fundamental principles on learning new features for effective dense matching in dense matching including stereo matching and optical flow nearly all existing approaches are based on simple features such as gray or rgb color gradient or simple transformations like census to calculate matching costs these features do not perform well in complex scenes that may involve radiometric changes noises overexposure and or textureless regions various problems may appear such as wrong matching at the pixel or region level flattening breaking of edges and or even entire structural collapse in this paper we propose two fundamental principles based on the consistency and the distinctiveness of features we show that almost all existing problems in dense matching are caused by features that violate one or both of these principles to systematically learn good features for dense matching we develop a general multi objective optimization based on these two principles and apply convolutional neural networks to find new features that lie on the pareto frontier by using two frame optical flow and stereo matching as applications our experimental results show that the features learned can significantly improve the performance of state of the art approaches based on the kitti benchmarks our method ranks first on the two stereo benchmarks and is the best among existing two frame optical flow algorithms on flow benchmarks

shape estimation from shading defocus and correspondence using light field angular coherence light field cameras are quickly becoming commodity items with consumer and industrial applications they capture many nearby views simultaneously using a single image with a micro lens array thereby providing a wealth of cues for depth recovery defocus correspondence and shading in particular apart from conventional image shading one can refocus images after acquisition and shift one s viewpoint within the sub apertures of the main lens effectively obtaining multiple views we present a principled algorithm for dense depth estimation that combines defocus and correspondence metrics we then extend our analysis to the additional cue of shading using it to refine fine details in the shape by exploiting an all in focus image in which pixels are expected to exhibit angular coherence we define an optimization framework that integrates photo consistency depth consistency and shading consistency we show that combining all three sources of information defocus correspondence and shading outperforms state of the art light field depth estimation algorithms in multiple scenarios

adaptive support weight approach for correspondence search we present a new window based method for correspondence search using varying support weights we adjust the support weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity our method outperforms other local methods on standard stereo benchmarks

image modeling and denoising with orientation adapted gaussian scale mixtures we develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation neighborhoods are modeled as samples of a multivariate gaussian density that are modulated and rotated according to the values of two hidden random variables thus allowing the model to adapt to the local amplitude and orientation of the signal a third hidden variable selects between this oriented process and a nonoriented scale mixture of gaussians process thus providing adaptability to the local orientedness of the signal based on this model we develop an optimal bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with gaussian scale mixtures

a fast approximation of the bilateral filter using a signal processing approach the bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges it has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics and fast versions have been proposed unfortunately little is known about the accuracy of such accelerations in this paper we propose a new signal processing analysis of the bilateral filter which complements the recent studies that analyzed it as a pde or as a robust statistical estimator the key to our analysis is to express the filter in a higher dimensional space where the signal intensity is added to the original domain dimensions importantly this signal processing perspective allows us to develop a novel bilateral filtering acceleration using downsampling in space and intensity this affords a principled expression of accuracy in terms of bandwidth and sampling the bilateral filter can be expressed as linear convolutions in this augmented space followed by two simple nonlinearities this allows us to derive criteria for downsampling the key operations and achieving important acceleration of the bilateral filter we show that for the same running time our method is more accurate than previous acceleration techniques typically we are able to process a 2 megapixel image using our acceleration technique in less than a second and have the result be visually similar to the exact computation that takes several tens of minutes the acceleration is most effective with large spatial kernels furthermore this approach extends naturally to color images and cross bilateral filtering

global stabilization of a class of invertible mimo nonlinear systems in this paper we consider the class of input affine mimo nonlinear systems which are assumed to be invertible and input output linearizable this class includes as a trivial special case the class of mimo systems possessing a well defined vector relative degree it is shown that if a system in this class is strongly minimum phase in a sense specified in the paper it is globally stabilizable via partial state feedback or semiglobally stabilizable via dynamic output feedback the result substantially broadens the class of nonlinear mimo systems for which global semiglobal stabilization via limited state information is known to be possible

