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

automatic model based segmentation of the heart in ct images automatic image processing methods are a pre requisite to efficiently analyze the large amount of image data produced by computed tomography ct scanners during cardiac exams this paper introduces a model based approach for the fully automatic segmentation of the whole heart four chambers myocardium and great vessels from 3 d ct images model adaptation is done by progressively increasing the degrees of freedom of the allowed deformations this improves convergence as well as segmentation accuracy the heart is first localized in the image using a 3 d implementation of the generalized hough transform pose misalignment is corrected by matching the model to the image making use of a global similarity transformation the complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model finally a deformable adaptation is performed to accurately match the boundaries of the patient s anatomy a mean surface to surface error of 0 82 mm was measured in a leave one out quantitative validation carried out on 28 images moreover the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis

inherent bias and noise in the hough transform considering the hough transformation as a linear imaging process recasts certain well known problems provides a useful vocab ulary and possibly indicates a source of applicable literature on the behavior of the hough transformation in various forms of noise a consideration of the analytic form of peaks in parameter space sets the stage for the idea of using complementary negative votes to cancel off peak positive votes in parameter space thus sharpening peaks and reducing bias

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

modification of hough transform for object recognition using a 2 dimensional array abstract in this paper a technique which transforms ballard s 4 dimensional 4d parameters x y s to a 2d format x1 y1 x2 y2 namely 2 point parameterization is introduced this technique leads to new voting and detection rules for object recognition using hough transform due to the 2 point parameterization technique three algorithms namely rotation invariant hough transform rht scale invariant hough transform sht and rotation scale invariant hough transform rsht are proposed for object recognition to handle 1 when the scale s is known and x y are unknown 2 when the orientation is known and x y s are unknown and 3 when all x y s are unknown these proposed methods require only a 2 dimensional array for the accumulator which greatly reduces the memory required in using hough transform for object recognition

shapes recognition using the straight line hough transform theory and generalization a shape matching technique based on the straight line hough transform slht is presented in the theta rho space the transform can be expressed as the sum of the translation term and the intrinsic term this formulation allows the translation rotation and intrinsic parameters of the curve to be easily decoupled a shape signature called the scalable translation invariant rotation to shifting stirs signature is obtained from the theta rho space by computing the distances between pairs of points having the same theta value this signature is invariant to translation and can be easily normalized and rotation in the image space corresponds to circular shifting of the signature matching two signatures only amounts to computing a 1d correlation the height and location of a peak if it exists indicate the similarity and orientation of the test object with respect to the reference object the location of the test object is obtained once the orientation is known by an inverse transform voting from the theta rho space to the x y plane

fuzzy hough transform and an mlp with fuzzy input output for character recognition abstract a neuro fuzzy system for character recognition using a fuzzy hough transform technique is presented in this paper for each character pattern membership values are determined for a number of fuzzy sets defined on the standard hough transform accumulator cells these basic fuzzy sets are combined by t norms to synthesize additional fuzzy sets whose heights form an n dimensional feature vector for the pattern a 3n dimensional fuzzy linguistic vector is generated from the n dimensional feature vector by defining three linguistic fuzzy sets namely weak moderate and strong the linguistic set membership functions are derived from the butterworth polynomials and are similar to the gain functions of low pass band pass and high pass filters respectively a multilayer perceptron mlp is trained with the fuzzy linguistic vectors by the back propagation of errors the mlp outputs represent fuzzy sets denoting similarity of an input feature vector to a number of character pattern classes recognition accuracy of the system is more than 98

learning hough transform a neural network model a single layered hough transform network is proposed that accepts image coordinates of each object pixel as input and produces a set of outputs that indicate the belongingness of the pixel to a particular structure e g a straight line the network is able to learn adaptively the parametric forms of the linear segments present in the image it is designed for learning and identification not only of linear segments in two dimensional images but also the planes and hyperplanes in the higher dimensional spaces it provides an efficient representation of visual information embedded in the connection weights the network not only reduces the large space requirement as in the case of classical hough transform but also represents the parameters with high precision

on detection of multiple object instances using hough transforms hough transform based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in hough images such postprocessing requires the tuning of many parameters and is often fragile especially when objects are located spatially close to each other in this paper we develop a new probabilistic framework for object detection which is related to the hough transform it shares the simplicity and wide applicability of the hough transform but at the same time bypasses the problem of multiple peak identification in hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category level pedestrian detection problem

signature recognition through spectral analysis abstract features such as shape motion and pressure minutiae details and timing and transformation methods such as hadamard and walsh have been used in handwritten signature recognition with various degrees of success others have successfully used nonlinear warping functions to optimally time match an unknown to a standard signature in this research a fast fourier transform is used to transform normalized signatures into the frequency domain fifteen harmonics having the largest magnitudes normalized by their corresponding variances were selected and used in a stepwise discriminant analysis it results in an error rate of 2 5 per cent with the generally more conservative jackknife procedure yielding the same small error rate

linear quadratic mean field type games jump diffusion process with regime switching in this paper we study a linear quadratic mean field type game problem under jump diffusion regime switching state dynamics that include mean field terms in the states and in the cost function in the cost functional we consider conditional variance of both the states and control actions mean of both states and control actions and covariance terms the underlying problem is solved in a semiexplicit way by using the direct method

theoretical quantification of shape distortion in fuzzy hough transform we present a generalization of classical hough transform in fuzzy set theoretic framework called fuzzy hough transform or fht in order to handle the impreciseness ill definedness in shape description in addition to identifying the shapes the methodology can quantify the amount of distortion present in each shape by suitably characterizing the parametric space we extended fht to take care of gray level images gray fht in order to handle the gray level variation along with shape distortion the gray fht gives rise to a scheme for image segmentation based on the a priori knowledge about the shapes

