randomized clustering forests for image classification some of the most effective recent methods for content based image classification work by quantizing image descriptors and accumulating histograms of the resulting visual word codes large numbers of descriptors and large codebooks are required for good results and this becomes slow using k means we introduce extremely randomized clustering forests ensembles of randomly created clustering trees and show that they provide more accurate results much faster training and testing and good resistance to background clutter second an efficient image classification method is proposed it combines erc forests and saliency maps very closely with the extraction of image information for a given image a classifier builds a saliency map online and uses it to classify the image we show in several state of the art image classification tasks that this method can speed up the classification process enormously finally we show that the proposed erc forests can also be used very successfully for learning distance between images the distance computation algorithm consists of learning the characteristic differences between local descriptors sampled from pairs of same or different objects these differences are vector quantized by erc forests and the similarity measure is computed from this quantization the similarity measure has been evaluated on four very different datasets and always outperforms the state of the art competitive approaches

shape retrieval using triangle area representation and dynamic space warping in this paper we present a shape retrieval method using triangle area representation for nonrigid shapes with closed contours the representation utilizes the areas of the triangles formed by the boundary points to measure the convexity concavity of each point at different scales or triangle side lengths this representation is effective in capturing both local and global characteristics of a shape invariant to translation rotation and scaling and robust against noise and moderate amounts of occlusion in the matching stage a dynamic space warping dsw algorithm is employed to search efficiently for the optimal least cost correspondence between the points of two shapes then a distance is derived based on the optimal correspondence the performance of our method is demonstrated using four standard tests on two well known shape databases the results show the superiority of our method over other recent methods in the literature

a model based approach to junction detection using radial energy a novel junction detector is presented that fits the neighborhood around a point to a junction model the junction model segments the neighborhood into wedges by determining a set of radial edges the radial edges are invariant to affine transforms creating an affine invariant junction detector the radial edges are evaluated based upon the pixels along the edge the angle between the pixel gradient and the vector to the potential junction point forms the initial basis for the measurement an initial set of radial edges is selected based upon identifying local maximums within a given arc distance an energy function is applied to the resulting radial segmentation and a greedy optimization routine is used to construct the minimal set of radial edges to identify the final junctions a second energy function is used that combines the components of the first energy function with the resulting change in standard deviation by separation into radial segments the junctions with the most energy in their local neighborhoods are selected as potential junctions the neighborhoods about the potential junctions are analyzed to determine if they represent a single line or multiple non parallel lines if the neighborhood represents multiple non parallel lines the point is classified as a junction point the junction detector is tested on several images including both synthetic and real images highlights of radially segmented junction points are displayed for the real images

shape decomposition and classification by searching optimal part pruning sequence representing shapes in terms of meaningful parts is a fundamental problem in shape analysis and part based object representation decomposition methods typically utilize handcrafted geometric rules in a nondata driven manner however these rules are insufficient to mimic human decomposition behavior which limits the applications of decomposition in vision tasks in this paper we propose a novel shape analysis framework that integrates shape decomposition with shape classification in fundamental level we first train probabilistic models for contours and part cuts involved in decomposition process next we construct a data structure called decomposition graph whose nodes represent intermediate contours and whose edges represent part cut selections the decomposition and classification results are obtained by efficiently searching the optimal path on decomposition graph with minimum energy experimental results show that such integrated framework improves the decomposition performance under various shape deformations and achieves competitive classification performance highlightsintegrating decomposition with classification will benefit both of them part based descriptor is robust to shape deformation and intra class variation class and geometric information are complementary in guiding decomposition learning from human decomposition result improves the decomposition performance the decomposition by optimal pruning sequence is solved in polynomial complexity

tensor voting for image correction by global and local intensity alignment this work presents a voting method to perform image correction by global and local intensity alignment the key to our modeless approach is the estimation of global and local replacement functions by reducing the complex estimation problem to the robust 2d tensor voting in the corresponding voting spaces no complicated model for replacement function curve is assumed subject to the monotonic constraint only we vote for an optimal replacement function by propagating the curve smoothness constraint using a dense tensor field our method effectively infers missing curve segments and rejects image outliers applications using our tensor voting approach are proposed and described the first application consists of image mosaicking of static scenes where the voted replacement functions are used in our iterative registration algorithm for computing the best warping matrix in the presence of occlusion our replacement function can be employed to construct a visually acceptable mosaic by detecting occlusion which has large and piecewise constant color furthermore by the simultaneous consideration of color matches and spatial constraints in the voting space we perform image intensity compensation and high contrast image correction using our voting framework when only two defective input images are given

contour detection and hierarchical image segmentation this paper investigates two fundamental problems in computer vision contour detection and image segmentation we present state of the art algorithms for both of these tasks our contour detector combines multiple local cues into a globalization framework based on spectral clustering our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree in this manner we reduce the problem of image segmentation to that of contour detection extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms the automatically generated hierarchical segmentations can be interactively refined by user specified annotations computation at multiple image resolutions provides a means of coupling our system to recognition applications

the high order dispersion analysis based on first passage time probability in financial markets abstract the study of first passage time fpt event about financial time series has gained broad research recently which can provide reference for risk management and investment in this paper a new measurement high order dispersion hod is developed based on fpt probability to explore financial time series the tick by tick data of three chinese stock markets and three american stock markets are investigated we classify the financial markets successfully through analyzing the scaling properties of fpt probabilities of six stock markets and employing hod method to compare the differences of fpt decay curves it can be concluded that long range correlation fat tailed broad probability density function and its coupling with nonlinearity mainly lead to the multifractality of financial time series by applying hod method furthermore we take the fluctuation function of multifractal detrended fluctuation analysis mf dfa to distinguish markets and get consistent results with hod method whereas the hod method is capable of fractionizing the stock markets effectively in the same region we convince that such explorations are relevant for a better understanding of the financial market mechanisms

design of steerable wavelets to detect multifold junctions we propose a framework for the detection of junctions in images although the detection of edges and key points is a well examined and described area the multiscale detection of junction centers especially for odd orders poses a challenge in pattern analysis the goal of this paper is to build optimal junction detectors based on 2d steerable wavelets that are polar separable in the fourier domain the approaches we develop are general and can be used for the detection of arbitrary symmetric and asymmetric junctions the backbone of our construction is a multiscale pyramid with a radial wavelet function where the directional components are represented by circular harmonics and encoded in a shaping matrix we are able to detect m fold junctions in different scales and orientations we provide experimental results on both simulated and real data to demonstrate the effectiveness of the algorithm

learning compositional shape models of multiple distance metrics by information projection this paper presents a novel compositional contour based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations our approach contains two key steps 1 contour feature generation and 2 generative model pursuit for each category we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics these metrics are diverse and complementary with each other to capture various shape deformations we regard the parameterized contour segment plus an additive residual epsilon as a basic subspace namely epsilon ball in the sense that it represents local shape variance under the certain distance metric using these epsilon balls as features we then propose a generative learning algorithm to pursue the compositional shape model which greedily selects the most representative features under the information projection principle in experiments we evaluate our model on several public challenging data sets and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations articulations and background clutter hence boosting system performance

