shape illumination and reflectance from shading a fundamental problem in computer vision is that of inferring the intrinsic 3d structure of the world from flat 2d images of that world traditional methods for recovering scene properties such as shape reflectance or illumination rely on multiple observations of the same scene to overconstrain the problem recovering these same properties from a single image seems almost impossible in comparison there are an infinite number of shapes paint and lights that exactly reproduce a single image however certain explanations are more likely than others surfaces tend to be smooth paint tends to be uniform and illumination tends to be natural we therefore pose this problem as one of statistical inference and define an optimization problem that searches for the most likely explanation of a single image our technique can be viewed as a superset of several classic computer vision problems shape from shading intrinsic images color constancy illumination estimation etc and outperforms all previous solutions to those constituent problems

harmony potentials the hierarchical conditional random field hcrf model have been successfully applied to a number of image labeling problems including image segmentation however existing hcrf models of image segmentation do not allow multiple classes to be assigned to a single region which limits their ability to incorporate contextual information across multiple scales at higher scales in the image this representation yields an oversimplified model since multiple classes can be reasonably expected to appear within large regions this simplified model particularly limits the impact of information at higher scales since class label information at these scales is usually more reliable than at lower noisier scales neglecting this information is undesirable to address these issues we propose a new consistency potential for image labeling problems which we call the harmony potential it can encode any possible combination of labels penalizing only unlikely combinations of classes we also propose an effective sampling strategy over this expanded label set that renders tractable the underlying optimization problem our approach obtains state of the art results on two challenging standard benchmark datasets for semantic image segmentation pascal voc 2010 and msrc 21

adaptive multiple subtraction with wavelet based complex unary wiener filters abstractadaptive subtraction is a key element in predictive multiple suppression methods it minimizes misalignments and amplitude differences between modeled and actual multiples and thus reduces multiple contamination in the data set after subtraction due to the high crosscorrelation between their waveforms the main challenge resides in attenuating multiples without distorting primaries as they overlap on a wide frequency range we split this wide band problem into a set of more tractable narrow band filter designs using a 1d complex wavelet frame this decomposition enables a single pass adaptive subtraction via complex single sample unary wiener filters consistently estimated on overlapping windows in a complex wavelet transformed domain each unary filter compensates for amplitude differences within its frequency support and can correct small and large misalignment errors through phase and integer delay corrections this approach greatly simplifies the matching filter estimation and despit

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

shortest paths with higher order regularization this paper describes a new method of finding thin elongated structures in images and volumes we use shortest paths to minimize very general functionals of higher order curve properties such as curvature and torsion our method uses line graphs to find the optimal path on a given discretization often in the order of seconds on a single computer the curves are then refined using local optimization making it possible to recover very smooth curves we are able to place constraints on our curves such as maximum integrated curvature or a maximum curvature at any point of the curve to our knowledge we are the first to perform experiments in three dimensions with curvature and torsion regularization the largest graphs we process have over a hundred billion arcs experiments on medical images and in multi view reconstruction show the significance and practical usefulness of higher order regularization

constrained least squares spectral analysis application to seismic data abstractan inversion based algorithm for computing the time frequency analysis of reflection seismograms using constrained least squares spectral analysis is formulated and applied to modeled seismic waveforms and real seismic data the fourier series coefficients are computed as a function of time directly by inverting a basis of truncated sinusoidal kernels for a moving time window the method resulted in spectra that have reduced window smearing for a given window length relative to the discrete fourier transform irrespective of window shape and a time frequency analysis with a combination of time and frequency resolution that is superior to the short time fourier transform and the continuous wavelet transform the reduction in spectral smoothing enables better determination of the spectral characteristics of interfering reflections within a short window the degree of resolution improvement relative to the short time fourier transform increases as window length decreases as compared with the continu

spectral decomposition of seismic data with continuous wavelet transform this paper presents a new methodology for computing a time frequency map for nonstationary signals using the continuous wavelet transform cwt the conventional method of producing a time frequency map using the short time fourier transform stft limits time frequency resolution by a predefined window length in contrast the cwt method does not require preselecting a window length and does not have a fixed time frequency resolution over the timefrequency space cwt uses dilation and translation of a wavelet to produce a time scale map a single scale encompasses a frequency band and is inversely proportional to the time support of the dilated wavelet previous workers have converted a time scale map into a time frequency map by taking the center frequencies of each scale we transform the time scale map by taking the fourier transform of the inverse cwt to produce a time frequency map thus a time scale map is converted into a time frequency map in which the amplitudes of individual frequencies rather than frequency bands are represented we refer to such a map as the time frequency cwt tfcwt we validate our approach with a nonstationary synthetic example and compare the results with the stft and a typical cwt spectrum two field examples illustrate that the tfcwt potentially can be used to detect frequency shadows caused by hydrocarbons and to identify subtle stratigraphic features for reservoir characterization

