electromagnetic scattering by an inhomogeneous conducting or dielectric layer on a perfectly conducting plate we consider a two dimensional problem of scattering of a time harmonic electromagnetic plane wave by an inhomogeneous conducting or dielectric layer on a perfectly conducting plate the magnetic permeability is assumed to be a fixed positive constant in the media the material properties of the media are characterized completely by an index of refraction which is a bounded measurable function in the layer and a positive constant above the layer corresponding to a homogeneous dielectric medium in this paper we only examine the tm transverse magnetic polarization case a radiation condition is introduced and equivalence with a second kind lippmann schwinger type integral equation is shown with additional assumptions on the index of refraction in the layer uniqueness of solution is proved existence of solution is then established by employing a form of fredholm alternative using a general result on the solvability of integral equations on unbounded domains published earlier by chandler wilde and zhang an approximate analytic solution for the case of a thin inhomogeneous layer is obtained from the integral equation formulation and is used to show that if the index of refraction is appropriately chosen the scattered field can grow with distance from the plate

graph theoretical clustering based on limited neighbourhood sets abstract a method for clustering data according to a visual model of clusters is proposed the method uses either of two graphs which are defined according to relative distance and based on the gabriel graph and the relative neighbourhood graph respectively the method is locally sensitive hierarchic and based on the concept of limited neighbourhood sets clusters that are either disjoint or homogeneous and separable by sharp changes in point density may be detected

multiregion image segmentation by parametric kernel graph cuts the purpose of this study is to investigate multiregion graph cut image partitioning via kernel mapping of the image data the image data is transformed implicitly by a kernel function so that the piecewise constant model of the graph cut formulation becomes applicable the objective function contains an original data term to evaluate the deviation of the transformed data within each segmentation region from the piecewise constant model and a smoothness boundary preserving regularization term the method affords an effective alternative to complex modeling of the original image data while taking advantage of the computational benefits of graph cuts using a common kernel function energy minimization typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point computation a quantitative and comparative performance assessment is carried out over a large number of experiments using synthetic grey level data as well as natural images from the berkeley database the effectiveness of the method is also demonstrated through a set of experiments with real images of a variety of types such as medical synthetic aperture radar and motion maps

efficient calculation of the green function for acoustic propagation above a homogeneous impedance plane this paper is concerned with the problem of propagation from a monofrequency coherent line source above a plane of homogeneous surface impedance the solution of this problem occurs in the kernel of certain boundary integral equation formulations of acoustic propagation above an impedance boundary and the discussion of the paper is motivated by this application the paper starts by deriving representations as laplace type integrals of the solution and its first partial derivatives the evaluation of these integral representations by gauss laguerre quadrature is discussed and theoretical bounds on the truncation error are obtained specific approximations are proposed which are shown to be accurate except in the very near field for all angles of incidence and a wide range of values of surface impedance the paper finishes with derivations of partial results and analogous laplace type integral representations for the case of a point source

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 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

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

shape from sharp and motion blurred image pair motion blur due to camera shake is a common occurrence during image capture the apparent motion of a scene point in the image plane varies according to both camera motion and scene structure our objective is to infer the camera motion and the depth map of static scenes using motion blur as a cue to this end we use an unblurred blurred image pair initially we develop a technique to estimate the transformation spread function tsf which symbolizes the camera shake this technique uses blur kernels estimated at different points across the image based on the estimated tsf we recover the complete depth map of the scene within a regularization framework

scale and rotation invariant matching using linearly augmented trees we propose a novel linearly augmented tree method for efficient scale and rotation invariant object matching the proposed method enforces pairwise matching consistency defined on trees and high order constraints on all the sites of a template the pairwise constraints admit arbitrary metrics while the high order constraints use l1 norms and therefore can be linearized such a linearly augmented tree formulation introduces hyperedges and loops into the basic tree structure but different from a general loopy graph its special structure allows us to relax and decompose the optimization into a sequence of tree matching problems that are efficiently solvable by dynamic programming the proposed method also works on continuous scale and rotation parameters we can match with a scale up to any large value with the same efficiency our experiments on ground truth data and a variety of real images and videos show that the proposed method is efficient accurate and reliable