globally consistent correspondence of multiple feature sets using proximal gauss seidel relaxation feature correspondence between two or more images is a fundamental problem towards many computer vision applications the case of correspondence between two images has been intensively studied however few works so far have been concerned with multi image correspondence in this paper we address the problem of establishing a globally consistent correspondence among multiple more than two feature sets given the pairwise feature affinity information our main contribution is to propose a novel optimization framework for solving this problem based on the so called proximal gauss seidel relaxation pgsr the proposed method is distinguished from previous works mainly in three aspects 1 it is more robust to noise and outliers 2 its solution is based on convex relaxation and the principled pgsr method which in general has convergence guarantee 3 the scale of the problem in our method is linear with respect to the number of feature sets making it computationally practical to be used in real world applications experimental results both synthetic and real image datasets have demonstrated the effectiveness and superiority of the proposed method highlightswe study finding globally consistent correspondence from multiple feature sets a novel method is proposed based on proximal gauss seidel relaxation pgsr pgsr is more robust to noise outliers and deformation than competitors the optimization has general convergence guarantee the scale of our formulation is linear w r t the number of feature sets

estimation of ergodicity limits of bag of words modeling for guaranteed stochastic convergence abstract this paper suggests an efficient dual ergodicity limits based bag of words del bow modeling technique the suggested del bow technique estimates two limits of ergodicity of a discrete random variable drv that is formed from the bow classification performance of multiple runs the first limit of ergodicity is estimated with a relatively larger ball of convergence to keep the drv shorter hence both robustness against random initialization and estimation of the optimal model order are realized with a reduced number of iterations once the optimal model order is estimated the radius of ball of convergence is reduced and a second limit of ergodicity is estimated reducing the ball of convergence enlarges the size of the considered performance drv that enhances the classification performance experiments conducted on caltech 101 caltech 256 15 scenes and flower 102 datasets resulted in classification accuracy of 86 91 72 57 90 57 and 90 86 respectively comparison with state of the art techniques shows the excellent performance of the del bow modeling process

bag of contour fragments for robust shape classification shape representation is a fundamental problem in computer vision current approaches to shape representation mainly focus on designing low level shape descriptors which are robust to rotation scaling and deformation of shapes in this paper we focus on mid level modeling of shape representation we develop a new shape representation called bag of contour fragments bcf inspired by classical bag of words bow model in bcf a shape is decomposed into contour fragments each of which is then individually described using a shape descriptor e g the shape context descriptor and encoded into a shape code finally a compact shape representation is built by pooling shape codes in the shape shape classification with bcf only requires an efficient linear svm classifier in our experiments we fully study the characteristics of bcf show that bcf achieves the state of the art performance on several well known shape benchmarks and can be applied to real image classification problem highlightsa new shape representation is proposed by encoding contour fragments in shape the proposed shape representation is compact yet informative the proposed shape representation is robust to shape deformation and conclusion we obtain the state of the art shape classification performance on several bench mark datasets

robust image corner detection through curvature scale space this paper describes a novel method for image corner detection based on the curvature scale space css representation the first step is to extract edges from the original image using a canny detector 1986 the corner points of an image are defined as points where image edges have their maxima of absolute curvature the corner points are detected at a high scale of the css and tracked through multiple lower scales to improve localization this method is very robust to noise and we believe that it performs better than the existing corner detectors an improvement to canny edge detector s response to 45 spl deg and 135 spl deg edges is also proposed furthermore the css detector can provide additional point features curvature zero crossings of image edge contours in addition to the traditional corners

analysis and design of linear finite state machines for signature analysis testing the authors present a theoretical investigation of the aliasing error probability aep in signature analysis testing by means of linear finite state machines lfsms the equations of the resulting markov chain model of the lfsm are solved to determine an exact expression of the aep as a function of the main lfsm features and of the relevant parameters of the testing environment this expression is used to prove criteria for the synthesis of lfsms with minimum asymptotic and transient aep a fundamental lower bound on the aep is presented which represents the performance limit of any lfsm with respect to aliasing minimization it is shown that the aep in machines realizing counters mod 2 sup k 1 is the closest to such a bound in particular periodically reaching it

an improved curvature scale space corner detector and a robust corner matching approach for transformed image identification there are many applications such as image copyright protection where transformed images of a given test image need to be identified the solution to this identification problem consists of two main stages in stage one certain representative features such as corners are detected in all images in stage two the representative features of the test image and the stored images are compared to identify the transformed images for the test image curvature scale space css corner detectors look for curvature maxima or inflection points on planar curves however the arc length used to parameterize the planar curves by the existing css detectors is not invariant to geometric transformations such as scaling as a solution to stage one this paper presents an improved css corner detector using the affine length parameterization which is relatively invariant to affine transformations we then present an improved corner matching technique as a solution to the stage two finally we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results

susan a new approach to low level image processing this paper describes a new approach to low level image processing in particular edge and corner detection and structure preserving noise reduction non linear filtering is used to define which parts of the image are closely related to each individual pixel each pixel has associated with it a local image region which is of similar brightness to that pixel the new feature detectors are based on the minimization of this local image region and the noise reduction method uses this region as the smoothing neighbourhood the resulting methods are accurate noise resistant and fast details of the new feature detectors and of the new noise reduction method are described along with test results

scale affine invariant interest point detectors in this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations our scale and affine invariant detectors are based on the following recent results 1 interest points extracted with the harris detector can be adapted to affine transformations and give repeatable results geometrically stable 2 the characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives the laplacian 3 the affine shape of a point neighborhood is estimated based on the second moment matrix our scale invariant detector computes a multi scale representation for the harris interest point detector and then selects points at which a local measure the laplacian is maximal over scales this provides a set of distinctive points which are invariant to scale rotation and translation as well as robust to illumination changes and limited changes of viewpoint the characteristic scale determines a scale invariant region for each point we extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood an iterative algorithm modifies location scale and neighborhood of each point and converges to affine invariant points this method can deal with significant affine transformations including large scale changes the characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point we present a comparative evaluation of different detectors and show that our approach provides better results than existing methods the performance of our detector is also confirmed by excellent matching resultss the image is described by a set of scale affine invariant descriptors computed on the regions associated with our points

interacting geometric priors for robust multimodel fitting recent works on multimodel fitting are often formulated as an energy minimization task where the energy function includes fitting error and regularization terms such as low level spatial smoothness and model complexity in this paper we introduce a novel energy with high level geometric priors that consider interactions between geometric models such that certain preferred model configurations may be induced we argue that in many applications such prior geometric properties are available and should be fruitfully exploited for example in surface fitting to point clouds the building walls are usually either orthogonal or parallel to each other our proposed energy function is useful in dealing with unknown distributions of data errors and outliers which are often the factors leading to biased estimation furthermore the energy can be efficiently minimized using the expansion move method we evaluate the performance on several vision applications using real data sets experimental results show that our method outperforms the state of the art methods without significant increase in computation

accurate junction detection and characterization in natural images accurate junction detection and characterization are of primary importance for several aspects of scene analysis including depth recovery and motion analysis in this work we introduce a generic junction analysis scheme the first asset of the proposed procedure is an automatic criterion for the detection of junctions permitting to deal with textured parts in which no detection is expected second the method yields a characterization of l y and x junctions including a precise computation of their type localization and scale contrary to classical approaches scale characterization does not rely on the linear scale space first an a contrario approach is used to compute the meaningfulness of a junction this approach relies on a statistical modeling of suitably normalized gray level gradients then exclusion principles between junctions permit their precise characterization we give implementation details for this procedure and evaluate its efficiency through various experiments