seismic data analysis by adaptive sparse time frequency decomposition the variation of frequency content of a seismic trace with time carries information about the properties of the subsurface reflectivity sequence time frequency tf analysis is a significant tool to extract such information for seismostratigraphic interpretation purposes however several tf transforms have been reported in the literature higher resolution and sensitivity to local changes of the signal have always mattered we have developed an adaptive high resolution tf transform that is performed in two sequential steps first the window length is adaptively determined for each sample of the signal such that it leads to maximum compactness of energy in the resulting tf plane second the generated nonstationary windows are used to inversely decompose the signal under study via a convex constrained sparse optimization where a mixed norm of the tf coefficients is minimized subject to invertibility of the transform later on the optimized transform is used as an efficient tool for seismic data analysis such as thin bed characterization and thin bedded gas reservoir detection in the case of gas reservoir detection based on amplitude versus offset analysis in the tf domain a simple new method called the difference section was evaluated the results of various numerical examples from synthetic and field data revealed a remarkable performance of the proposed method compared with the state of the art tf transforms

multi view scene flow estimation a view centered variational approach we present a novel method for recovering the 3d structure and scene flow from calibrated multi view sequences we propose a 3d point cloud parametrization of the 3d structure and scene flow that allows us to directly estimate the desired unknowns a unified global energy functional is proposed to incorporate the information from the available sequences and simultaneously recover both depth and scene flow the functional enforces multi view geometric consistency and imposes brightness constancy and piecewise smoothness assumptions directly on the 3d unknowns it inherently handles the challenges of discontinuities occlusions and large displacements the main contribution of this work is the fusion of a 3d representation and an advanced variational framework that directly uses the available multi view information this formulation allows us to advantageously bind the 3d unknowns in time and space different from optical flow and disparity the proposed method results in a nonlinear mapping between the images coordinates thus giving rise to additional challenges in the optimization process our experiments on real and synthetic data demonstrate that the proposed method successfully recovers the 3d structure and scene flow despite the complicated nonconvex optimization problem

an adaptive optimal kernel time frequency representation time frequency representations with fixed windows or kernels figure prominently in many applications but perform well only for limited classes of signals representations with signal dependent kernels can overcome this limitation however while they often perform well most existing schemes are block oriented techniques unsuitable for on line implementation or for tracking signal components with characteristics that change with time the time frequency representation developed in the present paper based on a signal dependent radially gaussian kernel that adapts over time surmounts these difficulties the method employs a short time ambiguity function both for kernel optimization and as an intermediate step in computing constant time slices of the representation careful algorithm design provides reasonably efficient computation and allows on line implementation certain enhancements such as cone kernel constraints and approximate retention of marginals are easily incorporated with little additional computation while somewhat more expensive than fixed kernel representations this new technique often provides much better performance several examples illustrate its behavior on synthetic and real world signals

multichannel matching pursuit for seismic trace decomposition the technique of matching pursuit can adaptively decompose a seismic trace into a series of wavelets however the solution is not unique and is also strongly affected by data noise multichannel matching pursuit mcmp exploiting lateral coherence as a constraint might improve the uniqueness of the solution it extracts a constituent wavelet that has an optimal correlation coefficient to neighboring traces instead of to a single trace only according to linearity theory a wavelet shared by neighboring traces is the best match to the average of multiple traces and therefore it might effectively suppress the data noise and stabilize the performance it is found that the mcmp scheme greatly improves spatial continuity in decomposition and can generate a plausible time frequency spectrum with high resolution for reservoir detection

the existence of regular orbits for nilpotent groups consider the following situation suppose r a cr where r is an r group and c is a cyclic group of order qe where r and q are distinct primes let k be a finite field of characteristic p and suppose v is a faithful irreducible k cr module this situation has been widely studied when p q it is similar to the situation occurring in the proof of theorem b of hall and higman 9 when p q it is similar to the situation occurring in the corresponding theorem of shult 131 in this paper a generalization of this situation is considered suppose g 4 ag where g is a solvable group a is nilpotent and ia g 1 again we assume v is a faithful irreducible k ag module two related questions arise in the study of this situation

