robust model predictive control of constrained linear systems with bounded disturbances this paper provides a novel solution to the problem of robust model predictive control of constrained linear discrete time systems in the presence of bounded disturbances the optimal control problem that is solved online includes uniquely the initial state of the model employed in the problem as a decision variable the associated value function is zero in a disturbance invariant set that serves as the origin when bounded disturbances are present and permits a strong stability result namely robust exponential stability of the disturbance invariant set for the controlled system with bounded disturbances to be obtained the resultant online algorithm is a quadratic program of similar complexity to that required in conventional model predictive control

motion of a sphere through a fluid in stationary homogeneous flow abstract we have extended our previous analysis about the motion of a particle through a fluid in elongational flow to the case in which the fluid is in arbitrary stationary homogeneous flow expressions for the mobility in some cases of interest namely rotational elongational and simple shear flow have been derived the nonequilibrium contributions to such a quantity depend on the corresponding inverse penetration lengths

interval estimation for lpv systems applying high order sliding mode techniques in this paper the problem of design of interval observers for linear parameter varying lpv systems containing non detectable or non strongly observable parts is addressed firstly a high order sliding mode hosm method is applied to the strongly observable subsystem obtained by an appropriate change of coordinates to estimate the state and its derivative secondly this information is used to decrease the level of uncertainty in the rest of the system which leads to improvement of accuracy of the set membership estimates generated by an interval observer moreover it is shown that hosm techniques allows us to relax the applicability conditions of standard interval observer design methods the efficiency of the proposed approach is demonstrated through simulation examples

probabilistic set invariance and ultimate boundedness the notions of invariant sets and ultimate bounds are important concepts in the analysis of dynamical systems and very useful tools for the design of control systems several approaches have been reported for the characterisation of these sets including constructive methods for their computation and procedures to obtain different approximations however there are shortcomings in those concepts in the sense that no general probability distributions can be considered for the disturbances affecting the system which for example precludes the assumption of gaussian distributions insofar as they are not bounded motivated by those shortcomings we propose in this paper the novel concepts of probabilistic ultimate bounds and probabilistic invariant sets which extend the notions of invariant sets and ultimate bounds to consider containment in probability and have the important feature of allowing stochastic noises with more general distributions including the ubiquitous gaussian distribution to be considered we introduce some key definitions for these sets establish their main properties and develop methods for their computation a numerical example illustrates the main ideas

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

brownian motion in a rotating flow the langevin equations for a particle of an arbitrary shape and the correlation functions for the fluctuating forces torques or force torque acting on the particle in a rotating flow are derived from the semimicroscopic level of coarse graining by using fluctuating hydrodynamics in order to obtain the solution of the navier stokes langevin equation valid over the entire flow region use is made of the method of matched asymptotic expansions in f a2 v 1 2 1 the cases of slow and rapid rotation are analyzed it is shown that the fluctuation dissipation theorems hold up to the order of f a2 v 1 2 in both slow and rapid rotation and that the diffusivity tensor depends on the angular velocity of the fluid and becomes anisotropic

variational learning of a dirichlet process of generalized dirichlet distributions for simultaneous clustering and feature selection this paper introduces a novel enhancement for unsupervised feature selection based on generalized dirichlet gd mixture models our proposal is based on the extension of the finite mixture model previously developed in 1 to the infinite case via the consideration of dirichlet process mixtures which can be viewed actually as a purely nonparametric model since the number of mixture components can increase as data are introduced the infinite assumption is used to avoid problems related to model selection i e determination of the number of clusters and allows simultaneous separation of data in to similar clusters and selection of relevant features our resulting model is learned within a principled variational bayesian framework that we have developed the experimental results reported for both synthetic data and real world challenging applications involving image categorization automatic semantic annotation and retrieval show the ability of our approach to provide accurate models by distinguishing between relevant and irrelevant features without over or under fitting the data

a representation for shape based on peaks and ridges in the difference of low pass transform this paper defines a multiple resolution representation for the two dimensional gray scale shapes in an image this representation is constructed by detecting peaks and ridges in the difference of lowpass dolp transform descriptions of shapes which are encoded in this representation may be matched efficiently despite changes in size orientation or position motivations for a multiple resolution representation are presented first followed by the definition of the dolp transform techniques are then presented for encoding a symbolic structural description of forms from the dolp transform this process involves detecting local peaks and ridges in each bandpass image and in the entire three dimensional space defined by the dolp transform linking adjacent peaks in different bandpass images gives a multiple resolution tree which describes shape peaks which are local maxima in this tree provide landmarks for aligning manipulating and matching shapes detecting and linking the ridges in each dolp bandpass image provides a graph which links peaks within a shape in a bandpass image and describes the positions of the boundaries of the shape at multiple resolutions detecting and linking the ridges in the dolp three space describes elongated forms and links the largest peaks in the tree the principles for determining the correspondence between symbols in pairs of such descriptions are then described such correspondence matching is shown to be simplified by using the correspondence at lower resolutions to constrain the possible correspondence at higher resolutions