robust design of nonlinear internal models without adaptation we propose a solution to the problem of semiglobal output regulation for nonlinear minimum phase systems driven by uncertain exosystems that does not rely upon conventional adaptation schemes to estimate the frequency of the exogenous signals rather the proposed approach relies upon regression like arguments used to derive a nonlinear internal model able to offset the presence of an unknown number of harmonic exogenous inputs of uncertain amplitude phase and frequency the design methodology guarantees asymptotic regulation if the dimension of the regulator exceeds a lower bound determined by the actual number of harmonic components of the exogenous input if this is not the case a bounded steady state regulation error is ensured whose amplitude though can be arbitrarily decreased by acting on a design parameter of the regulator

stereo matching using tree filtering matching cost aggregation is one of the oldest and still popular methods for stereo correspondence while effective and efficient cost aggregation methods typically aggregate the matching cost by summing averaging over a user specified local support region this is obviously only locally optimal and the computational complexity of the full kernel implementation usually depends on the region size in this paper the cost aggregation problem is re examined and a non local solution is proposed the matching cost values are aggregated adaptively based on pixel similarity on a tree structure derived from the stereo image pair to preserve depth edges the nodes of this tree are all the image pixels and the edges are all the edges between the nearest neighboring pixels the similarity between any two pixels is decided by their shortest distance on the tree the proposed method is non local as every node receives supports from all other nodes on the tree the proposed method can be naturally extended to the time domain for enforcing temporal coherence unlike previous methods the non local property guarantees that the depth edges will be preserved when the temporal coherency between all the video frames are considered a non local weighted median filter is also proposed based on the non local cost aggregation algorithm it has been demonstrated to outperform all local weighted median filters on disparity depth upsampling and refinement

robust output regulation for invertible nonlinear mimo systems abstract the robust output regulation problem for a class of invertible nonlinear mimo systems is addressed in this paper under a mild assumption on the high frequency gain matrix and strongly minimum phase assumption a robust regulator is proposed with the combination of nonlinear internal model and a new extended high gain observer more explicitly the robust output regulation is achieved by employing a novel extended high gain observer based stabilizer such that the regulated system augmented with the nonlinear internal model is asymptotically stabilized the novelty of this approach consists in the use of an extended observer to estimate under mild assumptions all unmeasured terms

bayesian texture classification based on contourlet transform and byy harmony learning of poisson mixtures as a newly developed 2 d extension of the wavelet transform using multiscale and directional filter banks the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification in this paper we propose a novel bayesian texture classifier based on the adaptive model selection learning of poisson mixtures on the contourlet features of texture images the adaptive model selection learning of poisson mixtures is carried out by the recently established adaptive gradient bayesian ying yang harmony learning algorithm for poisson mixtures it is demonstrated by the experiments that our proposed bayesian classifier significantly improves the texture classification accuracy in comparison with several current state of the art texture classification approaches

image denoising using scale mixtures of gaussians in the wavelet domain we describe a method for removing noise from digital images based on a statistical model of the coefficients of an overcomplete multiscale oriented basis neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables a gaussian vector and a hidden positive scalar multiplier the latter modulates the local variance of the coefficients in the neighborhood and is thus able to account for the empirically observed correlation between the coefficient amplitudes under this model the bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable we demonstrate through simulations with images contaminated by additive white gaussian noise that the performance of this method substantially surpasses that of previously published methods both visually and in terms of mean squared error

texture classification and segmentation using wavelet packet frame and gaussian mixture model in this paper we propose a scheme for texture classification and segmentation the methodology involves an extraction of texture features using the wavelet packet frame decomposition this is followed by a gaussian mixture based classifier which assigns each pixel to the class each subnet of the classifier is modeled by a gaussian mixture model and each texture image is assigned to the class to which pixels of the image most belong this scheme shows high recognition accuracy in the classification of brodatz texture images it can also be expanded to an unsupervised texture segmentation using a kullback leibler divergence between two gaussian mixtures the proposed method was successfully applied to brodatz mosaic image segmentation and fabric defect detection

