interest point detection using rank order log filter this paper proposes a novel nonlinear filter named rank order laplacian of gaussian rolg filter based on which a new interest point detector is developed the rolg filter is a weighted rank order filter it is used to detect the image local structures where a significant majority of pixels are brighter or darker than a significant majority of pixels in their corresponding surroundings compared to linear filter based detectors e g sift detector the proposed rank order filter based detector is more robust to abrupt variations of images caused by illumination and geometric changes experiments on the benchmark databases demonstrate that the proposed rolg detector achieves superior performance comparing to four state of the art detectors evaluation experiments are also conducted on face recognition problems the results on five face databases further demonstrate that the rolg detector significantly outperforms the other detectors

mdghm surf this paper proposes a novel family of local feature descriptors a variant of the speed up robust features surf descriptor which is capable of demonstrably better performance the conventional surf descriptor is an efficient implementation of the sift descriptor although the surf descriptor can represent the nature of the underlying image pattern it is still sensitive to more complicated deformations such as large viewpoint and rotation changes to solve this problem our family of descriptors called mdghm surf is based on the modified discrete gaussian hermite moment mdghm which devises a movable mask to represent the local feature information of non square images whereas conventional surf uses first order derivatives mdghm surf uses mdghm which offers more feature information than first order derivative based local descriptors such as surf and sift consequently by redefining the conventional surf descriptor using mdghm mdghm surf can extract more distinctive features than conventional surf the results of evaluations conducted with six types of deformations indicate that our proposed method outperforms the matching accuracy of other surf related algorithms we propose a novel family of the surf descriptor to solve the sensitivity of the surf we redefine the surf using mdghm mdghm surf the mdghm offers more feature information than the first order derivative based surf the evaluations are conducted with six types of deformations and panoramic image generation the results indicate that our method outperforms other surf related algorithms

structure propagation for image registration mosaicing is a commonly used technique in many medical imaging applications where subimages are stitched together in order to obtain a larger field of view however stitching which involves alignment or registration in overlapping regions is often challenging when the information shared by subimages is absent or small while it is not possible to perform an alignment without overlap using existing techniques imaging artifacts such as distortions towards image boundaries present further complications during registration by decreasing the reliability of available information without taking these into consideration a registration approach might violate the continuity and the smoothness of structures across subimages in this paper we propose a novel registration approach for the stitching of subimages in such challenging scenarios by using a perceptual grouping approach we extend subimages beyond their boundaries by propagating available structures in order to obtain structural maps in the extended regions these maps are then used to establish correspondences between subimages when the shared information is absent small or unreliable using our approach ensures the continuity and the smoothness of structures across subimage boundaries furthermore since only structures are used the proposed method can also be used for the stitching of multi modal images our approach is unique in that it also enables contactless stitching we demonstrate the effectiveness of the proposed method by performing several experiments on synthetic and medical images moreover we show how stitching is possible in the presence of a physical gap between subimages

toward dynamic image mosaic generation with robustness to parallax mosaicing is largely dependent on the quality of registration among the constituent input images parallax and object motion present challenges to image registration leading to artifacts in the result to reduce the impact of these artifacts traditional image mosaicing approaches often impose planar scene constraints or rely on purely rotational camera motion or dense sampling however these requirements are often impractical or fail to address the needs of all applications instead taking advantage of depth cues and a smooth transition criterion we achieve significantly improved mosaicing results for static scenes coping effectively with nontrivial parallax in the input we extend this approach to the synthesis of dynamic video mosaics incorporating foreground background segmentation and a consistent motion perception criterion although further additions are required to cope with unconstrained object motion our algorithm can synthesize a perceptually convincing output conveying the same appearance of motion as seen in the input sequences

code coherence based decision boundaries for feature correspondence a key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level this forces adoption of strict similarity thresholds that discard many true matches however if analyzed at a global level false matches are usually randomly scattered while true matches tend to be coherent clustered around a few dominant motions thus creating a coherence based separability constraint this paper proposes a non linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model once computed the model can filter the entire set of nearest neighbor matches which typically contains over 90 percent false matches for true matches we integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches

