem starts by describing the physical situation which produces images assume that a three dimensional world is observed by an eye or camera from some point p and that g1 rho represents the intensity of the light in this world approaching the point sub 1 from a direction rho if one has a lens at p focusing this light on a retina or a film in both cases a plane domain r in which we may introduce coordinates x y then let g x y be the strength of the light signal striking r at a point with coordinates x y g x y is essentially the same as sub 1 rho possibly after a simple transformation given by the geometry of the imaging syste the function g x y defined on the plane domain r will be called an image what sort of function is g the light reflected off the surfaces si of various solid objects o sub i visible from p will strike the domain r in various open subsets r sub i when one object o1 is partially in front of another object o2 as seen from p but some of object o2 appears as the background to the sides of o1 then the open sets r1 and r2 will have a common boundary the edge of object o1 in the image defined on r and one usually expects the image g x y to be discontinuous along this boundary jhd

mean curvature evolution and surface area scaling in image filtering representing the image as a surface an inhomogeneous diffusion algorithm is developed evolving the surface at a speed proportional to its mean curvature reducing noise while preserving image structure an adaptive scaling parameter increases the speed of the diffusion the properties of a discrete algorithm are demonstrated experimentally

automatic ultrasound image segmentation based on local entropy and active contour model abstract it is still a challenging task to segment ultrasound medical images automatically and accurately because of the poor quality of images to address these problems a two stage automatic segmentation scheme based on local entropy and a proposed active contour model for ultrasound images are put forward in this paper first a new region based active contour model in the level set formulation driven by global and local intensity information is established for the segmentation furthermore for automatically segmenting the ultrasound images a coarse segmentation is performed utilizing local entropy information of the ultrasound images then the coarse segmentation result is used as the initial value of the explicit segmentation based on the proposed model several experiments on real ultrasound images demonstrate that the proposed method outperforms other methods on both visual perception and objective evaluation metrics

fast nonparametric active contour adapted to quadratic inhomogeneous intensity fluctuations in the context of unsupervised segmentation of noisy images a minimum description length mdl polygonal active contour technique based on nonparametric modeling of the noise probability density function pdf has been proposed in 2011 this approach allows fast and efficient segmentation of an object without a priori knowledge on the intensity fluctuations nevertheless since the object and the background are assumed to be homogeneous degraded segmentation results are obtained when images present inhomogeneous intensity variations it is shown in this paper that this constraint of homogeneity can be removed still with minimizing a mdl criterion without undetermined parameters and adapted to nonparametric modeling of the noise pdf for that purpose the spatial inhomogeneity of the intensity is modeled with 2d quadratic functions moreover low computation times can be achieved approximately 60ms on 256x256 pixel images using a two step optimization strategy the efficiency and the robustness of this approach are then validated on various synthetic and real images acquired from different sensors

variational learning of clusters of undercomplete nonsymmetric independent components we apply a variational method to automatically determine the number of mixtures of independent components in high dimensional datasets in which the sources may be nonsymmetrically distributed the data are modeled by clusters where each cluster is described as a linear mixture of independent factors the variational bayesian method yields an accurate density model for the observed data without overfitting problems this allows the dimensionality of the data to be identified for each cluster the new method was successfully applied to a difficult real world medical dataset for diagnosing glaucoma

variational mixture of bayesian independent component analyzers there has been growing interest in subspace data modeling over the past few years methods such as principal component analysis factor analysis and independent component analysis have gained in popularity and have found many applications in image modeling signal processing and data compression to name just a few as applications and computing power grow more and more sophisticated analyses and meaningful representations are sought mixture modeling methods have been proposed for principal and factor analyzers that exploit local gaussian features in the subspace manifolds meaningful representations may be lost however if these local features are nongaussian or discontinuous in this article we propose extending the gaussian analyzers mixture model to an independent component analyzers mixture model we employ recent developments in variational bayesian inference and structure determination to construct a novel approach for modeling nongaussian discontinuous manifolds we automatically determine the local dimensionality of each manifold and use variational inference to calculate the optimum number of ica components needed in our mixture model we demonstrate our framework on complex synthetic data and illustrate its application to real data by decomposing functional magnetic resonance images into meaningful and medically useful features

area and length minimizing flows for shape segmentation a number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation such as snakes the essential idea is to evolve a curve in two dimensions or a surface in three dimensions under constraints from image forces so that it clings to features of interest in an intensity image the evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional tailored to features such as edges however because the flow may be slow to converge in practice a constant hyperbolic term is added to keep the curve surface moving in the desired direction we derive a modification of this term based on the gradient flow derived from a weighted area functional with image dependent weighting factor when combined with the earlier modified length gradient flow we obtain a partial differential equation pde that offers a number of advantages as illustrated by several examples of shape segmentation on medical images in many cases the weighted area flow may be used on its own with significant computational savings