the adaptive hough transform we introduce the adaptive hough transform aht as an efficient way of implementing the hough transform ht method for the detection of 2 d shapes the aht uses a small accumulator array and the idea of a flexible iterative coarse to fine accumulation and search strategy to identify significant peaks in the hough parameter spaces the method is substantially superior to the standard ht implementation in both storage and computational requirements in this correspondence we illustrate the ideas of the aht by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2 d parameter spaces we show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape

projection based polygonality measurement measuring the degree to which a shape resembles a polygon referred to as polygonality is a difficult problem due to the intrinsic diversity in the form and distortion of shapes caused by digitization and similarity transformation this paper proposes a generic approach for this problem by performing the measurement in the projection space where the radon image of some primitive shapes which compose the shape becomes apparent the obtained measures take value in 0 1 with 1 corresponding to analytical polygons they are robust to additive noise and boundary distortion and is invariant to similarity transformation the new framework generalizes existing polygonal measures such as triangularity and quadrangularity in addition the ability to estimate polygon s geometric quantities in the projection space allows approximating a shape by analytical polygons the efficiency of the proposed approach is demonstrated through a number of experiments on both synthetic and real data sets

on the intimate relationship between the principle of duality and the hough transform this paper shows the relationship between the principle of duality and the hough transform we show that the definition of the hough transform actually corresponds to an application of the principle of duality in this interpretation the general mathematical concepts of figures and high dimensional coordinates in projective space are re expressed in terms of shapes and mappings used for shape extraction in euclidean space so far some works have demonstrated the equivalence between the definition of several concepts used in pattern matching and the hough transform these works have related the hough transform to particular developments of diverse concepts including template matching the radon transform maximum likelihood estimation and robust statistics the correspondence between the hough transform definition and the principle of duality here developed suggests a more general definition of the hough transform with a deeper meaning in pure geometry this definition introduces the formalism of projective geometry to shape extraction and analysis and thus the ideas properties and geometric relationships in the projective space can have an interpretation for the development of pattern matching techniques we discuss the analogy between the definition of a figure in geometry and the notion of a shape in the hough transform and how this relationship and the generalization of the principle of duality to high dimensional spaces defined by space coordinates are related to the extensions of the hough transform parametric forms are used to develop a dual analytic expression of general forms the intimate relationship between the hough transform and the principle of duality increases our understanding of the dual nature of pattern matching which can now benefit from established results in projective geometry

solvable stochastic differential games in rank one compact symmetric spaces some nonlinear stochastic differential games are formulated in the family of complex and quaternion projective spaces that are among the rank one compact symmetric spaces that consist of the spheres the projective spaces over r c and h and one arising from an exceptional lie algebra called the cayley plane the payoff functionals for the differential games are obtained from some eigenfunctions of the radial part of the laplacians for these riemannian manifolds and these payoffs induce symmetries for the game problems that reduce the required analysis to a radial direction in these manifolds these projective spaces are given a natural riemannian metric from the killing forms of the compact lie groups for these spaces that are the special unitary groups su n and the symplectic groups sp n explicit optimal control strategies are obtained for these differential games and the explicit payoffs are given a countable family of distinct solvable stochastic differential games can be obtained for

robust object detection with interleaved categorization and segmentation this paper presents a novel method for detecting and localizing objects of a visual category in cluttered real world scenes our approach considers object categorization and figure ground segmentation as two interleaved processes that closely collaborate towards a common goal as shown in our work the tight coupling between those two processes allows them to benefit from each other and improve the combined performance the core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the generalized hough transform the resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result this segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background moreover the information from where in the image a hypothesis draws its support is employed in an mdl based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion an extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories including both rigid and articulated objects in addition its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems

real time line detection through an improved hough transform voting scheme the hough transform ht is a popular tool for line detection due to its robustness to noise and missing data however the computational cost associated to its voting scheme has prevented software implementations to achieve real time performance except for very small images many dedicated hardware designs have been proposed but such architectures restrict the image sizes they can handle we present an improved voting scheme for the ht that allows a software implementation to achieve real time performance even on relatively large images our approach operates on clusters of approximately collinear pixels for each cluster votes are cast using an oriented elliptical gaussian kernel that models the uncertainty associated with the best fitting line with respect to the corresponding cluster the proposed approach not only significantly improves the performance of the voting scheme but also produces a much cleaner voting map and makes the transform more robust to the detection of spurious lines

viewpoint independent matching of planar curves in 3d space we present a new approach to match planar curves using the weak perspective projection model this is based on a set of shape parameters that can be extracted from a closed or open contour derived from the original image as a s boundary code in order to reduce the complexity and increase the robustness ofthe matching process the original parameters are reduced to a set ofthree intermediate variables each ofwhich can be calculated independently these variables are contained within a system oflinear equations which de4ne the angles and the ratio ofthe heights ofcorresponding point pairs on the two contours with respect to a 5oating coordinate system the shape matching process is scale and orientation independent and the original parameters that describe the relative pose ofthe two contours in 3d space can be recovered subsequently the approach can be applied to featured and featureless contours to whole and partial contours and is demonstrated on images ofcontours and mechanical parts and tools to recover identity and pose

linear exponential quadratic gaussian control in this technical note an optimal control problem for a linear stochastic system with brownian motion and a cost that is an exponential of a quadratic functional of the state and the control is solved by obtaining explicitly an optimal control and the optimal cost while this solution has been previously obtained the approach given here is direct and elementary and does not use the well known solution methods of the hamilton jacobi bellman equation or the stochastic maximum principle the approach given here presents a basic insight in the solution by providing a simple explanation for the additional term in the riccati equation for the optimal control as compared to the riccati equation for the linear quadratic gaussian control problem