scalable large margin mahalanobis distance metric learning for many machine learning algorithms such as k nearest neighbor k nn classifiers and k means clustering often their success heavily depends on the metric used to calculate distances between different data points an effective solution for defining such a metric is to learn it from a set of labeled training samples in this work we propose a fast and scalable algorithm to learn a mahalanobis distance metric the mahalanobis metric can be viewed as the euclidean distance metric on the input data that have been linearly transformed by employing the principle of margin maximization to achieve better generalization performances this algorithm formulates the metric learning as a convex optimization problem and a positive semidefinite p s d matrix is the unknown variable based on an important theorem that a p s d trace one matrix can always be represented as a convex combination of multiple rank one matrices our algorithm accommodates any differentiable loss function and solves the resulting optimization problem using a specialized gradient descent procedure during the course of optimization the proposed algorithm maintains the positive semidefiniteness of the matrix variable that is essential for a mahalanobis metric compared with conventional methods like standard interior point algorithms or the special solver used in large margin nearest neighbor our algorithm is much more efficient and has a better performance in scalability experiments on benchmark data sets suggest that compared with state of the art metric learning algorithms our algorithm can achieve a comparable classification accuracy with reduced computational complexity

zonotopic guaranteed state estimation for uncertain systems this paper presents a new approach for guaranteed state estimation based on zonotopes for linear discrete time multivariable systems with interval multiplicative uncertainties in the presence of bounded state perturbations and noises at each sample time the presented approach computes a zonotope which contains the real system state a p radius based criterion is minimized in order to decrease the size of the zonotope at each sample time and to obtain an increasingly accurate state estimation the proposed approach allows one to efficiently handle the trade off between the complexity of the computation and the accuracy of the estimation an illustrative example is analyzed in order to highlight the advantages of the proposed state estimation technique

study of a robust feature the pointwise lipschitz regularity the aim of this paper is to highlight the relevance in computer vision of the pointwise lipschitz regularity the regularity gives a measure of the local regularity of the intensity function associated to an image known wavelet methods provide an efficient computation of at contour points of the image from a theoretical point of view we study the effect of geometric deformations and other specific transformations applied to the image showing invariance properties from a practical point of view we assess the robustness of the regularity when the image undergoes various transformations the results we obtain show the lipschitz regularity is a suitable feature for applications in computer vision

a new feature extractor invariant to intensity rotation and scaling of color images this paper proposes a new method for extracting the invariant features of an image based on the concept of principal component analysis and a competitive learning algorithm the proposed algorithm can be applied to binary gray level or colored texture images with a size greater than 256x256 pixels in addition to translation scaling and rotation invariant extraction the extraction of a feature invariant to color intensity can be implemented by using this method in our experiment the proposed method shows the capability to differentiate images having the same shape but different colored textures the experimental results report the effectiveness of this technique and its performance as measured by recognition accuracy rate and computational time these results are also compared with those obtained by classical techniques

rasim a novel rotation and scale invariant matching of local image interest points this paper presents a novel algorithm for matching image interest points potential interest points are identified by searching for local peaks in difference of gaussian dog images we refine and assign rotation scale and location for each keypoint by using the sift algorithm pseudo log polar sampling grid is then applied to properly scaled image patches around each keypoint and a weighted adaptive lifting scheme transform is designed for each ring of the log polar grid the designed adaptive transform for a ring in the reference keypoint and the general non adaptive transform are applied to the corresponding ring in a test keypoint similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non adaptive transforms we refer to the proposed versatile system of rotation and scale invariant matching as rasim our experiments show that the accuracy of rasim is more than sift which is the most widely used interest point matching algorithm in the literature rasim is also more robust to image deformations while its computation time is comparable to sift

ray projection for recovering projective transformations and illumination changes the ray projection and its application to recovering a projective geometric transformation and an affine lighting change between two objects are mathematically studied a novel technique viz variable contour is proposed for accurately evaluating the ray projection moreover a novel flexible framework of the ray projection is devised for the joint recovery of the eight parameters for the projective transformation and the two parameters for the lighting change finally the framework is experimentally evaluated by recovering a variety of geometric and photometric transformations between real images of indoor and outdoor scenes its robustness to image blur and occlusion is demonstrated its versatility is illustrated through matching different objects in different classes