investigation of generalized s transform analysis windows for time frequency analysis of seismic reflection data abstractthe frequency dependent width of the gaussian window function used in the s transform may not be ideal for all applications in particular in seismic reflection prospecting the temporal resolution of the resulting s transform time frequency spectrum at low frequencies may not be sufficient for certain seismic interpretation purposes a simple parameterization of the generalized s transform overcomes the drawback of poor temporal resolution at low frequencies inherent in the s transform at the necessary expense of reduced frequency resolution this is accomplished by replacing the frequency variable in the gaussian window with a linear function containing two coefficients that control resolution variation with frequency the linear coefficients can be directly calculated by selecting desired temporal resolution at two frequencies the resulting transform conserves energy and is readily invertible by an inverse fourier transform this modification of the s transform when applied to synthetic and

learning to refine depth for robust stereo estimation abstract traditional depth estimation from stereo images is usually formulated as a patch matching problem which requires post processing stages to impose smoothness and handle depth discontinuities and occlusions while recent deep network approaches directly learn a regressor for the entire disparity map they still suffer from large errors near the depth discontinuities in this paper we propose a novel method to refine the disparity maps generated by deep regression networks instead of relying on ad hoc post processing we learn a unified deep network model that predicts a confidence map and the disparity gradients from the learned feature representation in regression networks we integrate the initial disparity estimation the confidence map and the disparity gradients into a continuous markov random field mrf for depth refinement which is capable of representing rich surface structures our disparity mrf model can be solved via efficient global optimization in a closed form we evaluate our approach on both synthetic and real world datasets and the results show it achieves the state of art performance and produces more structure preserving disparity maps with smaller errors in the neighborhood of depth boundaries

roof duality complementation and persistency in quadratic 0 1 optimization the paper is concerned with the primal problem of maximizing a given quadratic pseudo boolean function four equivalent problems are discussed the primal the complementation the discrete rhys lp and the weighted stability problem of a sam graph each of them has a relaxation the roof dual the quadratic complementation the continuous rhys lp and the fractional weighted stability problem of a sam graph the main result is that the four gaps associated with the four relaxations are equal furthermore a solution to any of these problems leads at once to solutions of the other three equivalent ones the four relaxations can be solved in polynomial time by transforming them to a bipartite maximum flow problem the optimal solutions of the roof dual define best linear majorantsp x off having the following persistency property if theith coefficient inp is positive negative thenx i 1 0 in every optimum of the primal problem several characterizations are given for the case where these persistency results cannot be used to fix any variable of the primal on the other hand a class of gap free functions properly including the supermodular ones is exhibited

stereo matching with color weighted correlation hierarchical belief propagation and occlusion handling in this paper we formulate a stereo matching algorithm with careful handling of disparity discontinuity and occlusion the algorithm works with a global matching stereo model based on an energy minimization framework the global energy contains two terms the data term and the smoothness term the data term is first approximated by a color weighted correlation then refined in occluded and low texture areas in a repeated application of a hierarchical loopy belief propagation algorithm the experimental results are evaluated on the middlebury data sets showing that our algorithm is the top performer among all the algorithms listed there

improved recursive geodesic distance computation for edge preserving filter all known recursive filters based on the geodesic distance affinity are realized by two 1d recursions applied in two orthogonal directions of the image plane the 2d extension of the filter is not valid and has theoretically drawbacks which lead to known artifacts in this paper a maximum influence propagation method is proposed to approximate the 2d extension for the geodesic distance based recursive filter the method allows to partially overcome the drawbacks of the 1d recursion approach we show that our improved recursion better approximates the true geodesic distance filter and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance as an application we consider a geodesic distance based filter for image denoising experimental evaluation of our denoising method demonstrates comparable and for several test images better results than state of the art approaches while our algorithm is considerably faster with computational complexity o 8p