structured mapping problems for linearly structured matrices abstract given an appropriate class of structured matrices s we characterize matrices x and b for which there exists a matrix a s such that a x b and determine all matrices in s mapping x to b we also determine all matrices in s mapping x to b and having the smallest norm we use these results to investigate structured backward errors of approximate eigenpairs and approximate invariant subspaces and structured pseudospectra of structured matrices

using selective correlation coefficient for robust image registration a new method is proposed for robust image registration named selective correlation coefficient in order to search images under ill conditioned illumination or partial occlusion a correlation mask image is generated for selecting pixels of an image before matching the mask image can be derived from a binary coded increment sign image defined from any object image and the template the mask rate of occluded regions is theoretically expected to be 0 5 while unoccluded regions have much lower rate than 0 5 robustness for ill conditioned environment can be realized since inconsistent brightness of occluded regions can be omitted from the mask operation furthermore the mask enhancement procedure is proposed to get more stable robustness the effectiveness of masking increases by the procedure resulting in the rate around 0 7 for masking of occluded regions this paper includes a theoretical modeling and analysis of the proposed method and some experimental results with real images

fuzzy cognitive maps for stereovision matching this paper outlines a method for solving the stereovision matching problem using edge segments as the primitives in stereovision matching the following constraints are commonly used epipolar similarity smoothness ordering and uniqueness we propose a new matching strategy under a fuzzy context in which such constraints are mapped the fuzzy context integrates both fuzzy clustering and fuzzy cognitive maps with such purpose a network of concepts nodes is designed each concept represents a pair of primitives to be matched each concept has associated a fuzzy value which determines the degree of the correspondence the goal is to achieve high performance in terms of correct matches the main findings of this paper are reflected in the use of the fuzzy context that allows building the network of concepts where the matching constraints are mapped initially each concept value is loaded via the fuzzy clustering and then updated by the fuzzy cognitive maps framework this updating is achieved through the influence of the remainder neighboring concepts until a good global matching solution is achieved under this fuzzy approach we gain quantitative and qualitative matching correspondences this method works as a relaxation matching approach and its performance is illustrated by comparative analysis against some existing global matching methods

a general framework for subspace detection in unordered multidimensional data the analysis of large volumes of unordered multidimensional data is a problem confronted by scientists and data analysts every day often it involves searching for data alignments that emerge as well defined structures or geometric patterns in datasets for example straight lines circles and ellipses represent meaningful structures in data collected from electron backscatter diffraction particle accelerators and clonogenic assays also customers with similar behavior describe linear correlations in e commerce databases we describe a general approach for detecting data alignments in large unordered noisy multidimensional datasets in contrast to classical techniques such as the hough transforms which are designed for detecting a specific type of alignment on a given type of input our approach is independent of the geometric properties of the alignments to be detected as well as independent of the type of input data thus it allows concurrent detection of multiple kinds of data alignments in datasets containing multiple types of data given its general nature optimizations developed for our technique immediately benefit all its applications regardless the type of input data

the multiple window parameter transform the multiwindow transform an extension of parameter transform techniques that increase performance and scope by exploiting the long range correlated information contained in multiple portions of an image is presented multiple window transforms allow the extraction of high dimensional features with improvement in accuracy over conventional techniques while keeping linear to low order polynomial computational and space requirements with respect to image size and dimensionality of the features using correlated information provides a direct link between extracted features and supporting regions in the image this coupled with evidence integration techniques is used to suppress noisy or nonexistent feature hypotheses parameter spaces are implemented as constraint satisfaction networks where feature hypotheses with overlapping support in the image compete after an iterative relaxation phase surviving hypotheses have disjoint support forming a segmentation of the image examples show the performance and provide insight about the behavior

the combinatorics of local constraints in model based recognition and localization from sparse data the problem of recognizing what objects are where in the workspace of a robot can be cast as one of searching for a consistent matching between sensory data elements and equivalent model elements in principle this search space is enormous and to control the potential combinatorial explosion constraints between the data and model elements are needed a set of constraints for sparse sensory data that are applicable to a wide variety of sensors are derived and their characteristics are examined known bounds on the complexity of constraint satisfaction problems are used together with explicit estimates of the effectiveness of the constraints derived for the case of sparse noisy three dimensional sensory data to obtain general theoretical bounds on the number of interpretations expected to be consistent with the data it is shown that these bounds are consistent with empirical results reported previously the results are used to demonstrate the graceful degradation of the recognition technique with the presence of noise in the data and to predict the number of data points needed in general to uniquely determine the object being sensed

robust image registration by increment sign correlation abstract a novel and robust statistic as a similarity measure for robust image registration is proposed the statistic is named as increment sign correlation because it is based on the average evaluation of incremental tendency of brightness in adjacent pixels it is formalized to be a binary distribution or a gaussian distribution for a large image size through statistical analysis and modeling by utilizing the proposed statistical model for example we can theoretically determine a reasonable value of threshold for verification of matching this sign correlation can also be proved to expectedly have the constant value 0 5 for any uncorrelated images to a template image and then the property of the constancy can be utilized to analyze the high robustness for occlusion the good performance for the case of saturation or highlight can also be proved through theoretical analysis and fundamental experiments a basic algorithm for image scanning search and registration over a large scene is represented with a technique for a fast version by the branch and bound approach many experimental evidences with real images are provided and discussed

generalized hough transform using regions with homogeneous color a novel generalized hough transform algorithm which makes use of the color similarity between homogeneous segments as the voting criterion is proposed in this paper the input of the algorithm is some regions with homogeneous color these regions are obtained by first pre segmenting the image using the morphological watershed algorithm and then refining the resultant outputs by a region merging algorithm region pairs belonging to the object are selected to generate entries of the reference table for the hough transform every r table entry stores a relative color between the selected region pairs this is done in order to compute the color similarity and in turn generate votes during the voting process and some relevant information to recover the transformation parameters of the object based on the experimental results our algorithm is robust to change of illumination occlusion and distortion of the segmentation output it recognizes objects which were translated rotated scaled and even located in a complex environment

