binary image comparison with local dissimilarity quantification in this paper we present a method for binary image comparison for binary images intensity information is poor and shape extraction is often difficult therefore binary images have to be compared without using feature extraction due to the fact that different scene patterns can be present in the images we propose a modified hausdorff distance hd locally measured in an adaptive way the resulting set of measures is richer than a single global measure the local hd measures result in a local dissimilarity map ldmap including the dissimilarity spatial layout a classification of the images in function of their similarity is carried out on the ldmaps using a support vector machine the proposed method is tested on a medieval illustration database and compared with other methods to show its efficiency

a theory of automatic parameter selection for feature extraction with application to feature based multisensor image registration this paper generalizes the previously developed automated edge detection parameter selection algorithm of yitzhaky and peli we generalize the approach to arbitrary multidimensional continuous or discrete parameter spaces and feature spaces this generalization enables use of the parameter selection approach with more general image features for use in feature based multisensor image registration applications we investigate the problem of selecting a suitable parameter space sampling density in the automated parameter selection algorithm a real valued sensitivity measure is developed which characterizes the effect of parameter space sampling on feature set variability closed form solutions of the sensitivity measure for special feature set relationships are derived we conduct an analysis of the convergence properties of the sensitivity measure as a function of increasing parameter space sampling density for certain parameter space sampling sequence types closed form expressions for the sensitivity measure limit values are presented we discuss an approach to parameter space sampling density selection which uses the sensitivity measure convergence behavior we provide numerical results indicating the utility of the sensitivity measure for selecting suitable parameter values

distance maps from unthresholded magnitudes a straightforward algorithm that computes distance maps from unthresholded magnitudes is presented suitable for still images and video sequences while results on binary images are similar to classic euclidean distance transforms the proposed approach does not require a binarization step thus no thresholds are needed and no information is lost in intermediate classification stages experiments include the evaluation of spatial and temporal coherence of distance map values showing better results in both measurements than those obtained with sobel or deriche gradients and classic chessboard distance transforms

clique descriptor of affine invariant regions for robust wide baseline image matching assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small recent image matching algorithms have focused on affine invariant region ir detection and its description to increase the robustness in matching however the distinctiveness of an intensity based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern to address this problem we investigated the geometry of a local ir cluster also called a clique and propose a new clique based image matching method in the proposed method the clique of an ir is estimated by delaunay triangulation in a local affine frame and the hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors we also introduce two adaptively weighted clique distances where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution experimental results show the clique based matching method produces more tentative correspondences than variants of the sift based method

comparing images using the hausdorff distance the hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa thus this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another efficient algorithms for computing the hausdorff distance between all possible relative positions of a binary image and a model are presented the focus is primarily on the case in which the model is only allowed to translate with respect to the image the techniques are extended to rigid motion the hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived the method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods it is shown that the method extends naturally to the problem of comparing a portion of a model against an image

maximum likelihood image matching image matching applications such as tracking and stereo commonly use the sum of squared difference ssd measure to determine the best match however this measure is sensitive to outliers and is not robust to template variations alternative measures have also been proposed that are more robust to these issues we improve upon these using a probabilistic formulation for image matching in terms of maximum likelihood estimation that can be used for both edge template matching and gray level image matching this formulation generalizes previous edge matching methods based on distance transforms we apply the techniques to stereo matching and feature tracking uncertainty estimation techniques allow feature selection to be performed by choosing features that minimize the localization uncertainty

computing size functions from edge maps size functions are integer valued functions of two real variables which have been recently proposed for the representation and recognition of shape a main limitation of the theory of size functions appeared to be the fragility of the produced representation with respect to edge fragmentation in this paper it is shown that size functions can actually be defined without making assumptions on the topological structure of the viewed shape consequently size functions can be profitably used even in the presence of fragmented edge maps in order to demonstrate the potential of size functions for computer vision a system for shape recognition is described and tested on two different domains the very good performances of the system indicate that size functions are extremely effective for the analysis of shapes for which geometric models might be difficult to obtain

robustness of shape descriptors to incomplete contour representations with inspiration from psychophysical researches of the human visual system we propose a novel aspect and a method for performance evaluation of contour based shape recognition algorithms regarding their robustness to incompleteness of contours we use complete contour representations of objects as a reference training set incomplete contour representations of the same objects are used as a test set the performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained we call this evaluation procedure the icr test we consider three types of contour incompleteness viz segment wise contour deletion occlusion and random pixel depletion as an illustration the robustness of two shape recognition algorithms to contour incompleteness is evaluated these algorithms use a shape context and a distance multiset as local shape descriptors qualitatively both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours the distance multiset method performs better than the shape context method in this test framework