feature based sequence to sequence matching this paper studies the problem of matching two unsynchronized video sequences of the same dynamic scene recorded by different stationary uncalibrated video cameras the matching is done both in time and in space where the spatial matching can be modeled by a homography for 2d scenarios or by a fundamental matrix for 3d scenarios our approach is based on matching space time trajectories of moving objects in contrast to matching interest points e g corners as done in regular feature based image to image matching techniques the sequences are matched in space and time by enforcing consistent matching of all points along corresponding space time trajectories by exploiting the dynamic properties of these space time trajectories we obtain sub frame temporal correspondence synchronization between the two video sequences furthermore using trajectories rather than feature points significantly reduces the combinatorial complexity of the spatial point matching problem when the search space is large this benefit allows for matching information across sensors in situations which are extremely difficult when only image to image matching is used including a matching under large scale zoom differences b very wide base line matching and c matching across different sensing modalities e g ir and visible light cameras we show examples of recovering homographies and fundamental matrices under such conditions

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

matching images with multiple descriptors an unsupervised approach for locally adaptive descriptor selection with the aim to improve the performance of feature matching we present an unsupervised approach for adaptive description selection in the space of homographies inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain our approach stands on the unsupervised nature of feature matching and can choose a good descriptor locally for matching each feature point instead of using one global descriptor to this end the homography space serves as the domain for selecting various heterogeneous descriptors correspondences obtained by any descriptors are considered as points in the space and their geometric coherence and spatial continuity are measured via computing the geodesic distances in this way mutual verification across different descriptors is allowed and correct correspondences will be highlighted with a high degree of consistency short geodesic distances here it follows that one class svm can be applied to identifying these correct correspondences and achieves adaptive descriptor selection the proposed approach is comprehensively compared with the state of the art approaches and evaluated on five benchmarks of image matching the promising results manifest its effectiveness

global propagation of affine invariant features for robust matching local invariant features have been successfully used in image matching to cope with viewpoint change partial occlusion and clutters however when these factors become too strong there will be a lot of mismatches due to the limited repeatability and discriminative power of features in this paper we present an efficient approach to remove the false matches and propagate the correct ones for the affine invariant features which represent the state of the art local invariance first a pair wise affine consistency measure is proposed to evaluate the consensus of the matches of affine invariant regions the measure takes into account both the keypoint location and the region shape size and orientation based on this measure a geometric filter is then presented which can efficiently remove the outliers from the initial matches and is robust to severe clutters and non rigid deformation to increase the correct matches we propose a global match refinement and propagation method that simultaneously finds a optimal group of local affine transforms to relate the features in two images the global method is capable of producing a quasi dense set of matches even for the weakly textured surfaces that suffer strong rigid transformation or non rigid deformation the strong capability of the proposed method in dealing with significant viewpoint change non rigid deformation and low texture objects is demonstrated in experiments of image matching object recognition and image based rendering

geometric information criterion for model selection in building a 3 d model of the environment from image and sensor data one must fit to the data an appropriate class of models which can be regarded as a parametrized manifold or geometric model defined in the data space in this paper we present a statistical framework for detecting degeneracies of a geometric model by evaluating its predictive capability in terms of the expected residual and derive the geometric aic we show that it allows us to detect singularities in a structure from motion analysis without introducing any empirically adjustable thresholds we illustrate our approach by simulation examples we also discuss the application potential of this theory for a wide range of computer vision and robotics problems

feature vector field and feature matching in this paper we propose a feature vector field for images which is built by the inner products and exterior products of image gradients the feature vector field effectively represents image edges and feature points including corners and edge points with big curvature using the feature vector field some novel descriptors are constructed for point matching and curve matching the descriptors are all invariant to image euclidean transformations and linear intensity changes the experiments show that the descriptors also have a good adaptability to small image affine transformation jpeg compression and nonlinear changes of intensity

the design and use of steerable filters the authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters allowing one to adaptively steer a filter to any orientation and to determine analytically the filter output as a function of orientation steerable filters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation the authors show how to design and steer the filters and present examples of their use in the analysis of orientation and phase angularly adaptive filtering edge detection and shape from shading one can also build a self similar steerable pyramid representation the same concepts can be generalized to the design of 3 d steerable filters

robust interval observers and stabilization design for discrete time systems with input and output for a family of nonlinear discrete time systems with input output and uncertain terms a new interval observer is designed its main feature is that it is composed of two copies of classical observers this interval observer applies in the presence of unknown bounded nonlinear terms and additive disturbances and is used to achieve asymptotic stability through an appropriate choice of dynamic output feedback an illustrative example completes the presentation