extracting straight lines this paper presents a new approach to the extraction of straight lines in intensity images pixels are grouped into line support regions of similar gradient orientation and then the structure of the associated intensity surface is used to determine the location and properties of the edge the resulting regions and extracted edge parameters form a low level representation of the intensity variations in the image that can be used for a variety of purposes the algorithm appears to be more effective than previous techniques for two key reasons 1 the gradient orientation rather than gradient magnitude is used as the initial organizing criterion prior to the extraction of straight lines and 2 the global context of the intensity variations associated with a straight line is determined prior to any local decisions about participating edge elements

a fast discrete approximation algorithm for the radon transform this paper addresses fast parallel methods for the computation of the radon or hough transform the radon transform of an image is a set of projections of the image taken at different angles its computation is important in image processing and computer vision for problems such as pattern recognition and reconstruction of medical images a unique new method for combining partial results is presented from which an algorithm is constructed that computes a provably good approximation to the discrete radon transform the approximate discrete radon transform adrt algorithm computes 4 n 4 projections through an n times n image in time o n 2 mathrm lg n the majority of previous algorithms are o n 3 the method is quite simple and easy to parallelize a parallel version of the adrt requires only o mathrm lg n parallel steps on o n 2 processors ignoring communication time an additional property of the algorithm is that it can be applied directly to compute the backprojection step of the inverse rt

the fuzzy hough transform feature extraction in medical images identification of anatomical features is a necessary step for medical image analysis automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects but do not attempt to recover the exact or approximate shape of that object for this reason such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail even though the general geometrical shape is similar the authors present an algorithm that detects features in an image based on approximate geometrical models the algorithm is based on the traditional and generalized hough transforms but includes notions from fuzzy set theory the authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ and from this estimate to determine a region of interest around the organ based on this rough estimate of the border location and the derived region of interest the authors find the final improved estimate of the true borders with other subsequently used image processing techniques they present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans and of the left ventricular contours of a dog heart obtained from cine computed tomographic images the authors use this fuzzy hough transform algorithm as part of a larger procedure to automatically identify the myocardial contours of the heart this algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications

stochastic differential games and viscosity solutions of hamilton jacobi bellman isaacs equations in this paper we study zero sum two player stochastic differential games with the help of the theory of backward stochastic differential equations bsdes more precisely we generalize the results of the pioneering work of fleming and souganidis indiana univ math j 38 1989 pp 293 314 by considering cost functionals defined by controlled bsdes and by allowing the admissible control processes to depend on events occurring before the beginning of the game this extension of the class of admissible control processes has the consequence that the cost functionals become random variables however by making use of a girsanov transformation argument which is new in this context we prove that the upper and the lower value functions of the game remain deterministic apart from the fact that this extension of the class of admissible control processes is quite natural and reflects the behavior of the players who always use the maximum of available information its combination with bsde methods in particular that of the notion of stochastic backward semigroups introduced by peng bsde and stochastic optimizations in topics in stochastic analysis science press beijing 1997 allows us then to prove a dynamic programming principle for both the upper and the lower value functions of the game in a straightforward way the upper and the lower value functions are then shown to be the unique viscosity solutions of the upper and the lower hamilton jacobi bellman isaacs equations respectively for this peng s bsde method is extended from the framework of stochastic control theory into that of stochastic differential games

dynamic quantization two adaptive data structures for multidimensional spaces two new data structures are defined for use in multidimensional histogramming their purpose is to cover a parameter space with a limited number of histogram bins so that fine precision is maintained where it is needed the original motivation for these data structures was to implement hough like transforms in high dimensional parameter spaces the two data structures share the ability to adapt to distributions that change with time

face contour extraction from front view images abstract facial feature detection is crucial for visual recognition of human faces this paper reports on a study in detecting face contours from front view id type pictures based on the eye and mouth positions which can be detected by the algorithms proposed in ref 1 g chow and x li pattern recognition 26 1739 1755 1993 a simplified adaptive hough transform aht technique is used to identify straight cheek lines which are approximately vertical from the edge image independently parabolas forming the chin are detected by another aht procedure the location and curvature of the chin line together with the cheek lines characterize the shape of the face this method was tested on over 70 different face images and is shown to produce results with a high degree of accuracy we discuss in detail the specific issues involved in the detections such as the definition of relevant subimage parameter ranges resolution of the accumulator array peak cells and end point determination

possibilistic shell clustering of template based shapes in this paper we present a new type of alternating optimization based possibilistic c shell algorithm for clustering template based shapes a cluster prototype consists of a copy of the template after translation scaling rotation and or affine transformations this extends the capability of shell clustering beyond a few standard geometrical shapes that have been in the literature so far we use a number of 2 d datasets consisting of both synthetic and real world images to illustrate the capability of our algorithm in detecting generic template based shapes in images we also describe a progressive clustering procedure aimed to relax the requirements for a known number of clusters and good initialization as well as new performance measures of shell clustering algorithms

a fast hough transform for segment detection the authors describe a new algorithm for the fast hough transform fht that satisfactorily solves the problems other fast algorithms propose in the literature erroneous solutions point redundance scaling and detection of straight lines of different sizes and needs less storage space by using the information generated by the algorithm for the detection of straight lines they manage to detect the segments of the image without appreciable computational overhead they also discuss the performance and the parallelization of the algorithm and show its efficiency with some examples

a class of constrained clustering algorithms for object boundary extraction boundary extraction is a key task in many image analysis operations this paper describes a class of constrained clustering algorithms for object boundary extraction that includes several well known algorithms proposed in different fields deformable models constrained clustering data ordering and traveling salesman problems the algorithms belonging to this class are obtained by the minimization of a cost function with two terms a quadratic regularization term and an image dependent term defined by a set of weighting functions the minimization of the cost function is achieved by lowpass filtering the previous model shape and by attracting the model units toward the centroids of their attraction regions to define a new algorithm belonging to this class the user has to specify a regularization matrix and a set of weighting functions that control the attraction of the model units toward the data the usefulness of this approach is twofold it provides a unified framework for many existing algorithms in pattern recognition and deformable models and allows the design of new recursive schemes