establishing keypoint matches on multimodal images with bootstrap strategy and global information this paper proposes an algorithm of building keypoint matches on multimodal images by combining a bootstrap process and global information the correct ratio of keypoint matches built with descriptors is typically very low on multimodal images of large spectral difference to identify correct matches global information is utilized for evaluating keypoint matches and a bootstrap technique is employed to reduce the computational cost a keypoint match determines a transformation t and a similarity metric between the reference and the transformed test image by t the similarity metric encodes global information over entire images and hence a higher similarity indicates the match can bring more image content into alignment implying it tends to be correct unfortunately exhausting triplets quadruples of matches for affine projective transformation is computationally intractable when the number of keypoints is large to reduce the computational cost a bootstrap technique is employed that starts from single matches for a translation and rotation model and goes increasingly to quadruples of four matches for a projective model the global information screens for good matches at each stage and the bootstrap strategy makes the screening process computationally feasible experimental results show that the proposed method can establish reliable keypoint matches on challenging multimodal images of strong multimodality

a probabilistic approach for color correction in image mosaicking applications image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic this paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities first the image to be color corrected is segmented into several regions using mean shift then connected regions are extracted using a region fusion algorithm local joint image histograms of each region are modeled as collections of truncated gaussians using a maximum likelihood estimation procedure then local color palette mapping functions are computed using these sets of gaussians the color correction is performed by applying those functions to all the regions of the image an extensive comparison with ten other state of the art color correction algorithms is presented using two different image pair data sets results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures

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

ray saliency bottom up visual saliency for a rotating and zooming camera we extend the classical notion of computational visual saliency to multi image data collected using a stationary pan tilt zoom ptz camera by introducing the concept of consistency the requirement that the set of generated saliency maps should each assign the same saliency value to unique regions of the environment that appear in more than one image we show that processing each image independently will often fail to provide a consistent measure of saliency and that using an image mosaic to quantify saliency suffers from several drawbacks we then propose ray saliency and an immediate extension approximate ray saliency a mosaic free method for calculating a consistent measure of bottom up saliency experimental results demonstrating the effectiveness of the proposed approach are presented

robust high dynamic range imaging by rank minimization this paper introduces a new high dynamic range hdr imaging algorithm which utilizes rank minimization assuming a camera responses linearly to scene radiance the input low dynamic range ldr images captured with different exposure time exhibit a linear dependency and form a rank 1 matrix when stacking intensity of each corresponding pixel together in practice misalignments caused by camera motion presences of moving objects saturations and image noise break the rank 1 structure of the ldr images to address these problems we present a rank minimization algorithm which simultaneously aligns ldr images and detects outliers for robust hdr generation we evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state of the art hdr algorithms using challenging real world examples

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

heask robust homography estimation based on appearance similarity and keypoint correspondences abstract accurate homography estimation is a classical problem with high industrial value and has been investigated extensively most previous homography estimation methods used either appearance similarity or keypoint correspondences to find their best estimation in this paper a novel algorithm is proposed which integrates the advantages of the pixel based and the feature based homography estimation approaches we elegantly combined the probability models of appearance similarity and keypoint correspondences in a maximum likelihood framework which is named as homography estimation based on appearance similarity and keypoint correspondences heask in the model of keypoint correspondences the distribution of inlier location error is represented by a laplacian distribution which outperforms the previous gaussian distribution in characterizing heavy tailed distributions and in the model of appearance similarity the enhanced correlation coefficient ecc is adopted for describing image similarity and the distribution of ecc is studied and parametrically formulated using a truncated exponential distribution the proposed model is solved based on an improved framework of random sample consensus ransac several simulations summarize the performance of the proposed approach in objective quality measurement subjective visual quality and computation time the experimental results demonstrate that the proposed approach can achieve more accurate homography estimation under different image transformation degrees and with different ratios of inlier keypoint correspondences as compared to the state of the art works