a performance evaluation of local descriptors in this paper we compare the performance of descriptors computed for local interest regions as for example extracted by the harris affine detector mikolajczyk k and schmid c 2004 many different descriptors have been proposed in the literature it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector the descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations we compare shape context belongie s et al april 2002 steerable filters freeman w and adelson e setp 1991 pca sift ke y and sukthankar r 2004 differential invariants koenderink j and van doorn a 1987 spin images lazebnik s et al 2003 sift lowe d g 1999 complex filters schaffalitzky f and zisserman a 2002 moment invariants van gool l et al 1996 and cross correlation for different types of interest regions we also propose an extension of the sift descriptor and show that it outperforms the original method furthermore we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the sift based descriptors perform best moments and steerable filters show the best performance among the low dimensional descriptors

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

set membership filtering with incomplete observations abstract this study addresses the set membership estimation problem for a class of discrete time varying systems with incomplete observations a set membership filter is developed and a recursive algorithm is proposed to calculate the state estimate ellipsoid which contains the true value to solve the problem that the conventional set membership filter cannot guarantee the stability when applied to discrete time varying systems with incomplete observations a quantitative analysis method about incomplete observations is developed and a tight bound of the estimation error is found based on interval analysis and some bounded noise assumptions in terms of bounded assumptions the relationship between the bound of estimated error and the data dropout rate is obtained if the data dropout rate is less than a maximal value the set membership filter is asymptotically stable the proposed filter is applied to a two state example to demonstrate the effectiveness of theoretical results

common visual pattern discovery via nonlinear mean shift clustering discovering common visual patterns cvps from two images is a challenging task due to the geometric and photometric deformations as well as noises and clutters the problem is generally boiled down to recovering correspondences of local invariant features and the conventionally addressed by graph based quadratic optimization approaches which often suffer from high computational cost in this paper we propose an efficient approach by viewing the problem from a novel perspective in particular we consider each cvp as a common object in two images with a group of coherently deformed local regions a geometric space with matrix lie group structure is constructed by stacking up transformations estimated from initially appearance matched local interest region pairs this is followed by a mean shift clustering stage to group together those close transformations in the space joining regions associated with transformations of the same group together within each input image forms two large regions sharing similar geometric configuration which naturally leads to a cvp to account for the non euclidean nature of the matrix lie group mean shift vectors are derived in the corresponding lie algebra vector space with a newly provided effective distance measure extensive experiments on single and multiple common object discovery tasks as well as near duplicate image retrieval verify the robustness and efficiency of the proposed approach

spectral symmetry analysis we present a spectral approach for detecting and analyzing rotational and reflectional symmetries in n dimensions our main contribution is the derivation of a symmetry detection and analysis scheme for sets of points irn and its extension to image analysis by way of local features each object is represented by a set of points s irn where the symmetry is manifested by the multiple self alignments of s the alignment problem is formulated as a quadratic binary optimization problem with an efficient solution via spectral relaxation for symmetric objects this results in a multiplicity of eigenvalues whose corresponding eigenvectors allow the detection and analysis of both types of symmetry we improve the scheme s robustness by incorporating geometrical constraints into the spectral analysis our approach is experimentally verified by applying it to 2d and 3d synthetic objects as well as real images

performance comparisons of contour based corner detectors corner detectors have many applications in computer vision and image identification and retrieval contour based corner detectors directly or indirectly estimate a significance measure e g curvature on the points of a planar curve and select the curvature extrema points as corners while an extensive number of contour based corner detectors have been proposed over the last four decades there is no comparative study of recently proposed detectors this paper is an attempt to fill this gap the general framework of contour based corner detection is presented and two major issues curve smoothing and curvature estimation which have major impacts on the corner detection performance are discussed a number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets it is observed that while the detectors using indirect curvature estimation techniques are more robust the detectors using direct curvature estimation techniques are faster

interval observer design for lpv systems with parametric uncertainty observer design for dynamical systems with both known and unknown time varying parameters is of significant interest in a number of real world applications this class of systems has been rarely addressed in the existing literature this paper develops an interval observer design methodology for linear parameter varying lpv systems with parametric uncertainty with information on upper and lower bounds of the uncertain parameters an interval observer that produces an envelope covering all possible state trajectories is presented the application of the proposed algorithm in an important vehicle state estimation problem which aims at minimizing the worst case envelope of the side slip angle estimate in the presence of uncertain tire cornering stiffness parameters and varying vehicle speed is presented the obtained observer is evaluated in simulation using carsim a commercial industry standard vehicle simulation software the results verify the value of the developed observer design method