state space reconstruction parameters in the analysis of chaotic time series the role of the time window length abstract the most common state space reconstruction method in the analysis of chaotic time series is the method of delays mod many techniques have been suggested to estimate the parameters of mod i e the time delay and the embedding dimension m we discuss the applicability of these techniques with a critical view as to their validity and point out the necessity of determining the overall time window length w for successful embedding emphasis is put on the relation between w and the dynamics of the underlying chaotic system and we suggest to set w p the mean orbital period p is approximated from the oscillations of the time series the procedure is assessed using the correlation dimension for both synthetic and real data for clean synthetic data values of w larger than p always give good results given enough data and thus p can be considered as a lower limit w p for noisy synthetic data and real data an upper limit is reached for w which approaches p for increasing noise amplitude

co segmentation guided hough transform for robust feature matching we present an algorithm that integrates image co segmentation into feature matching and can robustly yield accurate and dense feature correspondences inspired by the fact that correct feature correspondences on the same object typically have coherent transformations we cast the task of feature matching as a density estimation problem in the homography space specifically we project the homographies of correspondence candidates into the parametric hough space in which geometric verification of correspondences can be activated by voting the precision of matching is then boosted on the other hand we leverage image co segmentation which discovers object boundaries to determine relevant voters and speed up hough voting in addition correspondence enrichment can be achieved by inferring the concerted homographies that are propagated between the features within the same segments the recall is hence increased in our approach feature matching and image co segmentation are tightly coupled through an iterative optimization process more and more correct correspondences are detected owing to object boundaries revealed by co segmentation the proposed approach is comprehensively evaluated promising experimental results on four datasets manifest its effectiveness

inexact alternating direction methods for image recovery in the image processing community there have recently been many restoration and reconstruction problems that can be reformulated into linearly constrained convex programming models whose objective functions have separable structures these favorable reformulations have promoted impressive applications of the alternating direction method adm in the field of image processing at each iteration the computation of adm is dominated by solving two subproblems exactly however in many restoration and reconstruction applications it is either impossible or extremely expensive to obtain exact solutions of these adm subproblems this fact urges the development on inexact versions of adm which allow the generated adm subproblems to be solved approximately subject to certain inexactness criteria in this paper we develop some truly implementable inexact adms whose inexactness criteria controlling the accuracy of the adm subproblems are easily implementable the convergence of the new inexact adms will be proved numerical results on several image processing problems will be given to illustrate the effectiveness of the proposed inexact adms

robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching in this paper we are going to present a novel shape similarity retrieval algorithm that can be used to match and recognize 2d objects the match process uses a new multi resolution polygonal shape descriptor that is invariant to scale rotation and translation the shape descriptor equally segments the contour of any shape regardless of its complexity and captures three features around its center including the distance and slope relative to the center all parameters are normalized relative to the max values the novel shape matching algorithm uses the shape descriptor and applies it by linearly scanning a stored set of shapes and measuring the similarity using elastic comparisons of shape segments similarity measurement is achieved by the sum of differences distance measure the multi resolution segmentation provides flexibility for applications that have different time and space requirements while maintaining high accuracy results and the elastic matching adds an advantage when matching partially occluded shapes we applied our algorithms on many test databases including the mpeg 7 shape core experiment and achieved the highest result reported with a score of 84 33 for the mpeg 7 part b similarity test

clustering with hypergraphs the case for large hyperedges the extension of conventional clustering to hypergraph clustering which involves higher order similarities instead of pairwise similarities is increasingly gaining attention in computer vision this is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two in the context of hypergraph clustering the calculation of such higher order similarities on data subsets gives rise to hyperedges almost all previous work on hypergraph clustering in computer vision however has considered the smallest possible hyperedge size due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them in this paper we show that large hyperedges are better from both a theoretical and an empirical standpoint we then propose a novel guided sampling strategy for large hyperedges based on the concept of random cluster models our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs we demonstrate the efficacy of our technique on various higher order grouping problems in particular we show that our approach improves the accuracy and efficiency of motion segmentation from dense long term trajectories

shape classification using the inner distance part structure and articulation are of fundamental importance in computer and human vision we propose using the inner distance to build shape descriptors that are robust to articulation and capture part structure the inner distance is defined as the length of the shortest path between landmark points within the shape silhouette we show that it is articulation insensitive and more effective at capturing part structures than the euclidean distance this suggests that the inner distance can be used as a replacement for the euclidean distance to build more accurate descriptors for complex shapes especially for those with articulated parts in addition texture information along the shortest path can be used to further improve shape classification with this idea we propose three approaches to using the inner distance the first method combines the inner distance and multidimensional scaling mds to build articulation invariant signatures for articulated shapes the second method uses the inner distance to build a new shape descriptor based on shape contexts the third one extends the second one by considering the texture information along shortest paths the proposed approaches have been tested on a variety of shape databases including an articulated shape data set mpeg7 ce shape 1 kimia silhouettes the eth 80 data set two leaf data sets and a human motion silhouette data set in all the experiments our methods demonstrate effective performance compared with other algorithms

heterogeneous feature structure fusion for classification the key to feature fusion for classification is to take advantage of the discriminative and complementary information from different feature sets which can be represented as internal within each feature set or external structures across different feature sets traditional approaches tend to preserve either internal or external structures via certain feature projection some early attempts consider both structures implicitly or indirectly without revealing their relative importance and relevance in feature fusion we propose a new unsupervised heterogeneous structure fusion hsf algorithm that is able to jointly optimize two kinds of structures explicitly and directly via unified feature projection specifically the internal structure is represented based on locality preserving projection lpp and the external structure is captured by canonical correlation analysis cca the objective function of hsf combines two feature structures in a closed form which can be optimized alternately via linear programming and eigenvector methods the hsf solution provides not only the optimal feature projection but also the weights that encode the relative importance between two kinds of feature structures the experimental results on image classification face recognition shape analysis and infrared target recognition demonstrate the effectiveness and efficiency of hsf compared with state of the art methods graphical abstractdisplay omitted highlightsthe external and internal feature structures are jointly optimized for fusion two kinds of feature structures are preserved via a unified feature projection the nonlinear relationship between two kinds of feature structures is revealed the new feature fusion algorithm shows great promise in four classification problems

characterising shape patterns using features derived from best fitting ellipsoids abstract a method is developed to characterise highly irregular shape patterns especially those appearing in biomedical settings a collection of best fitting ellipsoids is found using principal component analysis and features are defined based on these ellipsoids in four different ways the method is defined in a general setting but is illustrated using two dimensional images of dimorphic yeast exhibiting pseudohyphal growth three dimensional images of cancellous bone and three dimensional images of marbling in beef classifiers successfully distinguish between the yeast colonies with a mean classification accuracy of 0 843 sd 0 021 and between cancellous bone from rats in different experimental groups with a mean classification accuracy of 0 745 sd 0 024 a strong correlation r 2 0 797 is found between marbling ratio and a shape feature key aspects of the method are that local shape patterns including orientation are learned automatically from the data and the method applies to objects that are irregular in shape to the point where landmark points cannot be identified between samples