progressive match expansion via coherent subspace constraint in this paper a quasi dense matching algorithm called progressive match expansion via coherent subspace pecs is proposed our algorithm starts from a set of sparse seeds and iteratively expands potential matches in the region around the seeds until no more matches could be found the main difference from previous methods is that our method computes a coherent non rigid transformation to relate the matching pixels instead of using local planar affine model to approximate the real scene surface within the expanding region the points in the expanding region are treated as a whole and all the matches satisfying the transformation are found at once first dense sift descriptors are extracted and a subspace encoding the feature similarity between two point sets is computed this embedding pulls two points closer if they have similar features and makes the non rigid transformation learning more robust second a coherent non rigid transformation in the subspace is computed to move one point set towards the other and the spatially aligned two points denote a match the proposed model is robust to scenes with non smooth surface and experiment results reveal the good performance in both 2d image matching and 3d reconstruction

filter based mean field inference for random fields with higher order terms and product label spaces recently a number of cross bilateral filtering methods have been proposed for solving multi label problems in computer vision such as stereo optical flow and object class segmentation that show an order of magnitude improvement in speed over previous methods these methods have achieved good results despite using models with only unary and or pairwise terms however previous work has shown the value of using models with higher order terms e g to represent label consistency over large regions or global co occurrence relations we show how these higher order terms can be formulated such that filter based inference remains possible we demonstrate our techniques on joint stereo and object labelling problems as well as object class segmentation showing in addition for joint object stereo labelling how our method provides an efficient approach to inference in product label spaces we show that we are able to speed up inference in these models around 10 30 times with respect to competing graph cut move making methods as well as maintaining or improving accuracy in all cases we show results on pascalvoc 10 for object class segmentation and leuven for joint object stereo labelling

instantaneous spectral attributes using scales in continuous wavelet transform instantaneous spectral properties of seismic data center frequency root mean square frequency bandwidth often are extracted from time frequency spectra to describe frequency dependent rock properties these attributes are derived using definitions from probability theory a time frequency spectrum can be obtained from approaches such as short time fourier transform stft or time frequency continuous wavelet transform tfcwt tfcwt does not require preselecting a time window which is essential in stft the tfcwt method converts a scalogram i e time scale map obtained from the continuous wavelet transform cwt into a time frequency map however our method includes mathematical formulas that compute the instantaneous spectral attributes from the scalogram similar to those computed from the tfcwt avoiding conversion into a time frequency spectrum computation does not require a predefined window length because it is based on the cwt this technique optimally decomposes a multiscale signal f

constrained optical flow estimation as a matching problem in general discretization in the motion vector domain yields an intractable number of labels in this paper we propose an approach that can reduce general optical flow to the constrained matching problem by pre estimating a 2 d disparity labeling map of the desired discrete motion vector function one of the goals of the proposed paper is estimating coarse distribution of motion vectors and then utilizing this distribution as global constraints for discrete optical flow estimation this pre estimation is done with a simple frame to frame correlation technique also known as the digital symmetric phase only filter spof we discover a strong correlation between the output of the spof and the motion vector distribution of the related optical flow a two step matching paradigm for optical flow estimation is applied pixel accuracy integer flow and subpixel accuracy estimation the matching problem is solved by global optimization experiments on the middlebury optical flow datasets confirm our intuitive assumptions about strong correlation between motion vector distribution of optical flow and maximal peaks of spof outputs the overall performance of the proposed method is promising and achieves state of the art results on the middlebury benchmark

minimizing nonsubmodular functions with graph cuts a review optimization techniques based on graph cuts have become a standard tool for many vision applications these techniques allow to minimize efficiently certain energy functions corresponding to pairwise markov random fields mrfs currently there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of mrf energies e g submodular functions in this survey we review some results that show that graph cuts can be applied to a much larger class of energy functions in particular nonsubmodular functions while these results are well known in the optimization community to our knowledge they were not used in the context of computer vision and mrf optimization we demonstrate the relevance of these results to vision on the problem of binary texture restoration

exact optimization for markov random fields with convex priors we introduce a method to solve exactly a first order markov random field optimization problem in more generality than was previously possible the mrf has a prior term that is convex in terms of a linearly ordered label set the method maps the problem into a minimum cut problem for a directed graph for which a globally optimal solution can be found in polynomial time the convexity of the prior function in the energy is shown to be necessary and sufficient for the applicability of the method