exploring local and overall ordinal information for robust feature description this paper aims to build robust feature descriptors by exploring intensity order information in a patch to this end the local intensity order pattern liop and the overall intensity order pattern oiop are proposed to effectively encode intensity order information of each pixel in different aspects specifically liop captures the local ordinal information by using the intensity relationships among all the neighbouring sampling points around a pixel while oiop exploits the coarsely quantized overall intensity order of these sampling points these two kinds of patterns are then separately aggregated into different ordinal bins leading to two kinds of feature descriptors furthermore as these two kinds of descriptors could encode complementary ordinal information they are combined together to obtain a discriminative and compact mixed intensity order pattern descriptor all these descriptors are constructed on the basis of relative relationships of intensities in a rotationally invariant way making them be inherently invariant to image rotation and any monotonic intensity changes experimental results on image matching and object recognition are encouraging demonstrating the superiorities of our descriptors over the state of the art

testing the parametric null model hypothesis in semiparametric partial and generalized spline models on etend les resultats de cox et koh 1986 aux modeles generalises de wahba 1985 et a divers autres modeles splines partiels on montre que la statistique de test a une relation etroite avec le comportement de la fonction de validation croisee generalisee

matrix algebra for higher order moments abstract a large part of statistics is devoted to the estimation of models from the sample covariance matrix the development of the statistical theory and estimators has been greatly facilitated by the introduction of special matrices such as the commutation matrix and the duplication matrix and the corresponding matrix algebra some more extensive models require however estimation based on higher order moments typically third and fourth order moments an example is the popular kenny judd model that includes interactions between latent variables this paper introduces some special matrices that can be used to simplify the model expressions for third fourth and higher order moments gives some relationships between these matrices and related matrices and gives some formulas for kronecker products of three and four matrices the theory is applied to derive convenient expressions for third and fourth order moments of some structural equation models

discriminative learning of local image descriptors in this paper we explore methods for learning local image descriptors from training data we describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest neighbor classifier we consider both linear and nonlinear transforms with dimensionality reduction and make use of discriminant learning techniques such as linear discriminant analysis lda and powell minimization to solve for the parameters using these techniques we obtain descriptors that exceed state of the art performance with low dimensionality in addition to new experiments and recommendations for descriptor learning we are also making available a new and realistic ground truth data set based on multiview stereo data

osri a rotationally invariant binary descriptor binary descriptors are becoming widely used in computer vision field because of their high matching efficiency and low memory requirements since conventional approaches which first compute a floating point descriptor then binarize it are computationally expensive some recent efforts have focused on directly computing binary descriptors from local image patches although these binary descriptors enable a significant speedup in processing time their performances usually drop a lot due to orientation estimation errors and limited description abilities to address these issues we propose a novel binary descriptor based on the ordinal and spatial information of regional invariants osris over a rotation invariant sampling pattern our main contributions are twofold 1 each bit in osri is computed based on difference tests of regional invariants over pairwise sampling regions instead of difference tests of pixel intensities commonly used in existing binary descriptors which can significantly enhance the discriminative ability and 2 rotation and illumination changes are handled well by ordering pixels according to their intensities and gradient orientations meanwhile which is also more reliable than those methods that resort to a reference orientation for rotation invariance besides a statistical analysis of discriminative abilities of different parts in the descriptor is conducted to design a cascade filter which can reject nonmatching descriptors at early stages by comparing just a small portion of the whole descriptor further reducing the matching time extensive experiments on four challenging data sets oxford 53 objects zubud and kentucky show that osri significantly outperforms two state of the art binary descriptors freak and orb the matching performance of osri with only 512 bits is also better than the well known floating point descriptor sift 4k bits and is comparable with the state of the art floating point descriptor mrogh 6k bits while it is two orders of magnitude faster to match than sift and mrogh

testing for cubic smoothing splines under dependent data in most research on smoothing splines the focus has been on estimation while inference especially hypothesis testing has received less attention by defining design matrices for fixed and random effects and the structure of the covariance matrices of random errors in an appropriate way the cubic smoothing spline admits a mixed model formulation which places this nonparametric smoother firmly in a parametric setting thus nonlinear curves can be included with random effects and random coefficients the smoothing parameter is the ratio of the random coefficient and error variances and tests for linear regression reduce to tests for zero random coefficient variances we propose an exact f test for the situation and investigate its performance in a real pine stem data set and by simulation experiments under certain conditions the suggested methods can also be applied when the data are dependent

bayesian confidence intervals for the cross validated smoothing spline summary we consider the model y t g ti ei i 17 2 n where g t t 0 1 is a smooth function and the ei are independent n 0 a2 errors with g2 unknown the cross validated smoothing spline can be used to estimate g non parametrically from