a computational approach for corner and vertex detection corners and vertexes are strong and useful features in computer vision for scene analysis stereo matching and motion analysis here we deal with the development of a computational approach to these important features we consider first a corner model and study analytically its behavior once it has been smoothed using the well known gaussian filter this allows us to clarify the behavior of some well knowncornerness measure based approaches used to detect these points of interest most of these classical approaches appear to detect points that do not correspond to the exact position of the corner a new scale space based approach that combines useful properties from the laplacian and beaudet s measure beaudet 1978 is then proposed in order to correct and detect exactly the corner position an extension of this approach is then developed to solve the problem of trihedral vertex characterization and detection in particular it is shown that a trihedral vertex has two elliptic maxima on extremal contrast surfaces if the contrast is sufficient and this allows us to classify trihedral vertexes in 2 classes vertex and vertex as corner the corner detection approach developed is applied to accurately detect trihedral vertexes using an additional test in order to make a distinction between trihedral vertexes and corners many experiments have been carried out using noisy synthetic data and real images containing corners and vertexes most of the promising results obtained are used to illustrate the experimental section of this paper

scale based detection of corners of planar curves a technique for detecting and localizing corners of planar curves is proposed the technique is based on gaussian scale space which consists of the maxima of absolute curvature of the boundary function presented at all scales the scale space of isolated simple and double corners is first analyzed to investigate the behavior of scale space due to smoothing and interactions between two adjacent corners the analysis shows that the resulting scale space contains line patterns that either persist terminate or merge with a neighboring line next the scale space is transformed into a tree that provides simple but concise representation of corners at multiple scales finally a multiple scale corner detection scheme is developed using a coarse to fine tree parsing technique the parsing scheme is based on a stability criterion that states that the presence of a corner must concur with a curvature maximum observable at a majority of scales experiments were performed to show that the scale space corner detector is reliable for objects with multiple size features and noisy boundaries and compares favorably with other corner detectors tested

shape classification using line segment statistics in this study we use straight line segment statistics for shape analysis for each contour point we consider a continuous portion with length equal to a pre defined percentage of the contour size then we compute the length of the straight line segment between its extreme points for the set of straight line segments we compute statistical moments average and stardard deviation contour shape description is an important field in computer vision this is due to the fact that shape is an important low level image feature in light of this many approaches have been proposed in order to analyze it therefore this paper introduces a very simple yet efficient shape descriptor based on straight line segment statistics for each contour point we consider a continuous portion of the contour with length equal to a pre defined percentage of the contour size then we compute the length of the straight line segment between its extreme points for the set of straight line segments we compute statistical moments average and standard deviation lastly we perform this calculation for different lengths of contour portions the proposed shape descriptor is a powerful tool for shape discrimination it is robust it can characterize a huge set of different classes of shapes and is tolerant to variations in the shapes scale and orientation classification results of the proposed method overcome traditional methods found in literature proving that it is an efficient tool for shape analysis

hierarchical segmentation using tree based shape spaces current trends in image segmentation are to compute a hierarchy of image segmentations from fine to coarse a classical approach to obtain a single meaningful image partition from a given hierarchy is to cut it in an optimal way following the seminal approach of the scale set theory while interesting in many cases the resulting segmentation being a non horizontal cut is limited by the structure of the hierarchy in this paper we propose a novel approach that acts by transforming an input hierarchy into a new saliency map it relies on the notion of shape space a graph representation of a set of regions extracted from the image each region is characterized with an attribute describing it we weigh the boundaries of a subset of meaningful regions local minima in the shape space by extinction values based on the attribute this extinction based saliency map represents a new hierarchy of segmentations highlighting regions having some specific characteristics each threshold of this map represents a segmentation which is generally different from any cut of the original hierarchy this new approach thus enlarges the set of possible partition results that can be extracted from a given hierarchy qualitative and quantitative illustrations demonstrate the usefulness of the proposed method

multiscale distance matrix for fast plant leaf recognition in this brief we propose a novel contour based shape descriptor called the multiscale distance matrix to capture the shape geometry while being invariant to translation rotation scaling and bilateral symmetry the descriptor is further combined with a dimensionality reduction to improve its discriminative power the proposed method avoids the time consuming pointwise matching encountered in most of the previously used shape recognition algorithms it is therefore fast and suitable for real time applications we applied the proposed method to the task of plan leaf recognition with experiments on two data sets the swedish leaf data set and the icl leaf data set the experimental results clearly demonstrate the effectiveness and efficiency of the proposed descriptor

junctions detection classification and reconstruction junctions are important features for image analysis and form a critical aspect of image understanding tasks such as object recognition we present a unified approach to detecting classifying and reconstructing junctions in images our main contribution is a modeling of the junction which is complex enough to handle all these issues and yet simple enough to admit an effective dynamic programming solution we use a template deformation framework along with a gradient criterium to detect radial partitions of the template we use the minimum description length principle to obtain the optimal number of partitions that best describes the junction the kona detector presented by parida et al 1997 is an implementation of this model we demonstrate the stability and robustness of the detector by analyzing its behavior in the presence of noise using synthetic controlled apparatus we also present a qualitative study of its behavior on real images

a comparison of affine region detectors the paper gives a snapshot of the state of the art in affine covariant region detectors and compares their performance on a set of test images under varying imaging conditions six types of detectors are included detectors based on affine normalization around harris mikolajczyk and schmid 2002 schaffalitzky and zisserman 2002 and hessian points mikolajczyk and schmid 2002 a detector of maximally stable extremal regions proposed by matas et al 2002 an edge based region detector tuytelaars and van gool 1999 and a detector based on intensity extrema tuytelaars and van gool 2000 and a detector of salient regions proposed by kadir zisserman and brady 2004 the performance is measured against changes in viewpoint scale illumination defocus and image compression the objective of this paper is also to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework

junction detection and grouping with probabilistic edge models and bayesian a in this paper we propose and integrate two bayesian methods one of them for junction detection and the other one for junction grouping our junction detection method relies on a probabilistic edge model and a log likelihood test our junction grouping method relies on 6nding connecting paths between pairs of junctions path searching is performed by applying a bayesian a algorithm such algorithm uses both an intensity and geometric model for de6ning the rewards of a partial path and prunes those paths with low rewards we have extended such a pruning with an additional rule which favors the stability of longer paths against shorter ones we have tested experimentally the e ciency and robustness of the methods in an indoor image sequence 2002 pattern recognition society published by elsevier science ltd all rights reserved

learning shape classes using a mixture of tree unions this paper poses the problem of tree clustering as that of fitting a mixture of tree unions to a set of sample trees the tree unions are structures from which the individual data samples belonging to a cluster can be obtained by edit operations the distribution of observed tree nodes in each cluster sample is assumed to be governed by a bernoulli distribution the clustering method is designed to operate when the correspondences between nodes are unknown and must be inferred as part of the learning process we adopt a minimum description length approach to the problem of fitting the mixture model to data we make maximum likelihood estimates of the bernoulli parameters the tree unions and the mixing proportions are sought so as to minimize the description length criterion this is the sum of the negative logarithm of the bernoulli distribution and a message length criterion that encodes both the complexity of the union trees and the number of mixture components we locate node correspondences by minimizing the edit distance with the current tree unions and show that the edit distance is linked to the description length criterion the method can be applied to both unweighted and weighted trees we illustrate the utility of the resulting algorithm on the problem of classifying 20 shapes using a shock graph representation