a novel method for solving a class of singularly perturbed boundary value problems based on reproducing kernel method abstract in this paper a novel method is presented for solving a class of singularly perturbed boundary value problems firstly the original problem is reformulated as a new boundary value problem whose solution does not change rapidly via a proper transformation then the reproducing kernel method is employed to solve the boundary value new problem numerical results show that the present method can provide very accurate analytical approximate solutions

review of shape representation and description techniques more and more images have been generated in digital form around the world there is a growing interest in 1nding images in large collections or from remote databases in order to 1nd an image the image has to be described or represented by certain features shape is an important visual feature of an image searching for images using shape features has attracted much attention there are many shape representation anddescription techniques in the literature in this paper we classify and review these important techniques we examine implementation procedures for each technique and discuss its advantages and disadvantages some recent research results are also included and discussed in this paper finally we identify some promising techniques for image retrieval according to standard principles

bifurcation analysis of a non cooperative differential game with one weak player we study a bifurcation problem for a system of two differential equations in implicit form for each value of the parameter the solution yields a pair of nash equilibrium strategies in feedback form for a non cooperative differential game when 0 the second player has no power to influence the dynamics of the system and his optimal strategy is myopic the game thus reduces to an optimal control problem for the first player by studying the bifurcation in the solutions to the corresponding system of hamilton jacobi equations one can establish existence and multiplicity of solutions to the differential game as becomes strictly positive

complex wavelet structural similarity a new image similarity index we introduce a new measure of image similarity called the complex wavelet structural similarity cw ssim index and show its applicability as a general purpose image similarity index the key idea behind cw ssim is that certain image distortions lead to consistent phase changes in the local wavelet coefficients and that a consistent phase shift of the coefficients does not change the structural content of the image by conducting four case studies we have demonstrated the superiority of the cw ssim index against other indices e g dice hausdorff distance commonly used for assessing the similarity of a given pair of images in addition we show that the cw ssim index has a number of advantages it is robust to small rotations and translations it provides useful comparisons even without a preprocessing image registration step which is essential for other indices moreover it is computationally less expensive

learning probabilistic models for contour completion in natural images using a large set of human segmented natural images we study the statistics of region boundaries we observe several power law distributions which likely arise from both multi scale structure within individual objects and from arbitrary viewing distance accordingly we develop a scale invariant representation of images from the bottom up using a piecewise linear approximation of contours and constrained delaunay triangulation to complete gaps we model curvilinear grouping on top of this graphical geometric structure using a conditional random field to capture the statistics of continuity and different junction types quantitative evaluations on several large datasets show that our contour grouping algorithm consistently dominates and significantly improves on local edge detection

an efficiently computable metric for comparing polygonal shapes a method for comparing polygons that is a metric invariant under translation rotation and change of scale reasonably easy to compute and intuitive is presented the method is based on the l sub 2 distance between the turning functions of the two polygons it works for both convex and nonconvex polygons and runs in time o mn log mn where m is the number of vertices in one polygon and n is the number of vertices in the other some examples showing that the method produces answers that are intuitively reasonable are presented

an approximate method for solving a class of singular perturbation problems abstract an approximate method for the numerical solution of a class of singularly perturbed two point boundary value problems is presented the given region is divided into inner and outer regions the original second order problem is replaced by an asymptotically equivalent first order problem and solved as an initial value problem in the inner region a terminal boundary condition is then obtained from the solution of the inner region problem in turn an outer region problem is obtained by replacing the second order differential equation by an approximate first order differential equation with a small deviating argument and solved efficiently by employing the trapezoidal formula coupled with a discrete invariant imbedding algorithm the proposed method is iterative on the terminal point of the inner region problem several numerical examples have been solved to demonstrate the applicability of the method

initial value technique for singularly perturbed two point boundary value problems using an exponentially fitted finite difference scheme in this paper we present an approximate method initial value technique for the numerical solution of quasilinear singularly perturbed two point boundary value problems in ordinary differential equations having a boundary layer at one end left or right point it is motivated by the asymptotic behavior of singular perturbation problems the original problem is reduced to an asymptotically equivalent first order initial value problem by approximating the zeroth order term by outer solution obtained by asymptotic expansion and then this initial value problem is solved by an exponentially fitted finite difference scheme some numerical examples are given to illustrate the given method it is observed that the presented method approximates the exact solution very well for crude mesh size h