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

eigenvalues pseudospectrum and structured perturbations we investigate the behavior of eigenvalues under structured perturbations we show that for many common structures such as complex symmetric toeplitz symmetric toeplitz circulant and others the structured condition number is equal to the unstructured condition number for normwise perturbations and prove similar results for real perturbations an exception are complex skewsymmetric matrices we also investigate componentwise complex and real perturbations here hermitian and skew hermitian matrices are exceptional for real perturbations furthermore we characterize the structured complex and real pseudospectrum for a number of structures and show that often there is little or no significant difference to the usual unstructured pseudospectrum

adaptive time varying cancellation of wideband interferences in spread spectrum communications based on time frequency distributions the paper proposes an adaptive method for suppressing wideband interferences in spread spectrum ss communications the proposed method is based on the time frequency representation of the received signal from which the parameters of an adaptive time varying interference excision filter are estimated the approach is based on the generalized wigner hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise the performance of the proposed approach is evaluated in the presence of linear and sinusoidal fm interferences plus white gaussian noise in terms of the snr improvement factor and bit error rate ber

extended hough transform for linear feature detection improving the accuracy of line segment detection reduces the complexity of subsequent high level processing common in cartographic feature detection we developed a new extension to the hough transform and reported on its application to building extraction we expanded the hough space by a third parameter the horizontal or vertical coordinate of the image space to provide incremental information as to the length of the lineal feature being sought using this extended ht transform allowed us to more accurately detect the true length of a line segment in addition we used a bayesian probabilistic approach to process our extended hough space that further increased the accuracy of our extended hough transform

harmonic filters for 3d multichannel data rotation invariant detection of mitoses in colorectal cancer in this paper we present a novel approach for a trainable rotation invariant detection of complex structures in 3d microscopic multichannel data using a nonlinear filter approach the basic idea of our approach is to compute local features in a window around each 3d position and map these features by means of a nonlinear mapping onto new local harmonic descriptors of the local window these local harmonic descriptors are then combined in a linear way to form the output of the filter the optimal combination of the computed local harmonic descriptors is determined in previous training step and allows the filter to be adapted to an arbitrary structure depending on the problem at hand our approach is not limited to scalar valued images and can also be used for vector valued multichannel images such as gradient vector flow fields we present realizations of a scalar valued and a vector valued multichannel filter our proposed algorithm was quantitatively evaluated on colorectal cancer cell lines cells grown under controlled conditions on which we successfully detected complex 3d mitotic structures for a qualitative evaluation we tested our algorithms on human 3d tissue samples of colorectal cancer we compare our results with a steerable filter approach as well as a morphology based approach

pattern recognition using invariants defined from higher order spectra one dimensional inputs a new approach to pattern recognition using in variant parameters based on higher order spectra is presented in particular invariant parameters derived from the bispec trum are used to classify one dimensional shapes the bispec trum which is translation invariant is integrated along straight lines passing through the origin in bifrequency space the phase of the integrated bispectrum is shown to be scale and amplifi cation invariant as well a minimal set of these invariants is selected as the feature vector for pattern classification and a minimum distance classifier using a statistical distance measure is used to classify test patterns the classification technique is shown to distinguish two similar but different bolts given their one dimensional profiles pattern recognition using higher or der spectral invariants is fast suited for parallel implementa tion and has high immunity to additive gaussian noise sim ulation results show very high classification accuracy even for low signal to noise ratios

block decomposition and segmentation for fast hough transform evaluation the decomposition of binary images using rectangular blocks of foreground pixels as primitives is considered based on this type of decomposition a fast method for evaluating the hough transform is introduced a complexity analysis of the proposed block hough transform algorithm sets constraints on the complexity of algorithms used for block decomposition so that the total decomposition and hough transform application time is much less than the time consumed by the usual point hough transform using this analysis we propose two algorithms for the decomposition and segmentation of binary images into rectangular blocks a combination of these methods leads to significant acceleration in the identification of linear features which is demonstrated in various image processing experiments 1999 pattern recognition society published by elsevier science ltd all rights reserved

probabilistic relaxation and the hough transform abstract we discuss the recent developments in probabilistic relaxation which is used as a tool for contextual sensory data interpretation the relationship of this technique with the hough transform is then established focusing on the generalised hough transform ght we show that the label probability updating formula of the probabilistic relaxation process exploiting binary relations between object primitives under the assumption that the primitives convey weak context exhibits very close similarity to the voting function employed by a computationally efficient ght we argue that the relationship could be exploited by importing the positive features of the respective techniques to benefit one another specific suggestions for enhancing the respective techniques are mentioned they include the adoption of the representational efficiency of the hough transform to reduce the computational complexity of probabilistic relaxation vice versa in the case of the generalised hough transform it is pointed out that the effect of an unknown object transformation could be dealt with by means of a parallel cooperative interpretation rather than by means of an exhaustive search through the parameter space of a transformation group this has implications in terms of both the storage and computational requirements of the ght it also opens the possibility of using the ght for detecting objects subject to more complex transformations such as affine or perspective the relationship also suggests the possibility of using alternative voting functions which may speed up the object detection process