robust parameter estimation in computer vision estimation techniques in computer vision applications must estimate accurate model parameters despite small scale noise in the data occasional large scale measurement errors outliers and measurements from multiple populations in the same data set increasingly robust estimation techniques some borrowed from the statistics literature and others described in the computer vision literature have been used in solving these parameter estimation problems ideally these techniques should effectively ignore the outliers and measurements from other populations treating them as outliers when estimating the parameters of a single population two frequently used techniques are least median of squares lms p j rousseeuw j amer statist assoc 79 1984 pp 871 880 and m estimators robust statistics the approach based on influence functions f r hampel et al john wiley 1986 robust statistics p j huber john wiley 1981 lms handles large fractions of outliers up to the theoretical limit of 50 for estimators invariant to affine changes to the data but has low statistical efficiency m estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized while robust estimators have been used in a variety of computer vision applications three are considered here in analysis of range images images containing depth or x y z measurements at each pixel instead of intensity measurements robust estimators have been used successfully to estimate surface model parameters in small image regions in stereo and motion analysis they have been used to estimate parameters of what is called the fundamental matrix which characterizes the relative imaging geometry of two cameras imaging the same scene recently robust estimators have been applied to estimating a quadratic image to image transformation model necessary to create a composite mosaic image from a series of images of the human retina in each case a straightforward application of standard robust estimators is insufficient and carefully developed extensions are used to solve the problem

path similarity skeleton graph matching this paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes the main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints in contrast to typical tree or graph matching methods we do not consider the topological graph structure our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures the proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases the skeletons are pruned by contour partitioning with discrete curve evolution which implies that the endpoints of skeleton branches correspond to visual parts of the objects the experimental results demonstrate that our method is able to produce correct results in the presence of articulations stretching and contour deformations

accelerated hypothesis generation for multistructure data via preference analysis random hypothesis generation is integral to many robust geometric model fitting techniques unfortunately it is also computationally expensive especially for higher order geometric models and heavily contaminated data we propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting we show that residual sorting innately encodes the probability of two points having arisen from the same model and is obtained without recourse to domain knowledge e g keypoint matching scores typically used in previous sampling enhancement methods more crucially our approach encourages sampling within coherent structures and thus can very rapidly generate all inlier minimal subsets that maximize the robust criterion sampling within coherent structures also affords a natural ability to handle multistructure data a condition that is usually detrimental to other methods the result is a sampling scheme that offers substantial speed ups on common computer vision tasks such as homography and fundamental matrix estimation we show on many computer vision data especially those with multiple structures that ours is the only method capable of retrieving satisfactory results within realistic time budgets

matching widely separated views based on affine invariant regions invariant regions are self adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene such regions can be extracted directly from a single image they are then described by a set of invariant features which makes it relatively easy to match them between views even under wide baseline conditions in this contribution two methods to extract invariant regions are presented the first one starts from corners and uses the nearby edges while the second one is purely intensity based as a matter of fact the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit this yields more correspondences and a system that can deal with a wider range of images to increase the robustness of the system two semi local constraints on combinations of region correspondences are derived one geometric the other photometric they allow to test the consistency of correspondences and hence to reject falsely matched regions experiments on images of real world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach

can nonlinear compactors be better than linear ones this paper presents a new analytical method for estimating compaction quality based on the entropy maximization of the entropy of the signature results in an increase of compaction quality the paper studies the influence of the architecture of a compactor on the entropy growth this approach is suitable for nonlinear compactors widely known linear feedback shift registers lfsrs and the counting compactors as well useful theorems to determine entropy growth during the test have been given and proven furthermore the class of nonlinear compactors based on shift registers named cyclic feedback shift registers cfsrs which guarantee the maximal entropy growth and the aliasing probability of 2 sup n is found it is shown that they are considerably better than the simplest linear compactor feedback shift register and in many cases are quite as good as linear compactors lfsrs having primitive polynomials on the opposite side compactors based on counters are far worse than cfsrs the family of cfsrs is much greater than the already explored family of lfsrs and some cfsrs require less overhead

image stitching by line guided local warping with global similarity constraint abstract low textured image stitching remains a challenging problem it is difficult to achieve good alignment of images and it is easy to break images structures are often broken due to insufficient and unreliable point correspondences moreover because of the viewpoint variations between multiple images the stitched images suffer from projective distortions to solve these problems this paper presents a line guided local warping method with a global similarity constraint for image stitching line features which serve well for geometric descriptions and scene constraints are employed to guide image stitching accurately on one hand the line features are integrated into a local warping model through a designed weight function on the other hand line features are adopted to impose strong geometric constraints including line correspondence and line colinearity to improve the stitching performance through mesh optimization to mitigate projective distortions we adopt a global similarity constraint which is integrated with the projective warps via a designed weight strategy this constraint causes the final warp to slowly change from a projective to a similarity transformation across the image finally the images undergo a two stage alignment scheme that provides accurate alignment and reduces projective distortion we evaluate our method on a series of images and compare it with several other methods the experimental results demonstrate that the proposed method provides a convincing stitching performance and that it outperforms other state of the art methods

shape vocabulary a robust and efficient shape representation for shape matching in this paper a learning based shape descriptor for shape matching is demonstrated formulated in a bag of words like framework the proposed method summarizes the local features extracted from certain shape to generate a integrated representation it contributes to the speed up of shape matching since the distance metric in the vector space analysis can be directly applied to compare the constructed global descriptors eliminating the time consuming stage of local feature matching similar to the philosophy in spatial pyramid matching a strategy for feature division is applied in the phase of encoded feature pooling and vocabulary learning which helps to construct a more discriminative descriptor incorporating both global and local information also a local contour based feature extraction method is designed for 2d shapes while significant properties of the local contours are inspected for the design of feature division rules the designed local feature extraction method and the feature division rules manage to reduce the variances of shape representation due to the changes in rotation in addition to 2d shape we also present a simple and natural method to extend the proposed method to the scenario of 3d shape representation the proposed shape descriptor is validated on several benchmark data sets for evaluating 2d and 3d shape matching algorithms and it is observed that the investigated shape descriptor maintains superior discriminative power as well as high time efficiency

mtos a tree of shapes for multivariate images the topographic map of a gray level image also called tree of shapes provides a high level hierarchical representation of the image contents this representation invariant to contrast changes and to contrast inversion has been proved very useful to achieve many image processing and pattern recognition tasks its definition relies on the total ordering of pixel values so this representation does not exist for color images or more generally multivariate images common workarounds such as marginal processing or imposing a total order on data are not satisfactory and yield many problems this paper presents a method to build a tree based representation of multivariate images which features marginally the same properties of the gray level tree of shapes briefly put we do not impose an arbitrary ordering on values but we only rely on the inclusion relationship between shapes in the image definition domain the interest of having a contrast invariant and self dual representation of multivariate image is illustrated through several applications filtering segmentation and object recognition on different types of data color natural images document images satellite hyperspectral imaging multimodal medical imaging and videos