evolutionary compact embedding for large scale image classification effective dimensionality reduction is a classical research area for many large scale analysis tasks in computer vision several recent methods attempt to learn either graph embedding or binary hashing for fast and accurate applications in this paper we propose a novel framework to automatically learn the task specific compact coding called evolutionary compact embedding ece which can be regarded as an optimization algorithm combining genetic programming gp and a boosting trick as an evolutionary computation methodology gp can solve problems inspired by natural evolution without any prior knowledge of the solutions in our evolutionary architecture each bit of ece is iteratively computed using a binary classification function which is generated through gp evolving by jointly minimizing its empirical risk with the adaboost strategy on a training set we address this as greedy optimization leading to small hamming distances for similar samples and large distances for dissimilar samples we then evaluate ece on four image datasets usps digital hand writing cmu pie face cifar 10 tiny image and sun397 scene showing the accurate and robust performance of our method for large scale image classification

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

dependence of the friction tensor on the rotation of a frame of reference abstract is is argued that the expression for the friction tensor of a sphere immerssed in a fluid depends on the choice of the frame of reference and in particular its rotation the relation between the friction tensor in the laboratory frame and the corotating frame which have been derived by different authors respectively is explicitly given memory effects are found to play a crucial role in the transformation from one frame of reference to another

affine transforms between image space and color space for invariant local descriptors accurate local region description is a keypoint in many applications and has been the topic of lots of recent papers starting from the very accurate sift most of the approaches exploit the local gradient information that suffers from several drawbacks first it is unstable in case of severe geometry distortions second it cannot be easily summarized in a compact way and third it is not designed to account vectorial color information in this paper we propose an alternative by designing compact descriptors that account both the colors present in the region and their spatial distribution each pixel being characterized by five coordinates two in the image space and three in the color space we try to evaluate affine transforms that allow to go from the spatial coordinates to the color coordinates and inversely obviously such kind of transform does not exist but we show that after applying it to the original coordinates the resulted positions are both discriminative and invariant to many acquisition conditions hence depending on the original space image or color and the destination space color or image we design different complementary descriptors their discriminative power and invariance properties are assessed and compared with the best color descriptors in the context of region matching and object classification

retrieval of images of man made structures based on projective invariance in this paper we propose a geometry based image retrieval scheme that makes use of projectively invariant features cross ratio cr is an invariant feature under projective transformations for collinear points we compute the crs of point sets in quadruplets and the cr histogram is used as the feature for retrieval purposes being a geometric feature it allows us to retrieve similar images irrespective of view point and illumination changes we can retrieve the same building even if the facade has undergone a fresh coat of paints color and textural features can also be included if desired experimental results show a favorably very good retrieval accuracy when tested on an image database of size 4000 the method is very effective in retrieving images having man made objects rich in polygonal structures like buildings rail tracks etc

geometric latent dirichlet allocation on a matching graph for large scale image datasets given a large scale collection of images our aim is to efficiently associate images which contain the same entity for example a building or object and to discover the significant entities to achieve this we introduce the geometric latent dirichlet allocation glda model for unsupervised discovery of particular objects in unordered image collections this explicitly represents images as mixtures of particular objects or facades and builds rich latent topic models which incorporate the identity and locations of visual words specific to the topic in a geometrically consistent way applying standard inference techniques to this model enables images likely to contain the same object to be probabilistically grouped and ranked additionally to reduce the computational cost of applying the glda model to large datasets we propose a scalable method that first computes a matching graph over all the images in a dataset this matching graph connects images that contain the same object and rough image groups can be mined from this graph using standard clustering techniques the glda model can then be applied to generate a more nuanced representation of the data we also discuss how hub images images representative of an object or landmark can easily be extracted from our matching graph representation we evaluate our techniques on the publicly available oxford buildings dataset 5k images and show examples of automatically mined objects the methods are evaluated quantitatively on this dataset using a ground truth labeling for a number of oxford landmarks to demonstrate the scalability of the matching graph method we show qualitative results on two larger datasets of images taken of the statue of liberty 37k images and rome 1m images

robust imagegraph rank level feature fusion for image search recently feature fusion has demonstrated its effectiveness in image search however bad features and inappropriate parameters usually bring about false positive images i e outliers leading to inferior performance therefore a major challenge of fusion scheme is how to be robust to outliers towards this goal this paper proposes a rank level framework for robust feature fusion first we define rank distance to measure the relevance of images at rank level based on it bayes similarity is introduced to evaluate the retrieval quality of individual features through which true matches tend to obtain higher weight than outliers then we construct the directed imagegraph to encode the relationship of images each image is connected to its k nearest neighbors with an edge and the edge is weighted by bayes similarity multiple rank lists resulted from different methods are merged via imagegraph furthermore on the fused imagegraph local ranking is performed to re order the initial rank lists it aims at local optimization and thus is more robust to global outliers extensive experiments on four benchmark data sets validate the effectiveness of our method besides the proposed method outperforms two popular fusion schemes and the results are competitive to the state of the art