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

a rotationally invariant descriptor based on mixed intensity feature histograms abstract this paper proposes a novel method to build a rotation invariant local descriptor by mixed intensity feature histogram most existing local descriptors based on intensity ordinal information typically encode only one local feature for each sampling point in the image patch to address this problem we proposed a method to encode more than one different local features for each pixel in the image patch and construct a 2d mixed intensity feature histogram from which our proposed mifh descriptor is then constructed since the rotation invariant coordinate system is adopted the mifh descriptor does not need to estimate the reference orientation in order to evaluate the performance and the robustness of the mifh with other well known local descriptors e g sift gloh daisy hri csltp liop mrogh image matching experiments were carried out on standard oxford dataset additional image pairs with complex illumination changes and image sequences with different noises to further investigate the discriminative ability of the mifh descriptor a simple object recognition experiment was conducted on three public datasets the experimental results demonstrate that our descriptor mifh exhibits better performance and robustness than other evaluated local descriptors

contrast invariant interest point detection by zero norm log filter the laplacian of gaussian log filter is widely used in interest point detection however low contrast image structures though stable and significant are often submerged by the high contrast ones in the response image of the log filter and hence are difficult to be detected to solve this problem we derive a generalized log filter and propose a zero norm log filter the response of the zero norm log filter is proportional to the weighted number of bright dark pixels in a local region which makes this filter be invariant to the image contrast based on the zero norm log filter we develop an interest point detector to extract local structures from images compared with the contrast dependent detectors such as the popular scale invariant feature transform detector the proposed detector is robust to illumination changes and abrupt variations of images experiments on benchmark databases demonstrate the superior performance of the proposed zero norm log detector in terms of the repeatability and matching score of the detected points as well as the image recognition rate under different conditions

contour model based homography estimation of texture less planar objects in uncalibrated images this paper proposes a homograhpy estimation algorithm that uses contour model to track and locate the texture less object in high clutter environments the proposed method consists of a homography recognition with the correspondences between model and image lines unknown followed by a nonlinear homography optimization based on the maximum likelihood criterion in our approach the initial estimation of the homography is obtained in the framework of ransac like which contains two stages hypothesizing and verifying the first stage generates a number of homograhpy hypotheses from the correspondences of the quadrangle like structures next the homography hypotheses are quickly ranked according to a redefined distance function in the refinement procedure the model sample points are projected into the image plane after that 1d search is utilized along the normal direction to obtain the corresponding image point for each model sample point finally the optimized homography is obtained by minimizing the errors between the sample points and their corresponding image points experiments show that the proposed method performs robust homography recognition of texture less planar objects and maintains accurate and stable homography estimation in the cluttered environments as well as the cases of extreme slant angles we proposed an effective homography recognition based on quadrangle like structures we derived a new distance function between lines in the probabilistic criterion we presented a robust homography optimization without image contour extraction we solved the nonlinear homography refinement by the maximum likelihood approach

one dimensional mapping for estimating projective transformations in this paper the 1 d mapping is studied for estimating a general projective transformation between images first a ray model of image patches is proposed to convert a complex projective deformation of a patch in the cartesian space into two simple rigid shifts of radial lines in the log polar inverse polar space to capture these shifts via direct search two types of radial image line matching viz 1 d log polar mapping and 1 d inverse polar mapping are formally defined also the unique features of these shifts are analyzed then a novel two step mathematical framework is devised for transformation estimation in the first step the 1 d log polar mapping is applied to estimating four affine parameters via two alternative methods for simulated real images in the second step the 1 d inverse polar mapping is applied to estimating two projective parameters by a direct search combined with the two step line matching two translational parameters are also determined finally the performance of the framework is evaluated first in estimating a variety of projective transformations by comparing the estimates with their ground truths and between the two proposed methods and second in registering a wide range of challenging image pairs by comparing with a state of the art feature based method