antiextensive connected operators for image and sequence processing this paper deals with a class of morphological operators called connected operators these operators filter the signal by merging its flat zones as a result they do not create any new contours and are very attractive for filtering tasks where the contour information has to be preserved this paper shows that connected operators work implicitly on a structured representation of the image made of flat zones the max tree is proposed as a suitable and efficient structure to deal with the processing steps involved in antiextensive connected operators a formal definition of the various processing steps involved in the operator is proposed and as a result several lines of generalization are developed first the notion of connectivity and its definition are analyzed several modifications of the traditional approach are presented they lead to connected operators that are able to deal with texture they also allow the definition of connected operators with less leakage than the classical ones second a set of simplification criteria are proposed and discussed they lead to simplicity entropy and motion oriented operators the problem of using a nonincreasing criterion is analyzed its solution is formulated as an optimization problem that can be very efficiently solved by a viterbi 1979 algorithm finally several implementation issues are discussed showing that these operators can be very efficiently implemented

a probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix the perspective projections of n physical points on two views stereovision are constrained as soon as n 8 however to prove in practice the existence of a rigid motion between two images more than 8 point matches are desirable in order to compensate for the limited accuracy of the matches in this paper we propose a computational definition of rigidity and a probabilistic criterion to rate the meaningfulness of a rigid set as a function of both the number of pairs of points n and the accuracy of the matches this criterion yields an objective way to compare say precise matches of a few points and approximate matches of a lot of points it gives a yes no answer to the question could this rigid points correspondence have occurred by chance since it guarantees that the expected number of meaningful rigid sets found by chance in a random distribution of points is as small as desired it also yields absolute accuracy requirements for rigidity detection in the case of non matched points and optimal values of n depending on the expected accuracy of the matches and on the proportion of outliers we use it to build an optimized random sampling algorithm that is able to detect a rigid motion and estimate the fundamental matrix when the set of point matches contains up to 90 of outliers which outperforms the best currently known methods like m estimators lmeds classical ransac and tensor voting

in defense of the eight point algorithm the fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras and the eight point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches it has the advantage of simplicity of implementation the prevailing view is however that it is extremely susceptible to noise and hence virtually useless for most purposes this paper challenges that view by showing that by preceding the algorithm with a very simple normalization translation and scaling of the coordinates of the matched points results are obtained comparable with the best iterative algorithms this improved performance is justified by theory and verified by extensive experiments on real images

on the algorithmic implementation of multiclass kernel based vector machines in this paper we describe the algorithmic implementation of multiclass kernel based vector machines our starting point is a generalized notion of the margin to multiclass problems using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic objective function unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks our notion of margin yields a direct method for training multiclass predictors by using the dual of the optimization problem we are able to incorporate kernels with a compact set of constraints and decompose the dual problem into multiple optimization problems of reduced size we describe an efficient fixed point algorithm for solving the reduced optimization problems and prove its convergence we then discuss technical details that yield significant running time improvements for large datasets finally we describe various experiments with our approach comparing it to previously studied kernel based methods our experiments indicate that for multiclass problems we attain state of the art accuracy

statistics of natural images scaling in the woods we study the statistics of an ensemble of images taken in the woods distributions of local quantities such as contrast are scale invariant and have nearly exponential tails power spectra exhibit scaling with a nontrivial exponent these data limit the information content of natural images and point to the importance of gain control strategies in visual processing

a variational method for multiple image blending the main aim of this paper is to develop an algorithm for blending of multiple images in the image stitching process our idea is to propose a variational method containing an energy functional to determine both a stitched image and weighting mask functions of multiple input images for image blending the existence of the solution of the proposed energy functional is shown we also present an alternative minimizing algorithm to solve the proposed model numerically and show the convergence of this algorithm experimental results show that the proposed model works effectively and efficiently and that the proposed method is competitive with the tested existing methods under noisy conditions

hierarchical projective invariant contexts for shape recognition shape descriptors play an important role in various computer vision tasks many existing descriptors are typically derived from pair wise measures such as distances and angles which may vary with severe geometrical deformations including affine and projective transformations in this paper we propose a new shape descriptor from a newly developed projective invariant the characteristic number cn this new descriptor is invariant to projective or perspective transformations by computing cn values on a series of 5 sample points along the shape contour with the intervals varying from coarse to fine this hierarchical strategy yields a compacter descriptor so that the time complexity for both descriptor construction and shape matching are less or comparable to many existing methods we also use the derived points out of the contour and the ratio of two invariant values in order to improve the stability at finer scales and robustness to noise we demonstrate the performance of the descriptor by comparing with the state of the art on the mcd and other public shape sets with severe perspective transformations and other type variations including noise missing parts and articulated deformations graphical abstract highlightswe propose a new shape descriptor hcnc invariant to projective deformations hcnc is compact with a hierarchical strategy rendering efficient matching hcnc is robust to noise part missing and articulated deformations

guided mlesac faster image transform estimation by using matching priors mlesac is an established algorithm for maximum likelihood estimation by random sampling consensus devised for computing multiview entities like the fundamental matrix from correspondences between image features a shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences this paper explains the consequences of that omission and describes how the algorithm s theoretical standing and practical performance can be enhanced by deriving estimates of these prior probabilities using the priors in guided mlesac is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter this paper describes two further modifications to guided mlesac the first shows how all putative matches rather than just the best from a particular feature can be taken forward into the sampling stage albeit at the expense of additional computation the second suggests how to propagate the output from one frame forward to successive frames the additional information makes guided mlesac computationally realistic at video rates for correspondence sets modeled by two transformations and clutter

aliasing probability calculations for arbitrary compaction under independently selected random test vectors this paper discusses a systematic methodology for calculating the exact aliasing probability associated with schemes that use an arbitrary finite state machine to compact the response of a combinational circuit to a sequence of independently selected random test input vectors the proposed approach identifies the strong influence of fault activation probabilities on the probability of aliasing and uses an asymmetric error model to simultaneously track the states of two fictitious compactors one driven by the response of the fault free combinational circuit and one driven by the response of the faulty combinational circuit by deriving the overall markov chain that describes the combined behavior of these two compactors we are able to calculate the exact aliasing probability for any test sequence length in particular for long enough sequences the probability of aliasing is shown to only depend on the stationary distribution of the markov chain the insights provided by our analysis are used to evaluate the testing performance of simple examples of nonlinear compactors and to demonstrate regimes where they exhibit lower aliasing probability than linear compactors with the same number of states finally by establishing connections with previous work that evaluated aliasing probability in linear compactors our analysis clarifies the role played by the entropy of the stationary distribution of the compactor states

two bayesian methods for junction classification we propose two bayesian methods for junction classification which evolve from the kona method a region based method and an edge based method our region based method computes a one dimensional 1 d profile where wedges are mapped to intervals with homogeneous intensity these intervals are found through a growing and merging algorithm driven by a greedy rule on the other hand our edge based method computes a different profile which maps wedge limits to peaks of contrast and these peaks are found through thresholding followed by nonmaximum suppression experimental results show that both methods are more robust and efficient than the kona method and also that the edge based method outperforms the region based one

automatic panoramic image stitching using invariant features this paper concerns the problem of fully automated panoramic image stitching though the 1d problem single axis of rotation is well studied 2d or multi row stitching is more difficult previous approaches have used human input or restrictions on the image sequence in order to establish matching images in this work we formulate stitching as a multi image matching problem and use invariant local features to find matches between all of the images because of this our method is insensitive to the ordering orientation scale and illumination of the input images it is also insensitive to noise images that are not part of a panorama and can recognise multiple panoramas in an unordered image dataset in addition to providing more detail this paper extends our previous work in the area brown and lowe 2003 by introducing gain compensation and automatic straightening steps