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

efficient matrix completion for seismic data reconstruction abstractdespite recent developments in improved acquisition seismic data often remain undersampled along source and receiver coordinates resulting in incomplete data for key applications such as migration and multiple prediction we have interpreted the missing trace interpolation problem in the context of matrix completion mc and developed three practical principles for using low rank optimization techniques to recover seismic data specifically we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix we use an optimization program that restores this low rank structure to recover the full volume omitting one or more of these principles can lead to poor interpolation results as we found experimentally in light of this theory we compensate for the high rank behavior of data in the source receiver domain by using the midpoint offset transformation for 2d data and a source receiver permutation for 3d data to red

the robustness of seismic attenuation measurements using fixed and variable window time frequency transforms frequency based methods for measuring seismic attenuation are used commonly in exploration geophysics to measure the spectrum of a nonstationary seismic signal different methods are available including transforms with time windows that are either fixed or systematically varying with the frequency being analyzed we compare four time frequency transforms and show that the choice of a fixed or variable window transform affects the robustness and accuracy of the resulting attenuation measurements for fixed window transforms we use the short time fourier transform and gabor transform the s transform and continuous wavelet transform are analyzed as the variable length transforms first we conduct a synthetic transmission experiment and compare the frequency dependent scattering attenuation to the theoretically predicted values from this procedure we find that variable window transforms reduce the uncertainty and biasof the resulting attenuation estimate specifically at the upper and lower ends of th

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

wide baseline foreground object interpolation using silhouette shape prior we consider the synthesis of intermediate views of an object captured by two widely spaced and calibrated cameras this problem is challenging because foreshortening effects and occlusions induce significant differences between the reference images when the cameras are far apart that makes the association or disappearance appearance of their pixels difficult to estimate our main contribution lies in disambiguating this ill posed problem by making the interpolated views consistent with a plausible transformation of the object silhouette between the reference views this plausible transformation is derived from an object specific prior that consists of a nonlinear shape manifold learned from multiple previous observations of this object by the two reference cameras the prior is used to estimate the evolution of the epipolar silhouette segments between the reference views this information directly supports the definition of epipolar silhouette segments in the intermediate views as well as the synthesis of textures in those segments it permits to reconstruct the epipolar plane images epis and the continuum of views associated with the epi volume obtained by aggregating the epis experiments on synthetic and natural images show that our method preserves the object topology in intermediate views and deals effectively with the self occluded regions and the severe foreshortening effect associated with wide baseline camera configurations

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

seismic spectral decomposition using deconvolutive short time fourier transform spectrogram the spectral decomposition technique plays an important role in reservoir characterization for which the time frequency distribution method is essential the deconvolutive short time fourier transform dstft method achieves a superior time frequency resolution by applying a 2d deconvolution operation on the short time fourier transform stft spectrogram for seismic spectral decomposition to reduce the computation burden caused by the 2d deconvolution operation in the dstft the 2d stft spectrogram is cropped into a smaller area which includes the positive frequencies fallen in the seismic signal bandwidth only in general because the low frequency components of a seismic signal are dominant the removal of the negative frequencies may introduce a sharp edge at the zero frequency which would produce artifacts in the dstft spectrogram to avoid this problem we used the analytic signal which is obtained by applying the hilbert transform on the original real seismic signal to calculate the stft spectrogram in our method synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method

continuous 3d label stereo matching using local expansion moves we present an accurate stereo matching method using local expansion moves based on graph cuts this new move making scheme is used to efficiently infer per pixel 3d plane labels on a pairwise markov random field mrf that effectively combines recently proposed slanted patch matching and curvature regularization terms the local expansion moves are presented as many alpha expansions defined for small grid regions the local expansion moves extend traditional expansion moves by two ways localization and spatial propagation by localization we use different candidate alpha labels according to the locations of local alpha expansions by spatial propagation we design our local alpha expansions to propagate currently assigned labels for nearby regions with this localization and spatial propagation our method can efficiently infer mrf models with a continuous label space using randomized search our method has several advantages over previous approaches that are based on fusion moves or belief propagation it produces submodular moves deriving a subproblem optimality it helps find good smooth piecewise linear disparity maps it is suitable for parallelization it can use cost volume filtering techniques for accelerating the matching cost computations even using a simple pairwise mrf our method is shown to have best performance in the middlebury stereo benchmark v2 and v3