independent factor analysis we introduce the independent factor analysis ifa method for recovering independent hidden sources from their observed mixtures ifa generalizes and unifies ordinary factor analysis fa principal component analysis pca and independent component analysis ica and can handle not only square noiseless mixing but also the general case where the number of mixtures differs from the number of sources and the data are noisy ifa is a two step procedure in the first step the source densities mixing matrix and noise covariance are estimated from the observed data by maximum likelihood for this purpose we present an expectation maximization em algorithm which performs unsupervised learning of an associated probabilistic model of the mixing situation each source in our model is described by a mixture of gaussians thus all the probabilistic calculations can be performed analytically in the second step the sources are reconstructed from the observed data by an optimal nonlinear estimator a variational approximation of this algorithm is derived for cases with a large number of sources where the exact algorithm becomes intractable our ifa algorithm reduces to the one for ordinary fa when the sources become gaussian and to an em algorithm for pca in the zero noise limit we derive an additional em algorithm specifically for noiseless ifa this algorithm is shown to be superior to ica since it can learn arbitrary source densities from the data beyond blind separation ifa can be used for modeling multidimensional data by a highly constrained mixture of gaussians and as a tool for nonlinear signal encoding

approximation of functional depending on jumps by elliptic functional via t convergence we show how it is possible to approximate the mumford shah see 29 image segmentation functional by elliptic functionals defined on sobolev spaces the heuristic idea is to consider functionals h u z with z ranging between 0 and 1 and related to the set k the minimizing zh are near to 1 in a neighborhood of the set k and far from the neighborhood they are very small the neighborhood shrinks as h for a similar approach to the problem compare kulkarni see 25 the approximation of h to takes place in a variational sense the de giorgi f convergence

shapes from pixels continuous domain visual signals are usually captured as discrete digital images this operation is not invertible in general in the sense that the continuous domain signal cannot be exactly reconstructed based on the discrete image unless it satisfies certain constraints e g bandlimitedness in this paper we study the problem of recovering shape images with smooth boundaries from a set of samples thus the reconstructed image is constrained to regenerate the same samples consistency as well as forming a shape bilevel image we initially formulate the reconstruction technique by minimizing the shape perimeter over the set of consistent binary shapes next we relax the non convex shape constraint to transform the problem into minimizing the total variation over consistent non negative valued images we also introduce a requirement called reducibility that guarantees equivalence between the two problems we illustrate that the reducibility property effectively sets a requirement on the minimum sampling density we also evaluate the performance of the relaxed alternative in various numerical experiments

level set segmentation with multiple regions the popularity of level sets for segmentation is mainly based on the sound and convenient treatment of regions and their boundaries unfortunately this convenience is so far not known from level set methods when applied to images with more than two regions this communication introduces a comparatively simple way how to extend active contours to multiple regions keeping the familiar quality of the two phase case we further suggest a strategy to determine the optimum number of regions as well as initializations for the contours

area and length preserving geometric invariant scale spaces in this paper area preserving multi scale representations of planar curves are described this allows smoothing without shrinkage at the same time preserving all the scale space properties the representations are obtained deforming the curve via geometric heat flows while simultaneously magnifying the plane by a homethety which keeps the enclosed area constant when the euclidean geometric heat flow is used the resulting representation is euclidean invariant and similarly it is affine invariant when the affine one is used the flows are geometrically intrinsic to the curve and exactly satisfy all the basic requirements of scale space representations in the case of the euclidean heat flow it is completely local as well the same approach is used to define length preserving geometric flows a similarity scale invariant geometric heat flow is studied as well in this work

an information maximization approach to blind separation and blind deconvolution we derive a new self organizing learning algorithm that maximizes the information transferred in a network of nonlinear units the algorithm does not assume any knowledge of the input distributions and is defined here for the zero noise limit under these conditions information maximization has extra properties not found in the linear case linsker 1989 the nonlinearities in the transfer function are able to pick up higher order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation this enables the network to separate statistically independent components in the inputs a higher order generalization of principal components analysis we apply the network to the source separation or cocktail party problem successfully separating unknown mixtures of up to 10 speakers we also show that a variant on the network architecture is able to perform blind deconvolution cancellation of unknown echoes and reverberation in a speech signal finally we derive dependencies of information transfer on time delays we suggest that information maximization provides a unifying framework for problems in blind signal processing

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

kernel density estimation and intrinsic alignment for shape priors in level set segmentation in this paper we make two contributions to the field of level set based image segmentation firstly we propose shape dissimilarity measures on the space of level set functions which are analytically invariant under the action of certain transformation groups the invariance is obtained by an intrinsic registration of the evolving level set function in contrast to existing approaches to invariance in the level set framework this closed form solution removes the need to iteratively optimize explicit pose parameters the resulting shape gradient is more accurate in that it takes into account the effect of boundary variation on the object s pose secondly based on these invariant shape dissimilarity measures we propose a statistical shape prior which allows to accurately encode multiple fairly distinct training shapes this prior constitutes an extension of kernel density estimators to the level set domain in contrast to the commonly employed gaussian distribution such nonparametric density estimators are suited to model aribtrary distributions we demonstrate the advantages of this multi modal shape prior applied to the segmentation and tracking of a partially occluded walking person in a video sequence and on the segmentation of the left ventricle in cardiac ultrasound images we give quantitative results on segmentation accuracy and on the dependency of segmentation results on the number of training shapes