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

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

rotation and illumination invariant interleaved intensity order based local descriptor the region descriptors using local intensity ordering patterns have become more popular recent years for image matching due to its enhanced discriminative ability however the dimension of these descriptors increases rapidly with the slight increase in the number of local neighbors under consideration and becomes unreasonable for image matching due to time constraint in this paper we reduce the dimension of the descriptor and matching time significantly while keeping up the comparable performance by considering the number of neighboring sample points in an interleaved manner the proposed interleaved order based local descriptor iold considers the local neighbors of a pixel as a set of interleaved neighbors and constructs the descriptor over each set separately and finally combines them to produce a single pattern we extract the local ordering pattern to cope up with the illumination effect in an inherent rotation invariant manner the novelty lies with using multiple neighboring sets in an interleaved fashion we also explored the local intensity order pattern in a multisupport region scenario results are compared over three challenging and widely adopted image matching data sets with other prominent descriptors under various image transformations results based on experiments suggest that the proposed iold descriptor outperforms in terms of both improved matching performance and reduced matching time we also found that the amount of improvement is significant under complex illumination difference while showing more robustness toward noise

generalizing inverse compositional and esm image alignment inverse compositional ic image alignment baker and matthews in int j comput vis 56 3 221 255 2004 uses the symmetry between the roles of the fixed and moving images for faster processing however it requires implementation of compositional optimizer update steps the ic approach can be viewed as an efficient way of computing the similarity measure derivative relative to the fixed image warp parameters since the mapping between the fixed and moving warp parameters is continuous and differentiable this derivative can be converted into the moving warp space using the chain rule this avoids the need for compositional update steps our generalization also allows the efficient second order method esm malis in proceedings of the 2004 ieee international conference on robotics and automation icra04 pp 1843 1848 2004 benhimane and malis in ieee rsj international conference on intelligent robots and systems 2004 malis and benhimane in robot auton syst 52 1 39 52 2005 to be applied to general parameterizations of the transformation experiments using multiple similarity measures and optimizers show that our generalized ic method equals or exceeds the performance of the original ic approach the generalized esm approach is more reliable than the classic approach as it increases the capture radius of the optimization

bayesian model estimation and selection for epipolar geometry and generic manifold fitting computer vision often involves estimating models from visual input sometimes it is possible to fit several different models or hypotheses to a set of data and a decision must be made as to which is most appropriate this paper explores ways of automating the model selection process with specific emphasis on the least squares problem of fitting manifolds in particular algebraic varieties e g lines algebraic curves planes etc to data points illustrated with respect to epipolar geometry the approach is bayesian and the contribution three fold first a new bayesian description of the problem is laid out that supersedes the author s previous maximum likelihood formulations this formulation will reveal some hidden elements of the problem second an algorithm mapsac is provided to obtain the robust map estimate of an arbitrary manifold third a bayesian model selection paradigm is proposed the bayesian formulation of the manifold fitting problem uncovers an elegant solution to this problem for which a new method gric for approximating the posterior probability of each putative model is derived this approximations bears some similarity to the penalized likelihoods used by aic bic and mdl however it is far more accurate in situations involving large numbers of latent variables whose number increases with the data this will be empirically and theoretically demonstrated

linear multi view reconstruction and camera recovery using a reference plane this paper presents a linear algorithm for simultaneous computation of 3d points and camera positions from multiple perspective views based on having a reference plane visible in all views the reconstruction and camera recovery is achieved in a single step by finding the null space of a matrix built from image data using singular value decomposition contrary to factorization algorithms this approach does not need to have all points visible in all views this paper investigates two reference plane configurations finite reference planes defined by four coplanar points and infinite reference planes defined by vanishing points a further contribution of this paper is the study of critical configurations for configurations with four coplanar points by simultaneously reconstructing points and views we can exploit the numerical stabilizing effect of having wide spread cameras with large mutual baselines this is demonstrated by reconstructing the outside and inside courtyard of a building on the basis of 35 views in one single singular value decomposition