wavelet domain multifractal analysis for static and dynamic texture classification in this paper we propose a new texture descriptor for both static and dynamic textures the new descriptor is built on the wavelet based spatial frequency analysis of two complementary wavelet pyramids standard multiscale and wavelet leader these wavelet pyramids essentially capture the local texture responses in multiple high pass channels in a multiscale and multiorientation fashion in which there exists a strong power law relationship for natural images such a power law relationship is characterized by the so called multifractal analysis in addition two more techniques scale normalization and multiorientation image averaging are introduced to further improve the robustness of the proposed descriptor combining these techniques the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes we apply the descriptor for classifying both static and dynamic textures our method has demonstrated excellent performance in comparison with the state of the art approaches in several public benchmark datasets

a visual shape descriptor using sectors and shape context of contour lines this paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching the proposed descriptor consists of two component feature vectors first the local region is separated into sectors and their gradient magnitude and orientation values are extracted a feature vector is then constructed from these values second local shape features are obtained using the shape context of contour lines another feature vector is then constructed from these contour lines the proposed approach calculates the local shape feature without needing to consider the edges this can overcome the difficulty associated with textured images and images with ill defined edges the combination of two component feature vectors makes the proposed descriptor more robust to image scale changes illumination variations and noise the proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy 14 525 better than sift 21 better than pca sift 11 86 better than gloh and 25 66 better than the shape context

positive semidefinite metric learning using boosting like algorithms the success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data it is often beneficial to learn such a metric from the input training data instead of using a default one such as the euclidean distance in this work we propose a boosting based technique termed boostmetric for learning a quadratic mahalanobis distance metric learning a valid mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive semidefinite semidefinite programming is often used to enforce this constraint but does not scale well and is not easy to implement boostmetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace one rank one matrices boostmetric thus uses rank one positive semidefinite matrices as weak learners within an efficient and scalable boosting based learning process the resulting methods are easy to implement efficient and can accommodate various types of constraints we extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor experiments on various data sets demonstrate that the proposed algorithms compare favorably to those state of the art methods in terms of classification accuracy and running time

affine covariant features for fisheye distortion local modeling perspective cameras are the most popular imaging sensors used in computer vision however many application fields including automotive surveillance and robotics require the use of wide angle cameras e g fisheye which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images affine covariant feature detectors have proved successful in a variety of computer vision applications including object recognition image registration and visual search moreover their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature in this paper we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses as theoretical outcome we show that the inherently non linear radial distortion can be locally approximated by linear functions with a reasonably small error the experimental analysis builds on mikolajczyk s benchmark to assess the robustness of three popular affine region detectors i e maximally stable extremal regions and harris and hessian affine region detectors with respect to different variabilities as well as to radial distortion to support the evaluations we rely on the oxford data set and introduce a novel benchmark data set comprising 50 images depicting different scene categories experiments are carried out on rectilinear images to which radial distortion is artificially added and on real world images acquired using fisheye lenses our analysis points out that affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modeled as an additional affine variability

geometric and photometric invariant distinctive regions detection in this paper we present a number of enhancements to the kadir brady salient region detector which result in a significant improvement in performance the modifications we make include stabilising the difference between consecutive scales when calculating the inter scale saliency a new sampling strategy using overlap of pixels partial volume estimation and parzen windowing repeatability is used as the criterion for evaluating the performance of the algorithm we observe the repeatability for distinctive regions selected from an image and from the same image after applying a particular transformation the transformations we use include planar rotation pixel translation spatial scaling and intensity shifts and scaling experimental results show that the average repeatability rate is improved from 46 to approximately 78 when all the enhancements are applied we also compare our algorithm with other region detectors on a set of sequences of real images and our detector outperforms most of the state of the art detectors

action recognition by spatio temporal oriented energies in this paper we present a unified representation based on the spatio temporal steerable pyramid stsp for the holistic representation of human actions a video sequence is viewed as a spatio temporal volume preserving all the appearance and motion information of an action in it by decomposing the spatio temporal volumes into band passed sub volumes the spatio temporal laplacian pyramid provides an effective technique for multi scale analysis of video sequences and spatio temporal patterns with different scales could be well localized and captured to efficiently explore the underlying local spatio temporal orientation structures at multiple scales a bank of three dimensional separable steerable filters are conducted on each of the sub volume from the laplacian pyramid the outputs of the quadrature pair of steerable filters are squared and summed to yield a more robust oriented energy representation to be further invariant and compact a spatio temporal max pooling operation is performed between responses of the filtering at adjacent scales and over spatio temporal neighbourhoods in order to capture the appearance local geometric structure and motion of an action we apply the stsp on the intensity 3d gradients and optical flow of video sequences yielding a unified holistic representation of human actions taking advantage of multi scale multi orientation analysis and feature pooling stsp produces a compact but informative and invariant representation of human actions we conduct extensive experiments on the kth ucf sports and hmdb51 datasets which shows the unified stsp achieves comparable results with the state of the art methods