accurate stereo matching by two step energy minimization in stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques due to their global extent energy minimization methods obtain good stereo matching results however they tend to fail in occluded regions in which cost filtering approaches obtain better results in this paper we intend to combine both the approaches with the aim to improve overall stereo matching results we show that a global optimization with a fully connected model can be solved by cost filtering methods based on this observation we propose to perform stereo matching as a two step energy minimization algorithm we consider two markov random field mrf models 1 a fully connected model defined on the complete set of pixels in an image and 2 a conventional locally connected model we solve the energy minimization problem for the fully connected model after which the marginal function of the solution is used as the unary potential in the locally connected mrf model experiments on the middlebury stereo data sets show that the proposed method achieves the state of the arts results

joint histogram based cost aggregation for stereo matching this paper presents a novel method for performing efficient cost aggregation in stereo matching the cost aggregation problem is reformulated from the perspective of a histogram giving us the potential to reduce the complexity of the cost aggregation in stereo matching significantly differently from previous methods which have tried to reduce the complexity in terms of the size of an image and a matching window our approach focuses on reducing the computational redundancy that exists among the search range caused by a repeated filtering for all the hypotheses moreover we also reduce the complexity of the window based filtering through an efficient sampling scheme inside the matching window the tradeoff between accuracy and complexity is extensively investigated by varying the parameters used in the proposed method experimental results show that the proposed method provides high quality disparity maps with low complexity and outperforms existing local methods this paper also provides new insights into complexity constrained stereo matching algorithm design

feature extraction for texture discrimination via random field models with random spatial interaction in this paper we attack the problem of distinguishing textured images of real surfaces using small samples we first analyze experimental data that results from applying ordinary conditional markov fields in the face of the disappointing performance of these models we introduce a random field with spatial interaction that is itself a random variable usually referred to as a random field in a random environment for this class of models we establish the power spectrum and the autocorrelation function as well defined quantities and we devise a scheme for the estimation of related parameters the new set of features that resulted from this approach was applied to real images accurate discrimination was observed even for boxes of size 10 spl times 16

dominant local binary patterns for texture classification this paper proposes a novel approach to extract image features for texture classification the proposed features are robust to image rotation less sensitive to histogram equalization and noise it comprises of two sets of features dominant local binary patterns dlbp in a texture image and the supplementary features extracted by using the circularly symmetric gabor filter responses the dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information while the gabor based features aim at supplying additional global textural information to the dlbp features through experiments the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram equalized randomly rotated and noise corrupted images in outex brodatz meastex and curet texture image databases our method has also been compared with six published texture features in the experiments it is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions

large displacement optical flow descriptor matching in variational motion estimation optical flow estimation is classically marked by the requirement of dense sampling in time while coarse to fine warping schemes have somehow relaxed this constraint there is an inherent dependency between the scale of structures and the velocity that can be estimated this particularly renders the estimation of detailed human motion problematic as small body parts can move very fast in this paper we present a way to approach this problem by integrating rich descriptors into the variational optical flow setting this way we can estimate a dense optical flow field with almost the same high accuracy as known from variational optical flow while reaching out to new domains of motion analysis where the requirement of dense sampling in time is no longer satisfied

yet another cost aggregation over models in the recent decades we have witnessed the advent of local or non local filters for cost aggregation in stereo matching pushing the envelope of local methods to the degree of global methods while maintaining the efficiency a specific filter with a specific parameter setting may have a potential to best work for an image pair but may not guarantee equally good performance for other image pairs to address this problem we propose a mixture of experts model which applies a heterogeneous set of filters on the cost volume and adaptively combines the results we employ supervised learning to estimate per pixel mixing coefficients which are used to adaptively control the weight of the filter responses through experiments we show that the mixture model significantly reduces errors in disparity estimation and even outperforms the strategy of selecting the best per pixel filter from the pool of filters in the average sense