recognition of feature curves on 3d shapes using an algebraic approach to hough transforms abstract feature curves are largely adopted to highlight shape features such as sharp lines or to divide surfaces into meaningful segments like convex or concave regions extracting these curves is not sufficient to convey prominent and meaningful information about a shape we have first to separate the curves belonging to features from those caused by noise and then to select the lines which describe non trivial portions of a surface the automatic detection of such features is crucial for the identification and or annotation of relevant parts of a given shape to do this the hough transform ht is a feature extraction technique widely used in image analysis computer vision and digital image processing while for 3d shapes the extraction of salient feature curves is still an open problem thanks to algebraic geometry concepts the ht technique has been recently extended to include a vast class of algebraic curves thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations in the paper for the first time we apply this novel extension of the ht technique to the realm of 3d shapes in order to identify and localize semantic features like patterns decorations or anatomical details on 3d objects both complete and fragments even in the case of features partially damaged or incomplete the method recognizes various features possibly compound and it selects the most suitable feature profiles among families of algebraic curves

scale and orientation invariant generalized hough transform a new approach abstract the conventional generalized hough transform ght is useful for detecting or locating translated 2 dimensional 2d object shapes however a weakness of the conventional ght is that a brute force approach is usually required to handle shape scaling and rotation resulting in the use of a 4d hough counting space hcs a new version of the ght called scale and orientation invariant ght soight is proposed to remove this weakness the improvement is based on the use of half lines and circles to replace the displacement vectors used in the conventional ght for cell value incrementation the required dimensionality of the hcs for the soight is reduced to 2d so that the storage and computation requirements for cell value incrementation and maximum detection in the hcs can be reduced effectively some experimental results are included to demonstrate the applicability of the proposed soight

robust ellipse detection with gaussian mixture models the euclidian distance between gaussian mixtures has been shown to be robust to perform point set registration jian and vemuri 2011 we propose to extend this idea for robustly matching a family of shapes ellipses optimisation is performed with an annealing strategy and the search for occurrences is repeated several times to detect multiple instances of the shape of interest we compare experimentally our approach to other state of the art techniques on a benchmark database for ellipses and demonstrate the good performance of our approach highlightswe extend the framework based on l2 to estimate a parametric family of curves we propose a non isotropic and multidimensional modeling for the density functions we propose a method for detecting multiple instances of an ellipse

an iterative hough procedure for three dimensional object recognition abstract this paper describes an iterative hough procedure for recognizing images of three dimensional objects straight line segments in the image are matched by finding the parameters of a viewing transformation of a three dimensional model consisting of line segments assuming the scale of the object is known there are three orientation and two translation parameters to be estimated initially a sparse regular subset of parameters and transformations is evaluated for goodness of fit then the procedure is repeated by successively subdividing the parameter space near current best estimates or peaks

rotation discriminating template matching based on fourier coefficients of radial projections with robustness to scaling and partial occlusion we consider brightness contrast invariant and rotation discriminating template matching that searches an image to analyze a for a query image q we propose to use the complex coefficients of the discrete fourier transform of the radial projections to compute new rotation invariant local features these coefficients can be efficiently obtained via fft we classify templates in stable and unstable ones and argue that any local feature based template matching may fail to find unstable templates we extract several stable sub templates of q and find them in a by comparing the features the matchings of the sub templates are combined using the hough transform as the features of a are computed only once the algorithm can find quickly many different sub templates in a and it is suitable for finding many query images in a multi scale searching and partial occlusion robust template matching

using orientation codes for rotation invariant template matching abstract a new method for rotation invariant template matching in gray scale images is proposed it is based on the utilization of gradient information in the form of orientation codes as the feature for approximating the rotation angle as well as for matching orientation codes based matching is robust for searching objects in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc we use a two stage framework for realizing the rotation invariant template matching in the first stage histograms of orientation codes are employed for approximating the rotation angle of the object and then in the second stage matching is performed by rotating the object template by the estimated angle matching in the second stage is performed only for the positions which have higher similarity results in the first stage thereby pruning out insignificant locations to speed up the search experiments with real world scenes demonstrate the rotation and brightness invariance of the proposed method for performing object search

a fast memory efficient and parallelizable arc circle segmentation algorithm abstract this paper presents a fast parallelizable arc circle segmentation method with small memory cost firstly the proposed method extracts the edge and skeleton information in image space records every line fragment in a multi fork tree as a node which can be processed concurrently for simplifying the computation every line fragment is approximated by its piecewise linear polygon plg after that local discrete curvature of each dominant point dp on the plg is calculated and used to estimate the local arc s existence approximately the arc s parameters are calculated simultaneously finally the inveracious arcs would be erased according to the results of arc verification and the arc fragments would be jointed by the arc combination and tracking this proposed method can deal with the edge rupture intersection and overlapping conditions furthermore it requires small memory space and the parallel processing technology can be implemented to make it faster

robust photometric invariant features from the color tensor luminance based features are widely used as low level input for computer vision applications even when color data is available the extension of feature detection to the color domain prevents information loss due to isoluminance and allows us to exploit the photometric information to fully exploit the extra information in the color data the vector nature of color data has to be taken into account and a sound framework is needed to combine feature and photometric invariance theory in this paper we focus on the structure tensor or color tensor which adequately handles the vector nature of color images further we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features we circumvent the drawback of unstable photometric invariants by deriving an uncertainty measure to accompany the photometric invariant derivatives the uncertainty is incorporated in the color tensor hereby allowing the computation of robust photometric invariant features the combination of the photometric invariance theory and tensor based features allows for detection of a variety of features such as photometric invariant edges corners optical flow and curvature the proposed features are tested for noise characteristics and robustness to photometric changes experiments show that the proposed features are robust to scene incidental events and that the proposed uncertainty measure improves the applicability of full invariants