zonotopes and kalman observers state bounding observation based on zonotopes is the subject of this paper dealing with zonotopes is motivated by set operations resulting in simple matrix calculations with regard to the often huge number of facets and vertices of the equivalent polytopes discrete time ltv lpv systems with state and measurement uncertainties are considered based on a new zonotope size criterion called f w radius and by merging optimal and robust observer gain designs a zonotopic kalman filter zkf is proposed with a proof of robust convergence the notion of covariation is introduced and results in an explicit bridge between the zonotopic set membership and the stochastic paradigms for kalman filtering no intersection is used and the influence of the reduction operator limiting to a tunable maximum the size of the matrices involved in the zonotopic set computations is fully taken into account in the lmi based robust stability analysis a numerical example illustrates the effectiveness of the proposed zkf

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

corner detection based on gradient correlation matrices of planar curves an efficient and novel technique is developed for detecting and localizing corners of planar curves this paper discusses the gradient feature distribution of planar curves and constructs gradient correlation matrices gcms over the region of support ros of these planar curves it is shown that the eigen structure and determinant of the gcms encode the geometric features of these curves such as curvature features and the dominant points the determinant of the gcms is shown to have a strong corner response and is used as a cornerness measure of planar curves a comprehensive performance evaluation of the proposed detector is performed using the acu and localization error criteria experimental results demonstrate that the gcm detector has a strong corner position response along with a high detection rate and good localization performance

compact representation of high dimensional feature vectors for large scale image recognition and retrieval in large scale visual recognition and image retrieval tasks feature vectors such as fisher vector fv or the vector of locally aggregated descriptors vlad have achieved state of the art results however the combination of the large numbers of examples and high dimensional vectors necessitates dimensionality reduction in order to reduce its storage and cpu costs to a reasonable range in spite of the popularity of various feature compression methods this paper shows that the feature dimension selection is a better choice for high dimensional fv vlad than the feature dimension compression methods e g product quantization we show that strong correlation among the feature dimensions in the fv and the vlad may not exist which renders feature selection a natural choice we also show that many dimensions in fv vlad are noise throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods to choose features we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases for visual recognition and image retrieval respectively combining with the 1 bit quantization feature selection has achieved both higher accuracy and less computational cost than feature compression methods such as product quantization on the fv and the vlad image representations

interval state estimation for a class of nonlinear systems the goal of this technical note is to design interval observers for a class of nonlinear continuous time systems the first part of this work shows that it is usually possible to design an interval observer for linear systems by means of linear time invariant changes of coordinates even if the system is not cooperative this result is extended to a class of nonlinear systems using partial exact linearisations the proposed observers guarantee to enclose the set of system states that is consistent with the model the disturbances and the measurement noise moreover it is only assumed that the measurement noise and the disturbances are bounded without any additional information such as stationarity uncorrelation or type of distribution the proposed observer is illustrated through numerical simulations

local grayvalue invariants for image retrieval this paper addresses the problem of retrieving images from large image databases the method is based on local grayvalue invariants which are computed at automatically detected interest points a voting algorithm and semilocal constraints make retrieval possible indexing allows for efficient retrieval from a database of more than 1 000 images experimental results show correct retrieval in the case of partial visibility similarity transformations extraneous features and small perspective deformations

fast representation based on a double orientation histogram for local image descriptors in recent years extensive research on local invariant features has been conducted and many novel descriptors have been developed for different scenarios frequently these descriptors can offer unique advantages such as stability precision and speed for select applications but perform unsatisfactorily from a comprehensive perspective consequently a novel local image descriptor named fast representation using a double orientation histogram frdoh is developed in this paper based on existing descriptors first a region is divided using intensity order as in the local intensity order pattern descriptor to encode spatial information then the discriminability of the descriptor is enhanced using our proposed double orientation histogram second to further improve the discriminability of the descriptor the hellinger distance is used to balance the effects of large and small bins in the histogram for the similarity measure of features finally a novel interpolation strategy known as rapidly cascaded interpolation is used to calculate the intensity of the neighboring points to reduce the computation time while achieving high precision the performance of the developed descriptor is evaluated via numerous experiments on the affine covariant feature data set of the oxford data set a subset of a 3d object data set and a subset of the iit delhi touchless palmprint data set these experiments demonstrate that the developed frdoh descriptor outperforms the state of the art descriptors in terms of comprehensive performance