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

cccp algorithms to minimize the bethe and kikuchi free energies convergent alternatives to belief propagation this article introduces a class of discrete iterative algorithms that are provably convergent alternatives to belief propagation bp and generalized belief propagation gbp our work builds on recent results by yedidia freeman and weiss 2000 who showed that the fixed points of bp and gbp algorithms correspond to extrema of the bethe and kikuchi free energies respectively we obtain two algorithms by applying cccp to the bethe and kikuchi free energies respectively cccp is a procedure introduced here for obtaining discrete iterative algorithms by decomposing a cost function into a concave and a convex part we implement our cccp algorithms on two and three dimensional spin glasses and compare their results to bp and gbp our simulations show that the cccp algorithms are stable and converge very quickly the speed of cccp is similar to that of bp and gbp unlike cccp bp will often not converge for these problems gbp usually but not always converges the results found by cccp applied to the bethe or kikuchi free energies are equivalent or slightly better than those found by bp or gbp respectively when bp and gbp converge note that for these and other problems bp and gbp give very accurate results see yedidia et al 2000 and failure to converge is their major error mode finally we point out that our algorithms have a large range of inference and learning applications

on the solvability of second kind integral equations on the real line abstract this paper considers general second kind integral equations of the form s r k s t t dt s in operator form k k where the functions k and are assumed known with y the space of bounded continuous functions on r and k such that the mapping s k s from r to l1 r is bounded and continuous the function y is the solution to be determined conditions on a set w bc r l1 r are obtained such that a generalised fredholm alternative holds if w satisfies these conditions and i k k is injective for all k w then i k k is also surjective for all k w and moreover the inverse operators i k k 1 on y are uniformly bounded for k w the approximation of the kernel in the integral equation by a sequence kn converging in a weak sense to k is also considered and results on stability and convergence are obtained these general theorems are used to establish results for two special classes of kernels k s t s t z t and k s t s t s t t where l1 r z l r and bc r 0 r kernels of both classes arise in problems of time harmonic wave scattering by unbounded surfaces the general integral equation results are here applied to prove the existence of a solution for a boundary integral equation formulation of scattering by an infinite rough surface and to consider the stability and convergence of approximation of the rough surface problem by a sequence of diffraction grating problems of increasingly large period

patchmatch filter edge aware filtering meets randomized search for visual correspondence though many tasks in computer vision can be formulated elegantly as pixel labeling problems a typical challenge discouraging such a discrete formulation is often due to computational efficiency recent studies on fast cost volume filtering based on efficient edge aware filters provide a fast alternative to solve discrete labeling problems with the complexity independent of the support window size however these methods still have to step through the entire cost volume exhaustively which makes the solution speed scale linearly with the label space size when the label space is huge or even infinite which is often the case for subpixel accurate stereo and optical flow estimation their computational complexity becomes quickly unacceptable developed to search approximate nearest neighbors rapidly the patchmatch method can significantly reduce the complexity dependency on the search space size but its pixel wise randomized search and fragmented data access within the 3d cost volume seriously hinder the application of efficient cost slice filtering this paper presents a generic and fast computational framework for general multi labeling problems called patchmatch filter pmf we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation i e patchmatch based randomized search and efficient edge aware image filtering by decompositing an image into compact superpixels we also propose superpixel based novel search strategies that generalize and improve the original patchmatch method further motivated to improve the regularization strength we propose a simple yet effective cross scale consistency constraint which handles labeling estimation for large low textured regions more reliably than a single scale pmf algorithm focusing on dense correspondence field estimation in this paper we demonstrate pmf s applications in stereo and optical flow our pmf methods achieve top tier correspondence accuracy but run much faster than other related competing methods often giving over 10 100 times speedup

a multisize superpixel approach for salient object detection based on multivariate normal distribution estimation this paper presents a new method for salient object detection based on a sophisticated appearance comparison of multisize superpixels those superpixels are modeled by multivariate normal distributions in cie lab color space which are estimated from the pixels they comprise this fitting facilitates an efficient application of the wasserstein distance on the euclidean norm formula see text to measure perceptual similarity between elements saliency is computed in two ways on the one hand we compute global saliency by probabilistically grouping visually similar superpixels into clusters and rate their compactness on the other hand we use the same distance measure to determine local center surround contrasts between superpixels then an innovative locally constrained random walk technique that considers local similarity between elements balances the saliency ratings inside probable objects and background the results of our experiments show the robustness and efficiency of our approach against 11 recently published state of the art saliency detection methods on five widely used benchmark data sets

revisiting graph construction for fast image segmentation abstract in this paper we propose a simple but effective method for fast image segmentation we re examine the locality preserving character of spectral clustering by constructing a graph over image regions with both global and local connections our novel approach to build graph connections relies on two key observations 1 local region pairs that co occur frequently will have a high probability to reside on a common object 2 spatially distant regions in a common object often exhibit similar visual saliency which implies their neighborship in a manifold we present a novel energy function to efficiently conduct graph partitioning based on multiple high quality partitions we show that the generated eigenvector histogram based representation can automatically drive effective unary potentials for a hierarchical random field model to produce multi class segmentation sufficient experiments on the bsds500 benchmark large scale pascal voc and coco datasets demonstrate the competitive segmentation accuracy and significantly improved efficiency of our proposed method compared with other state of the arts