extremal regions detection guided by maxima of gradient magnitude a problem of computer vision applications is to detect regions of interest under different imaging conditions the state of the art maximally stable extremal regions msers detects affine covariant regions by applying all possible thresholds on the input image and through three main steps including 1 making a component tree of extremal regions evolution 2 obtaining region stability criterion and 3 cleaning up the mser performs very well but it does not consider any information about the boundaries of the regions which are important for detecting repeatable extremal regions we have shown in this paper that employing prior information about boundaries of regions results in a novel region detector algorithm that not only outperforms mser but avoids the mser s rather complicated steps of enumeration and the cleaning up to employ the information about the region boundaries we introduce maxima of gradient magnitudes mgms which are shown to be points that are mostly around the boundaries of the regions having found the mgms the method obtains a global criterion for each level of the input image which is used to find extremum levels els the found els are then used to detect extremal regions the proposed algorithm which is called extremal regions of extremum levels erel has been tested on the public benchmark data set of mikolajczyk the obtained experimental results show that the inclusion of region boundaries through mgms results in a detector that detects regions with high repeatability scores and is more robust against noise compared with mser

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

on the detection of dominant points on digital curves a parallel algorithm is presented for detecting dominant points on a digital closed curve the procedure requires no input parameter and remains reliable even when features of multiple sizes are present on the digital curve the procedure first determines the region of support for each point based on its local properties then computes measures of relative significance e g curvature of each point and finally detects dominant points by a process of nonmaximum suppression this procedure leads to the observation that the performance of dominant points detection depends not only on the accuracy of the measure of significance but also on the precise determination of the region of support this solves the fundamental problem of scale factor selection encountered in various dominant point detection algorithms the inherent nature of scale space filtering in the procedure is addressed and the performance of the procedure is compared to those of several other dominant point detection algorithms using a number of examples

mode seeking over permutations for rapid geometric model fitting in this paper we show how to exploit the statistics of permutations to perform rapid hypothesis sampling for robust geometric model fitting the permutations encapsulate data preferences for random model hypotheses and we demonstrate that such permutations exhibit a clustering based on the membership of inliers to genuine structures in the data we perform non parametric mode seeking in the space of permutations the results of which are used to derive a set of sampling distributions for minimal subset selection our method fully takes advantage of the allocated time by using only the most relevant subsets of the input data for hypothesis generation moreover it can naturally handle data with multiple structures a condition that is usually disastrous for other methods that rely on ad hoc inlier probabilities such as keypoint matching scores compared to others our method consistently returns a much lower time to first solution and median fitting error given the same run time

robust symbolic representation for shape recognition and retrieval a new method for shape recognition and retrieval is proposed here the suggested algorithm is based on several steps the algorithm analyzes the contour of pairs of shapes their contours are recovered and represented by a pair of n points obtained by linear interpolation given two points p i and q j from the two shapes the cost of their matching is evaluated by using the shape context and by using dynamic programming the best matching between the point sets is obtained dynamic programming not only recovers the best matching but also identifies occlusions i e points in the two shapes which cannot be properly matched given the correspondence between the two point sets the two contours are aligned using procrustes analysis after alignment each contour is transformed into a string of symbols and a modified version of edit distance is used to compute the similarity between strings of symbols finally recognition and retrieval are obtained by a simple nearest neighbor procedure the algorithm has been tested on a large set of shape databases kimia mpeg 7 natural silhouette database gesture database marine database swedish leaf database diatom database eth 80 3d object database providing performances for both in recognition and in retrieval superior to most of previously proposed approaches

sampling minimal subsets with large spans for robust estimation when sampling minimal subsets for robust parameter estimation it is commonly known that obtaining an all inlier minimal subset is not sufficient the points therein should also have a large spatial extent this paper investigates a theoretical basis behind this principle based on a little known result which expresses the least squares regression as a weighted linear combination of all possible minimal subset estimates it turns out that the weight of a minimal subset estimate is directly related to the span of the associated points we then derive an analogous result for total least squares which unlike ordinary least squares corrects for errors in both dependent and independent variables we establish the relevance of our result to computer vision by relating total least squares to geometric estimation techniques as practical contributions we elaborate why naive distance based sampling fails as a strategy to maximise the span of all inlier minimal subsets produced in addition we propose a novel method which unlike previous methods can consciously target all inlier minimal subsets with large spans

non rigid graph registration using active testing search we present a new approach for matching sets of branching curvilinear structures that form graphs embedded in mathbb r 2 or mathbb r 3 and may be subject to deformations unlike earlier methods ours does not rely on local appearance similarity nor does require a good initial alignment furthermore it can cope with non linear deformations topological differences and partial graphs to handle arbitrary non linear deformations we use gaussian process regressions to represent the geometrical mapping relating the two graphs in the absence of appearance information we iteratively establish correspondences between points update the mapping accordingly and use it to estimate where to find the most likely correspondences that will be used in the next step to make the computation tractable for large graphs the set of new potential matches considered at each iteration is not selected at random as with many ransac based algorithms instead we introduce a so called active testing search strategy that performs a priority search to favor the most likely matches and speed up the process we demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data retinal fundus images and microscopy image stacks acquired at very different resolutions

a robust approach for object matching and classification using partial dominant orientation descriptor this paper introduces a novel approach to measure the correspondence between objects and exploit it for object and image classification tasks using the proposed partial dominant orientation descriptor pdod in particular the object is represented by a set of stable and informative key locations sampled using difference of gaussian the proposed pdod at each extracted key location takes into account the position and partially computes the dominant orientation of other key locations relative to it thus offering a global distinctive and discriminative characterization this allows us to learn features that are largely invariant to common image transformations including changes in object colors and textures the correspondence in between two objects is performed by finding for each key location in one object the key location in the other object that has the most similar descriptor object classification proceeds by assigning the most relevant category that has maximally similar stored prototype objects to the query object using k nearest neighbors algorithm with adaptive object distance for efficiency we further investigate pdod for image classification by developing powerful image representations based on the popular bag of words model the extensive experiments demonstrate that the proposed approach greatly improves the matching and classification results while achieving the state of the art performances on several challenging benchmark datasets the obtained results suggest also broader applicability to other classification modalities we propose a novel descriptor for complex object matching and classification the proposed descriptor combines both local and global properties of interest points the proposed descriptor is highly distinctive and insensitive to common image transformations adaptive object distance is proposed to measure the similarity between objects we compare our approach with many existing methods and we achieve the state of the art performances on several benchmarks

tree based morse regions a topological approach to local feature detection this paper introduces a topological approach to local invariant feature detection motivated by morse theory we use the critical points of the graph of the intensity image revealing directly the topology information as initial interest points critical points are selected from what we call a tree based shape space in particular they are selected from both the connected components of the upper level sets of the image the max tree and those of the lower level sets the min tree they correspond to specific nodes on those two trees 1 to the leaves extrema and 2 to the nodes having bifurcation saddle points we then associate to each critical point the largest region that contains it and is topologically equivalent in its tree we call such largest regions the tree based morse regions tbmrs the tbmr can be seen as a variant of maximally stable extremal region mser which are contrasted regions contrarily to mser tbmr relies only on topological information and thus fully inherit the invariance properties of the space of shapes e g invariance to affine contrast changes and covariance to continuous transformations in particular tbmr extracts the regions independently of the contrast which makes it truly contrast invariant furthermore it is quasi parameter free tbmr extraction is fast having the same complexity as mser experimentally tbmr achieves a repeatability on par with state of the art methods but obtains a significantly higher number of features both the accuracy and robustness of tbmr are demonstrated by applications to image registration and 3d reconstruction