improving bag of features for large scale image search this article improves recent methods for large scale image search we first analyze the bag of features approach in the framework of approximate nearest neighbor search this leads us to derive a more precise representation based on hamming embedding he and weak geometric consistency constraints wgc he provides binary signatures that refine the matching based on visual words wgc filters matching descriptors that are not consistent in terms of angle and scale he and wgc are integrated within an inverted file and are efficiently exploited for all images in the dataset we then introduce a graph structured quantizer which significantly speeds up the assignment of the descriptors to visual words a comparison with the state of the art shows the interest of our approach when high accuracy is needed experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints as well as their efficiency estimation of the full geometric transformation i e a re ranking step on a short list of images is shown to be complementary to our weak geometric consistency constraints our approach is shown to outperform the state of the art on the three datasets

probability guaranteed set membership filtering for systems with incomplete measurements in this note the probability guaranteed set membership filtering problem is investigated for a class of time varying systems with incomplete measurements the uniform distribution over known finite ranges is assumed for mutually independent stochastic variables existing in the functions of system matrices the process noise and measurement noise are unknown bounded and confined to a specified ellipsoidal set respectively which is handled by transformation to the inequality constraints the purpose is to design a set membership filter for all the admissible unknown but bounded noises known deterministic input and incomplete measurements such that the ellipsoidal set including all possible states can be determined by a convex optimization approach with probability constraint a recursive linear matrix inequality rlmi approach is proposed to address the probability guaranteed set membership filtering problem finally a simulation example demonstrates the effectiveness of the proposed filtering scheme

interval state observer for nonlinear time varying systems this paper is devoted to the design of interval observers for linear time varying ltv systems and a class of nonlinear time varying systems in the output canonical form an interval observer design is feasible if it is possible to calculate the observer gains making the estimation error dynamics cooperative and stable it is shown that under some mild conditions the cooperativity of an ltv system can be ensured by a static linear transformation of coordinates the efficiency of the proposed approach is demonstrated through numerical simulations

h infinity set membership observer design for discrete time lpv systems this paper presents a new set membership observer design method for discrete time linear parameter varying systems the real process is assumed to be perturbed by unknown but bounded disturbances

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

survey paper set invariance in control the properties of positively invariant sets are involved in many different problems in control theory such as constrained control robustness analysis synthesis and optimization in this paper we provide an overview of the literature concerning positively invariant sets and their application to the analysis and synthesis of control systems

affinity learning with diffusion on tensor product graph in many applications we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples often the pairwise similarities which are also called affinities are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples as observed in several recent approaches more reliable similarities can be obtained if the original similarities are diffused in the context of other data points where the context of each point is a set of points most similar to it compared to the existing methods our approach differs in two main aspects first instead of diffusing the similarity information on the original graph we propose to utilize the tensor product graph tpg obtained by the tensor product of the original graph with itself since tpg takes into account higher order information it is not a surprise that we obtain more reliable similarities however it comes at the price of higher order computational complexity and storage requirement the key contribution of the proposed approach is that the information propagation on tpg can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph we prove that a graph diffusion process on tpg is equivalent to a novel iterative algorithm on the original graph which is guaranteed to converge after its convergence we obtain new edge weights that can be interpreted as new learned affinities we stress that the affinities are learned in an unsupervised setting we illustrate the benefits of the proposed approach for data manifolds composed of shapes images and image patches on two very different tasks of image retrieval and image segmentation with learned affinities we achieve the bull s eye retrieval score of 99 99 percent on the mpeg 7 shape dataset which is much higher than the state of the art algorithms when the data points are image patches the ncut with the learned affinities not only significantly outperforms the ncut with the original affinities but it also outperforms state of the art image segmentation methods

color constancy for multiple light sources color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes however in reality this assumption is often violated due to the presence of multiple light sources in this paper we will address more realistic scenarios where the uniform light source assumption is too restrictive first a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches rather than globally to the entire image after local patch based illuminant estimation these estimates are combined into more robust estimations and a local correction is applied based on a modified diagonal model quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene if the chromatic difference between these two illuminants is more than 1 the proposed framework outperforms algorithms based on the uniform light source assumption with error reduction up to approximately 30 otherwise when the chromatic difference is less than 1 and the scene can be considered to contain one approximately uniform light source the performance of the proposed method framework is similar to global color constancy methods