the hausdorff fuzzy quasi metric removing the condition of symmetry in the notion of a fuzzy pseudo metric in kramosil and michalek s sense one has the notion of a fuzzy quasi pseudo metric then for each fuzzy quasi pseudo metric on a set x we construct a fuzzy quasi pseudo metric on the collection of all nonempty subsets of x called the hausdorff fuzzy quasi pseudo metric we investigate several properties of this structure and present several illustrative examples as well as an application to the domain of words the notion of hausdorff fuzzy quasi pseudo metric when quasi pseudo metric fuzziness is considered in the sense of george and veeramani is also discussed

noisy logo recognition using line segment hausdorff distance logo recognition is of great interest in the document and shape analysis domain in order to develop a recognition method that is robust to employ under adverse conditions such as different scale orientation broken curves added noise and occlusion a modified line segment hausdorff distance is proposed in this paper the new approach has the advantage to incorporate structural and spatial information to compute dissimilarity between two sets of line segments rather than two sets of points the proposed technique has been applied on line segments generated from logos with encouraging results clear cut distinction between the correct and incorrect matches has been observed this suggests a strong potential for logo and shape recognition system

a maximum entropy method for solving the boundary value problem of second order ordinary differential equations we propose a new method to solve the boundary value problem for a class of second order linear ordinary differential equations which has a non negative solution the method applies the maximum entropy principle to approximating the solution numerically the theoretical analysis and numerical examples show that our method is convergent we propose a new method to solve the boundary value problem for a class of second order linear ordinary differential equations which has a non negative solution the method applies the maximum entropy principle to approximating the solution numerically the theoretical analysis and numerical examples show that our method is convergent

fast and robust optic disc detection using pyramidal decomposition and hausdorff based template matching reports on the design and test of an image processing algorithm for the localization of the optic disk od in low resolution about 20 spl mu pixel color fundus images the design relies on the combination of two procedures 1 a hausdorff based template matching technique on edge map guided by 2 a pyramidal decomposition for large scale object tracking the two approaches are tested against a database of 40 images of various visual quality and retinal pigmentation as well as of normal and small pupils an average error of 7 on od center positioning is reached with no false detection in addition a confidence level is associated to the final detection that indicates the level of difficulty the detector has to identify the od position and shape

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

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

robust shape tracking with multiple models in ultrasound images this paper addresses object tracking in ultrasound images using a robust multiple model tracker the proposed tracker has the following features 1 it uses multiple dynamic models to track the evolution of the object boundary and 2 it models invalid observations outliers reducing their influence on the shape estimates the problem considered in this paper is the tracking of the left ventricle which is known to be a challenging problem the heart motion presents two phases diastole and systole with different dynamics the multiple models used in this tracker try to solve this difficulty in addition ultrasound images are corrupted by strong multiplicative noise which prevents the use of standard deformable models robust estimation techniques are used to address this difficulty the multiple model data association mmda tracker proposed in this paper is based on a bank of nonlinear filters organized in a tree structure the algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution using robust estimation techniques

rigid shape matching by segmentation averaging we use segmentations to match images by shape the new matching technique does not require point to point edge correspondence and is robust to small shape variations and spatial shifts to address the unreliability of segmentations computed bottom up we give a closed form approximation to an average over all segmentations our method has many extension yielding new algorithms for tracking object detection segmentation and edge preserving smoothing for segmentation instead of a maximum a posteriori approach we compute the central segmentation minimizing the average distance to all segmentations of an image for smoothing instead of smoothing images based on local structures we smooth based on the global optimal image structures our methods for segmentation smoothing and object detection perform competitively and we also show promising results in shape based tracking

an adaptive image euclidean distance the image euclidean distance imed considers the spatial relationship between the pixels of different images and can easily be embedded in existing image recognition algorithms that are based on euclidean distance imed uses the prior knowledge that pixels located near one another have little variance in gray scale values and defines a metric matrix according to the spatial distance between pixels in this paper we propose an adaptive image euclidean distance aimed which considers not only the prior spatial knowledge but also the prior gray level knowledge from images the most important advantage of the proposed aimed over imed is that aimed makes the metric matrix adaptive to the content of the concerned images two ways of using gray level information are proposed one is based on gray level distances and the other is based on cosine dissimilarity of gray levels experiments on two facial databases and a handwritten digital database show that aimed achieves the highest classification accuracy when it is embedded in nearest neighbor classifiers principal component analysis and support vector machines