affine morphological multiscale analysis of corners andmultiple junctions in this paper we study the application of the affine morphological scale space amss to the analysis of singularities corners or multiple junctions of the shapes present in a 2 d image we introduce a new family of travelling wave solutions of amss which determines the evolution of the initial shapes given by conics we characterize the evolution of corners accross the scales according to their angle we develop a numerical algorithm to compute amss accross the scales and we present some experimental results about corners and multiple junction detection

coding images with local features we develop a qualitative measure for the completeness and complementarity of sets of local features in terms of covering relevant image information the idea is to interpret feature detection and description as image coding and relate it to classical coding schemes like jpeg given an image we derive a feature density from a set of local features and measure its distance to an entropy density computed from the power spectrum of local image patches over scale our measure is meant to be complementary to existing ones after task usefulness of a set of detectors has been determined regarding robustness and sparseness of the features the scheme can be used for comparing their completeness and assessing effects of combining multiple detectors the approach has several advantages over a simple comparison of image coverage it favors response on structured image parts penalizes features in purely homogeneous areas and accounts for features appearing at the same location on different scales combinations of complementary features tend to converge towards the entropy while an increased amount of random features does not we analyse the complementarity of popular feature detectors over different image categories and investigate the completeness of combinations the derived entropy distribution leads to a new scale and rotation invariant window detector which uses a fractal image model to take pixel correlations into account the results of our empirical investigations reflect the theoretical concepts of the detectors

a new framework for designing and analyzing bist techniques and zero aliasing compression a general framework for shift register based signature analysis is presented and a mathematical model for this framework based on coding theory is developed there are two key features of this formulation first it allows for uniform treatment of lfsr misr and multiple misr based signature analyzer in addition using this formulation a new compression scheme for multiple output cut is proposed this scheme referred to as multiinput lfsr has the potential to achieve better aliasing than other schemes such as the multiple misr scheme of comparable hardware complexity several results on aliasing are presented and certain known results are shown to be direct consequences of the formulation also developed are error models that take into account the circuit topology and the effect of faults at the outputs using these models exact closed form expressions for aliasing probability are developed a closed form aliasing expression for misr under an independent error model is provided

lsd a fast line segment detector with a false detection control we propose a linear time line segment detector that gives accurate results a controlled number of false detections and requires no parameter tuning this algorithm is tested and compared to state of the art algorithms on a wide set of natural images

new types of nonlinear auto correlations of bivariate data and their applications abstract the paper introduces new types of nonlinear correlations between bivariate data sets and derives nonlinear auto correlations on the same data set these auto correlations are of different types to match signals with different types of nonlinearities examples are cited in all cases to make the definitions meaningful next correlogram diagrams are drawn separately in all cases from these diagrams proper time lags delays are determined these give rise to independent coordinates of the attractors finally three dimensional attractors are reconstructed in each case separately with the help of these independent coordinates moreover for the purpose of making proper distinction between the signals the attractors so reconstructed are quantified by a new technique called ellipsoid fit

robust line matching through line point invariants this paper is about line matching by line point invariants which encode local geometric information between a line and its neighboring points specifically two kinds of line point invariants are introduced in this paper one is an affine invariant constructed from one line and two points while the other is a projective invariant constructed from one line and four points the basic idea of our proposed line matching methods is to use cheaply obtainable matched points to boost line matching via line point invariants even if the matched points are susceptible to severe outlier contamination to deal with the inevitable mismatches in the matched points two line similarity measures are proposed one is based on the maximum and the other is based on the maximal median therefore four different line matching methods are obtained by combining different line point invariants with different similarity measures their performances are evaluated by extensive experiments the results show that our proposed methods outperform the state of the art methods and are robust to mismatches in the matched points used for line matching

building the component tree in quasi linear time the level sets of a map are the sets of points with level above a given threshold the connected components of the level sets thanks to the inclusion relation can be organized in a tree structure that is called the component tree this tree under several variations has been used in numerous applications various algorithms have been proposed in the literature for computing the component tree the fastest ones considering the worst case complexity have been proven to run in o nln n in this paper we propose a simple to implement quasi linear algorithm for computing the component tree on symmetric graphs based on tarjan s union find procedure we also propose an algorithm that computes the n most significant lobes of a map

template free wavelet based detection of local symmetries our goal is to detect and group different kinds of local symmetries in images in a scale and rotation invariant way we propose an efficient wavelet based method to determine the order of local symmetry at each location our algorithm relies on circular harmonic wavelets which are used to generate steerable wavelet channels corresponding to different symmetry orders to give a measure of local symmetry we use the f test to examine the distribution of the energy across different channels we provide experimental results on synthetic images biological micrographs and electron microscopy images to demonstrate the performance of the algorithm

anisotropic scale junction detection and matching for indoor images junctions play an important role in characterizing local geometrical structures of images and the detection of which is a longstanding but challenging task existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales which however contain rich geometries of images this paper presents a novel approach for junction detection and characterization which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a contrario model the output junctions are with anisotropic scales saying that a scale parameter is associated with each branch of a junction and are thus named as anisotropic scale junctions asjs we then apply the new detected asjs for matching indoor images where there are dramatic changes of viewpoints and the detected local visual features e g key points are usually insufficient and lack distinctive ability we propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images the matching results on sets of indoor images demonstrate that our approach achieves the state of the art performance on indoor image matching

evaluation of interest point detectors many different low level feature detectors exist and it is widely agreed that the evaluation of detectors is important in this paper we introduce two evaluation criteria for interest points repeatability rate and information content repeatability rate evaluates the geometric stability under different transformations information content measures the distinctiveness of features different interest point detectors are compared using these two criteria we determine which detector gives the best results and show that it satisfies the criteria well

edge junction and corner detection using color distributions for over 30 years 1970 2000 researchers in computer vision have been proposing new methods for performing low level vision tasks such as detecting edges and corners one key element shared by most methods is that they represent local image neighborhoods as constant in color or intensity with deviations modeled as noise due to computational considerations that encourage the use of small neighborhoods where this assumption holds these methods remain popular the research presented models a neighborhood as a distribution of colors the goal is to show that the increase in accuracy of this representation translates into higher quality results for low level vision tasks on difficult natural images especially as neighborhood size increases we emphasize large neighborhoods because small ones often do not contain enough information we emphasize color because it subsumes gray scale as an image range and because it is the dominant form of human perception we discuss distributions in the context of detecting edges corners and junctions and we show results for each

an a contrario decision method for shape element recognition shape recognition is the field of computer vision which addresses the problem of finding out whether a query shape lies or not in a shape database up to a certain invariance most shape recognition methods simply sort shapes from the database along some dis similarity measure to the query shape their main weakness is the decision stage which should aim at giving a clear cut answer to the question do these two shapes look alike in this article the proposed solution consists in bounding the number of false correspondences of the query shape among the database shapes ensuring that the obtained matches are not likely to occur by chance as an application one can decide with a parameterless method whether any two digital images share some shapes or not