output input stability implies feedback stabilization abstract we study the recently introduced notion of output input stability which is a robust variant of the minimum phase property for general smooth nonlinear control systems this paper develops the theory of output input stability in the multi input multi output setting we show that output input stability is a combination of two system properties one related to detectability and the other to left invertibility for systems affine in controls we derive a necessary and sufficient condition for output input stability which relies on a global version of the nonlinear structure algorithm this condition leads naturally to a globally asymptotically stabilizing state feedback strategy for affine output input stable systems

uniform practical nonlinear output regulation in this paper we present a solution to the problem of asymptotic and practical semiglobal regulation by output feedback for nonlinear systems a key feature of the proposed approach is that practical regulation is achieved uniformly with respect to the dimension of the internal model and to the gain of the stabilizer near the zero error manifold this property renders the approach interesting for a number of real cases by bridging the gap between output regulation theory and advanced engineering applications simulation results regarding meaningful control problems are also presented

near real time stereo matching using geodesic diffusion adaptive weight algorithms currently represent the state of the art in local stereo matching however due to their computational requirements these types of solutions are not suitable for real time implementation here we present a novel aggregation method inspired by the anisotropic diffusion technique used in image filtering the proposed aggregation algorithm produces results similar to adaptive weight solutions while reducing the computational requirements moreover near real time performance is demonstrated with a gpu implementation of the algorithm

stereo matching using belief propagation in this paper we formulate the stereo matching problem as a markov network and solve it using bayesian belief propagation the stereo markov network consists of three coupled markov random fields that model the following a smooth field for depth disparity a line process for depth discontinuity and a binary process for occlusion after eliminating the line process and the binary process by introducing two robust functions we apply the belief propagation algorithm to obtain the maximum a posteriori map estimation in the markov network other low level visual cues e g image segmentation can also be easily incorporated in our stereo model to obtain better stereo results experiments demonstrate that our methods are comparable to the state of the art stereo algorithms for many test cases

the laplacian pyramid as a compact image code we describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions the representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space pixel to pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself the result is a net data compression since the difference or error image has low variance and entropy and the low pass filtered image may represented at reduced sample density further data compression is achieved by quantizing the difference image these steps are then repeated to compress the low pass image iteration of the process at appropriately expanded scales generates a pyramid data structure the encoding process is equivalent to sampling the image with laplacian operators of many scales thus the code tends to enhance salient image features a further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression fast algorithms are described for coding and decoding

the nonsubsampled contourlet transform theory design and applications in this paper we develop the nonsubsampled contourlet transform nsct and study its applications the construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks the result is a flexible multiscale multidirection and shift invariant image decomposition that can be efficiently implemented via the a trous algorithm at the core of the proposed scheme is the nonseparable two channel nonsubsampled filter bank nsfb we exploit the less stringent design condition of the nsfb to design filters that lead to a nsct with better frequency selectivity and regularity when compared to the contourlet transform we propose a design framework based on the mapping approach that allows for a fast implementation based on a lifting or ladder structure and only uses one dimensional filtering in some cases in addition our design ensures that the corresponding frame elements are regular symmetric and the frame is close to a tight one we assess the performance of the nsct in image denoising and enhancement applications in both applications the nsct compares favorably to other existing methods in the literature