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

robust estimation for range image segmentation and reconstruction this correspondence presents a segmentation and fitting method using a new robust estimation technique we present a robust estimation method with high breakdown point which can tolerate more than 80 of outliers the method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type from k samples we choose one set of sample points that determines a best fit equation for the largest homogeneous surface patch in the region this choice is made by measuring a residual consensus resc using a compressed histogram method which is effective at various noise levels after we get the best fit surface parameters the surface patch can be segmented from the region and the process is repeated until no pixel left the method segments the range image into planar and quadratic surfaces the resc method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus a genetic algorithm is also incorporated to accelerate the random search

underdetermined sparse blind source separation of nonnegative and partially overlapped data we study the solvability of sparse blind separation of n nonnegative sources from m linear mixtures in the underdetermined regime m n the geometric properties of the mixture matrix and the sparseness structure of the source matrix are closely related to the identification of the mixing matrix we first illustrate and establish necessary and sufficient conditions for the unique separation for the case of m mixtures and m 1 sources and develop a novel algorithm based on data geometry source sparseness and ell 1 minimization then we extend the results to any order m times n 3 leq m n based on the degree of degeneracy of the columns of the mixing matrix numerical results substantiate the proposed solvability conditions and show satisfactory performance of our approach

linear exponential quadratic stochastic differential games in this technical note a two person noncooperative stochastic differential game described by a linear stochastic equation with a brownian motion and a payoff that is the exponential of a quadratic functional in the state and the control strategies of the two players is explicitly solved the optimal strategies form a nash equilibrium the value of the game is also determined explicitly the solution of this problem is naturally related to a linear quadratic stochastic differential game and a linear exponential quadratic control problem

on spectral variations under bounded real matrix perturbations in this paper we investigate the set of eigenvalues of a perturbed matrix ie509 1 wherea is given and n n is arbitrary we determine a lower bound for thisspectral value set which is exact for normal matricesa with well separated eigenvalues we also investigate the behaviour of the spectral value set under similarity transformations the results are then applied tostability radii which measure the distance of a matrixa from the set of matrices having at least one eigenvalue in a given closed instability domain b

analysis of multicomponent lfm signals by a combined wigner hough transform the aim of the paper is the performance evaluation of a method for the analysis of mono or multicomponent linear frequency modulation lfm signals based on the hough transform of the wigner ville distribution of the signals a closed form expression is found for the signal to noise ratio and the parameter estimation accuracy the overall method as any nonlinear method exhibits a threshold effect nevertheless it is shown to be asymptotically efficient and offers a good rejection capability of the cross terms

use of the two dimensional radon transform to generate a taxonomy of shape for the characterization of abrasive powder particles an image processing technique for the extraction of parameters characteristic of the shape and angularity of abrasive powder particles is proposed the image data are not analyzed directly information concerning angularity and shape is extracted from the parametric transformation of the 2d binarized edge map the transformation process used the radon transform is one to many that is each image point generates in transform space the parameters of all the possible curves on which it may lie and the resulting distribution is an accumulation of that evidence once the image data are segmented the technique has the potential to deliver a comprehensive numerical description of the shape and angularity of the particles under investigation without the need for further interaction by the operator the parameters obtained are arranged into a taxonomy according to their usefulness in categorizing the shapes under inspection the technique is novel in that it offers an analytical definition of a corner and its apex and it automatically selects only those protrusions coincident with the convex hull of the shape and hence those most likely to contribute to the process of abrasion the advantages and potential pitfalls of using the technique are illustrated and discussed using real image data

a note on structured pseudospectra of block matrices in this note we consider the question of equivalence of pseudospectra and structured pseudospectra of block matrices the structures we study are all so called double structures that is the blocks of the given matrix are of the same structure as the block matrix the approach is based on that of non block matrices which are also briefly studied and the use of distance to singularity we also list some open problems and conjectures

discrete time linear quadratic mean field type repeated games perfect incomplete and imperfect information in this paper we study linear quadratic mean field type repeated games with arbitrary number of interacting decision makers different information structures are examined i perfect knowledge of the model ii imperfect knowledge of the model under perfect state observation we provide semi explicit solutions for both non cooperative and cooperative cases the state dynamics is given by a stochastic difference equation that includes mean field terms i e the expected value for both the state and control inputs in addition the cost function incorporates not only the expected value of the states but also the variance of both system state and control inputs the effect of the information is quantified using price of anarchy and price of imperfect information finally some numerical examples are presented

the contracting curve density algorithm fitting parametric curve models to images using local self adapting separation criteria the task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications such as image segmentation pose estimation object tracking and 3 d reconstruction in this article we propose the contracting curve density ccd algorithm as a solution to the curve fitting problem the ccd algorithm extends the state of the art in two important ways first it applies a novel likelihood function for the assessment of a fit between the curve model and the image data this likelihood function can cope with highly inhomogeneous image regions because it is formulated in terms of local image statistics the local image statistics are learned on the fly from the vicinity of the expected curve they provide therefore locally adapted criteria for separating the adjacent image regions these local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties the second contribution is the use of blurred curve models as efficient means for iteratively optimizing the posterior density over possible model parameters these blurred curve models enable the algorithm to trade off two conflicting objectives namely heaving a large area of convergence and achieving high accuracy we apply the ccd algorithm to several challenging image segmentation and 3 d pose estimation problems our experiments with rgb images show that the ccd algorithm achieves a high level of robustness and sub pixel accuracy even in the presence of severe texture shading clutter partial occlusion and strong changes of illumination