bayesian confidence intervals for smoothing splines abstract the frequency properties of wahba s bayesian confidence intervals for smoothing splines are investigated by a large sample approximation and by a simulation study when the coverage probabilities for these pointwise confidence intervals are averaged across the observation points the average coverage probability acp should be close to the nominal level from a frequency point of view this agreement occurs because the average posterior variance for the spline is similar to a consistent estimate of the average squared error and because the average squared bias is a modest fraction of the total average squared error these properties are independent of the bayesian assumptions used to derive this confidence procedure and they explain why the acp is accurate for functions that are much smoother than the sample paths prescribed by the prior this analysis accounts for the choice of the smoothing parameter bandwidth using cross validation in the case of natural splines an adaptive method for avo

on p values for smooth components of an extended generalized additive model the problem of testing smooth components of an extended generalized additive model for equality to zero is considered confidence intervals for such components exhibit good across the function coverage probabilities if based on the approximate result hat f i n f i v f i i where f is the vector of evaluated values for the smooth component of interest and v f is the covariance matrix for f according to the bayesian view of the smoothing process based on this result a wald type test of f 0 is proposed it is shown that care must be taken in selecting the rank used in the test statistic the method complements previous work by extending applicability beyond the gaussian case while considering tests of zero effect rather than testing the parametric hypothesis given by the null space of the component s smoothing penalty the proposed p values are routine and efficient to compute from a fitted model without requiring extra model fits or null distribution simulation

human action recognition in unconstrained videos by explicit motion modeling human action recognition in unconstrained videos is a challenging problem with many applications most state of the art approaches adopted the well known bag of features representations generated based on isolated local patches or patch trajectories where motion patterns such as object object and object background relationships are mostly discarded in this paper we propose a simple representation aiming at modeling these motion relationships we adopt global and local reference points to explicitly characterize motion information so that the final representation is more robust to camera movements which widely exist in unconstrained videos our approach operates on the top of visual codewords generated on dense local patch trajectories and therefore does not require foreground background separation which is normally a critical and difficult step in modeling object relationships through an extensive set of experimental evaluations we show that the proposed representation produces a very competitive performance on several challenging benchmark data sets further combining it with the standard bag of features or fisher vector representations can lead to substantial improvements

a novel algorithm for view and illumination invariant image matching the challenges in local feature based image matching are variations of view and illumination many methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors however the matching performance is still unstable and inaccurate particularly when large variation in view or illumination occurs in this paper we propose a view and illumination invariant image matching method we iteratively estimate the relationship of the relative view and illumination of the images transform the view of one image to the other and normalize their illumination for accurate matching our method does not aim to increase the invariance of the detector but to improve the accuracy stability and reliability of the matching results the performance of matching is significantly improved and is not affected by the changes of view and illumination in a valid range the proposed method would fail when the initial view and illumination method fails which gives us a new sight to evaluate the traditional detectors we propose two novel indicators for detector evaluation namely valid angle and valid illumination which reflect the maximum allowable change in view and illumination respectively extensive experimental results show that our method improves the traditional detector significantly even in large variations and the two indicators are much more distinctive

true multi image alignment and its application to mosaicing and lens distortion correction multiple images of a scene are related through 2d 3d view transformations and linear and nonlinear camera transformations we present an algorithm for true multi image alignment that does not rely on the measurements of a reference image being distortion free the algorithm is developed to specifically align and mosaic images using parametric transformations in the presence of lens distortion when lens distortion is present none of the images can be assumed to be ideal in our formulation all the images are modeled as intensity measurements represented in their respective coordinate systems each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation the goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images key advantages of the technique presented in this paper are i no reliance on one distortion free image ii ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence and iii ability to handle linear and nonlinear transformations within the same framework results of applying the algorithm are presented for the correction of lens distortion and creation of video mosaics