texture classification by modeling joint distributions of local patterns with gaussian mixtures texture classification generally requires the analysis of patterns in local pixel neighborhoods statistically the underlying processes are comprehensively described by their joint probability density functions jpdfs even for small neighborhoods however stable estimation of jpdfs by joint histograms jhsts is often infeasible since the number of entries in the jhst exceeds by far the number of pixels in a typical texture region moreover evaluation of distance functions between jhsts is often computationally prohibitive practically the number of entries in a jhst is therefore reduced by considering only two pixel patterns leading to 2d jhsts known as cooccurrence matrices or by quantization of the gray levels in local patterns to only two gray levels yielding local binary patterns lbps both approaches result in a loss of information we introduce here a framework for supervised texture classification which reduces or avoids this information loss local texture neighborhoods are first filtered by a filter bank without further quantization the jpdf of the filter responses is then described parametrically by gaussian mixture models gmms we show that the parameters of the gmms can be reliably estimated from small image regions moreover distances between the thus modelled jpdfs of different texture patterns can be computed efficiently in closed form from their model parameters we furthermore extend this texture descriptor to achieve full invariance to rotation we evaluate the framework for different filter banks on the brodatz texture set we first show that combining the lbp difference filters with the gmm based density estimator outperforms the classical lbp approach and its codebook extensions when replacing these rather elementary difference filters by the wavelet frame transform wft the performance of the framework on all 111 brodatz textures exceeds the one obtained more recently by spin image and rift descriptors by lazebnik et al

output feedback stabilization of nonlinear mimo systems having uncertain high frequency gain matrix abstract the purpose of this paper is to provide a method for semi global asymptotic stabilization of a nonlinear minimum phase mimo system under a mild hypothesis of the so called high frequency gain matrix this result is based on a non trivial extension to the mimo setting of the approach based on the use of extended observers as a byproduct a dynamic output feedback control is obtained that asymptotically stabilizes the equilibrium of the closed loop system in spite of uncertainties in the high frequency gain matrix

output stabilization via nonlinear luenberger observers the present paper addresses the problem of the existence of an output feedback law that asymptotically steers to zero prescribed outputs while keeping all state variables bounded for any initial conditions in a given compact set the problem can be viewed as an extension of the classical problem of semiglobally stabilizing the trajectories of a controlled system to a compact set the problem also encompasses a version of the classical problem of output regulation under only a weak minimum phase assumption it is shown that there exists a controller solving the problem at hand the paper is deliberately focused on theoretical results regarding the existence of such a controller practical aspects involving the design and the implementation of the controller are left to a forthcoming work

a modified algorithm for invertibility in nonlinear systems the algorithm of hirschorn 1 for the construction of inverses of nonlinear systems is modified and new sufficient conditions for invertibility and inverse system for a larger class of systems are obtained for the systems which do not satisfy the invertibility conditions a characterization of input space on which the input output map is injective is derived

translation invariant contourlet transform and its application to image denoising most subsampled filter banks lack the feature of translation invariance which is an important characteristic in denoising applications in this paper we study and develop new methods to convert a general multichannel multidimensional filter bank to a corresponding translation invariant ti framework in particular we propose a generalized algorithme agrave trous which is an extension of the algorithme agrave trous introduced for 1 d wavelet transforms using the proposed algorithm as well as incorporating modified versions of directional filter banks we construct the ti contourlet transform tict to reduce the high redundancy and complexity of the tict we also introduce semi translation invariant contourlet transform stict then we employ an adapted bivariate shrinkage scheme to the stict to achieve an efficient image denoising approach our experimental results demonstrate the benefits and potential of the proposed denoising approach complexity analysis and efficient realization of the proposed ti schemes are also presented

svd based modeling for image texture classification using wavelet transformation this paper introduces a new model for image texture classification based on wavelet transformation and singular value decomposition the probability density function of the singular values of wavelet transformation coefficients of image textures is modeled as an exponential function the model parameter of the exponential function is estimated using maximum likelihood estimation technique truncation of lower singular values is employed to classify textures in the presence of noise kullback leibler distance kld between estimated model parameters of image textures is used as a similarity metric to perform the classification using minimum distance classifier the exponential function permits us to have closed form expressions for the estimate of the model parameter and computation of the kld these closed form expressions reduce the computational complexity of the proposed approach experimental results are presented to demonstrate the effectiveness of this approach on the entire 111 textures from brodatz database the experimental results demonstrate that the proposed approach improves recognition rates using a lower number of parameters on large databases the proposed approach achieves higher recognition rates compared to the traditional subband energy based approach the hybrid imm svm approach and the ggd based approach