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

a pixel dissimilarity measure that is insensitive to image sampling because of image sampling traditional measures of pixel dissimilarity can assign a large value to two corresponding pixels in a stereo pair even in the absence of noise and other degrading effects we propose a measure of dissimilarity that is provably insensitive to sampling because it uses the linearly interpolated intensity functions surrounding the pixels experiments on real images show that our measure alleviates the problem of sampling with little additional computational overhead

registration and interactive planar segmentation for stereo images of polyhedral scenes we introduce a two step iterative segmentation and registration method to find coplanar surfaces among stereo images of a polyhedral environment the novelties of this paper are i to propose a user defined initialization easing the image matching and segmentation ii to incorporate color appearance and planar projection information into a bayesian segmentation scheme and iii to add consistency to the projective transformations related to the polyhedral structure of the scenes the method utilizes an assisted bayesian color segmentation scheme the initial user assisted segmentation is used to define search regions for planar homography image registration the two reliable methods cooperate to obtain probabilities for coplanar regions with similar color information that are used to get a new segmentation by means of quadratic markov measure fields qmmf we search for the best regions by iterating both steps registration and segmentation

a comparative study of energy minimization methods for markov random fields with smoothness based priors among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel labeling tasks such as depth or texture computation it has been known for decades that such problems can be elegantly expressed as markov random fields yet the resulting energy minimization problems have been widely viewed as intractable algorithms such as graph cuts and loopy belief propagation lbp have proven to be very powerful for example such methods form the basis for almost all the top performing stereo methods however the trade offs among different energy minimization algorithms are still not well understood in this paper we describe a set of energy minimization benchmarks and use them to compare the solution quality and runtime of several common energy minimization algorithms we investigate three promising methods graph cuts lbp and tree reweighted message passing in addition to the well known older iterated conditional mode icm algorithm our benchmark problems are drawn from published energy functions used for stereo image stitching interactive segmentation and denoising we also provide a general purpose software interface that allows vision researchers to easily switch between optimization methods the benchmarks code images and results are available at http vision middlebury edu mrf

time frequency analysis of seismic data using local attributes time frequency analysis is an important technology in seismic data processing and interpretation to localize frequency content in time we have developed a novel method for computing a time frequency map for nonstationary signals using an iterative inversion framework we calculated time varying fourier coefficients by solving a least squares problem that uses regularized nonstationary regression we defined the time frequency map as the norm of time varying coefficients time varying average frequency of the seismic data can also be estimated from the time frequency map calculated by our method we tested the method on benchmark synthetic signals and compared it with the well known s transform two field data examples showed applications of the proposed method for delineation of sand channels and for detection of low frequency anomalies

global color sparseness and a local statistics prior for fast bilateral filtering the property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool however its non linear nature results in a computationally costly operation various works propose fast approximations to the bilateral filter however the majority does not generalize to vector input as is the case with color images we propose a fast approximation to the bilateral filter for color images the filter is based on two ideas first the number of colors which occur in a single natural image is limited we exploit this color sparseness to rewrite the initial non linear bilateral filter as a number of linear filter operations second we impose a statistical prior to the image values that are locally present within the filter window we show that this statistical prior leads to a closed form solution of the bilateral filter finally we combine both ideas into a single fast and accurate bilateral filter for color images experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation but with limited accuracy which has potential application in real time video filtering our bilateral filter which combines color sparseness and local statistics yields a fast and accurate bilateral filter approximation and obtains the state of the art results

the regular module problem i in the study of induced representations the following problem arises let h ag be a finite solvable group and k a field with chark a let v be an irreducible faithful primitive k 4g module suppose h contains a normal extraspecial r subgroup r with z r z h and that a acts faith fully on r under what conditions does a have a regular direct summand in vi in this paper we consider this question under the hypotheses that g mr where 1 m is normal abelian in am a is nilpotent a mr m r 1 and r z r is a faithful irreducible am modu e we show that a has at least three regular direct summands in v unless a exp m and r satisfy certain very restrictive conditions let h ag be a finite solvable group where a is nilpotent g is normal and y4 g 1 let k be a field of characteristic not dividing a and v a zc module assume that v is faithful and irreducible the study of induced representations often leads to the following two questions 1 regular orbit problem under what conditions does a have a regular orbit on f

global stereo reconstruction under second order smoothness priors second order priors on the smoothness of 3d surfaces are a better model of typical scenes than first order priors however stereo reconstruction using global inference algorithms such as graph cuts has not been able to incorporate second order priors because the triple cliques needed to express them yield intractable nonsubmodular optimization problems this paper shows that inference with triple cliques can be effectively performed our optimization strategy is a development of recent extensions to alpha expansion based on the ldquo qpbordquo algorithm the strategy is to repeatedly merge proposal depth maps using a novel extension of qpbo proposal depth maps can come from any source for example frontoparallel planes as in alpha expansion or indeed any existing stereo algorithm with arbitrary parameter settings