a note on structured pseudospectra in this note we study the notion of structured pseudospectra we prove that for toeplitz circulant hankel and symmetric structures the structured pseudospectrum equals the unstructured pseudospectrum we show that this is false for hermitian and skew hermitian structures we generalize the result to pseudospectra of matrix polynomials indeed we prove that the structured pseudospectrum equals the unstructured pseudospectrum for matrix polynomials with toeplitz circulant hankel and symmetric structures we conclude by giving a formula for structured pseudospectra of real matrix polynomials the particular type of perturbations used for these pseudospectra arise in control theory

a probabilistic hough transform abstract the hough transform for straight line detection is considered it is shown that if just a small subset of the edge points in the image selected at random is used as input for the hough transform the performance is often only slightly impaired thus the execution time can be considerably shortened the performance of the resulting probabilistic hough transform is analysed the analysis is supported by experimental evidence

a study of affine matching with bounded sensor error affine transformations of the plane have been used in a number of model based recognition systems because the underlying mathematics are based on exact data in practice various heuristics are used to adapt the methods to real data where there is positional uncertainty this paper provides a precise analysis of affine point matching under uncertainty we obtain an expression for the range of affine invariant values that are consistent with a given set of four points where each image point lies in an disc of uncertainty this range is shown to depend on the actualx y positions of the data points in other words given uncertainty in the data there are no representations that are invariant with respect to the cartesian coordinate system of the data this is problematic for methods such as geometric hashing that are based on affine invariant representations we also analyze the effect that uncertainty has on the probability that recognition methods using affine transformations will find false positive matches we find that there is a significant probability of false positives with even moderate levels of sensor error suggesting the importance of good verification techniques and good grouping techniques

an efficient 2d deformable objects detection and location algorithm abstract this paper presents a complete method for the automatic detection and location of two dimensional objects even in the presence of noise occlusion cluttering and or deformations this method is based on shape information extracted from the edges gradient and only needs a template of the object to be located a new generalized hough transform is proposed to automatically locate rigid objects in the presence of noise occlusion and or cluttering a bayesian scheme uses this rigid objects location algorithm to obtain the deformation of the object the whole method is invariant to rotation scale displacement and minor deformations several examples with real images are presented to show the validity of the method

demisting the hough transform for 3d shape recognition and registration in applying the hough transform to the problem of 3d shape recognition and registration we develop two new and powerful improvements to this popular inference method the first intrinsic hough solves the problem of exponential memory requirements of the standard hough transform by exploiting the sparsity of the hough space the second minimum entropy hough explains away incorrect votes substantially reducing the number of modes in the posterior distribution of class and pose and improving precision our experiments demonstrate that these contributions make the hough transform not only tractable but also highly accurate for our example application both contributions can be applied to other tasks that already use the standard hough transform

the angle of sight signature for two dimensional shape analysis of manufactured objects abstract a new two dimensional 2d shape encoding scheme is introduced which is based on the idea of the angle of sight aos using this scheme a shape can be efficiently transformed into a one dimensional 1d signature by recording the aos vs distance of each boundary point with respect to a shape specific chord of sight cos the cos is selected by using an extension of the notion of shape boundary to the idea of shape specific points and the characteristic ellipse ce the aos signature has many important properties including it is information preserving and thus unique it does not require boundary smoothing it has its own selectable smoothing property it can provide a set of multi scale representations by means of a simple operation it is transformation invariant it is defined at all points it preserves symmetries as well for matching purposes a two level matching process is proposed using a global measure the eccentricity of the ce of a shape and a dissimilarity measure based on the aos signature the encoding and matching techniques developed have been tested with 35 manufactured objects the results obtained show that the aos signature and the two level matching technique are quite effective and reliable for the recognition of 2d shapes of typical manufactured objects

lane departure warning for mobile devices based on a fuzzy representation of images the use of driver assistance systems is a current trend in the car industry however most driver assistance systems require the use of multiple sensors increasing the cost of their implementation in cars and making them inaccessible to many users in this paper we present an efficient lane departure warning system for mobile devices which is real time accurate and accessible to most users on the one hand the accuracy of the system relies on line detection based on hough transforms a combination that has proven to be reliable for this goal in many approaches on the other hand the reduction of the computational time to achieve real time processing is due mainly to a proposed fuzzy representation of images finally we also present various tests that show that the system proposed runs at up to 20 fps on a mobile device with a dual core cpu at 1 0 ghz with a resolution of 320240px

pose determination of a three dimensional object using triangle pairs a procedure is presented to estimate the unconstrained three dimensional location and orientation of an object with a known shape when it is visible in a single image using a generalized hough transform all six parameters of the object position are estimated from the distribution of values determined by matching triples of points on the object to possibly corresponding triples in the image most likely candidates for location are found and then the remaining rotational parameters are evaluated two solutions are generally admitted to the distribution by every match of triangles the number of possible matches is reduced by checking a simple geometric relation among triples even with partial occlusion experiments indicate that the procedure is very reliably accurate although optimization can further improve estimates of the parameters

scale based description and recognition of planar curves and two dimensional shapes the problem of finding a description at varying levels of detail for planar curves and matching two such descriptions is posed and solved in this paper a number of necessary criteria are imposed on any candidate solution method path based gaussian smoothing techniques are applied to the curve to find zeros of curvature at varying levels of detail the result is the generalized scale space image of a planar curve which is invariant under rotation uniform scaling and translation of the curve these properties make the scale space image suitable for matching the matching algorithm is a modification of the uniform cost algorithm and finds the lowest cost match of contours in the scale space images it is argued that this is preferable to matching in a so called stable scale of the curve because no such scale may exist for a given curve this technique is applied to register a landsat satellite image of the strait of georgia b c manually corrected for skew to a map containing the shorelines of an overlapping area