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

label information guided graph construction for semi supervised learning in the literature most existing graph based semi supervised learning methods only use the label information of observed samples in the label propagation stage while ignoring such valuable information when learning the graph in this paper we argue that it is beneficial to consider the label information in the graph learning stage specifically by enforcing the weight of edges between labeled samples of different classes to be zero we explicitly incorporate the label information into the state of the art graph learning methods such as the low rank representation lrr and propose a novel semi supervised graph learning method called semi supervised low rank representation this results in a convex optimization problem with linear constraints which can be solved by the linearized alternating direction method though we take lrr as an example our proposed method is in fact very general and can be applied to any self representation graph learning methods experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data and therefore is more effective for semi supervised learning tasks

a uniqueness result for scattering by infinite rough surfaces consider the dirichlet boundary value problem for the helmholtz equation in a non locally perturbed half plane with an unbounded piecewise lyapunov boundary this problem models time harmonic electromagnetic scattering in transverse magnetic polarization by one dimensional rough perfectly conducting surfaces a radiation condition is introduced for the problem which is a generalization of the usual one used in the study of diffraction by gratings when the solution is quasi periodic and allows a variety of incident fields including an incident plane wave to be included in the results obtained we show in this paper that the boundary value problem for the scattered field has at most one solution for the case when the whole boundary is lyapunov and is a small perturbation of a flat boundary we also prove existence of solution and show a limiting absorption principle

entropy rate clustering cluster analysis via maximizing a submodular function subject to a matroid constraint we propose a new objective function for clustering this objective function consists of two components the entropy rate of a random walk on a graph and a balancing term the entropy rate favors formation of compact and homogeneous clusters while the balancing function encourages clusters with similar sizes and penalizes larger clusters that aggressively group samples we present a novel graph construction for the graph associated with the data and show that this construction induces a matroid a combinatorial structure that generalizes the concept of linear independence in vector spaces the clustering result is given by the graph topology that maximizes the objective function under the matroid constraint by exploiting the submodular and monotonic properties of the objective function we develop an efficient greedy algorithm furthermore we prove an approximation bound of 1 2 for the optimality of the greedy solution we validate the proposed algorithm on various benchmarks and show its competitive performances with respect to popular clustering algorithms we further apply it for the task of superpixel segmentation experiments on the berkeley segmentation data set reveal its superior performances over the state of the art superpixel segmentation algorithms in all the standard evaluation metrics

constructing free energy approximations and generalized belief propagation algorithms important inference problems in statistical physics computer vision error correcting coding theory and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs the belief propagation bp algorithm is an efficient way to solve these problems that is exact when the factor graph is a tree but only approximate when the factor graph has cycles we show that bp fixed points correspond to the stationary points of the bethe approximation of the free energy for a factor graph we explain how to obtain region based free energy approximations that improve the bethe approximation and corresponding generalized belief propagation gbp algorithms we emphasize the conditions a free energy approximation must satisfy in order to be a valid or maxent normal approximation we describe the relationship between four different methods that can be used to generate valid approximations the bethe method the junction graph method the cluster variation method and the region graph method finally we explain how to tell whether a region based approximation and its corresponding gbp algorithm is likely to be accurate and describe empirical results showing that gbp can significantly outperform bp

feedback and surround modulated boundary detection edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes the human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations the classical approach assumes that these cells are only responsive to the stimulus present within their receptive fields however recent studies demonstrate that surrounding regions and inter areal feedback connections influence their responses significantly in this work we propose a biologically inspired edge detection model in which orientation selective neurons are represented through the first derivative of a gaussian function resembling double opponent cells in the primary visual cortex v1 in our model we account for four kinds of receptive field surround i e full far iso and orthogonal orientation whose contributions are contrast dependant the output signal from v1 is pooled in its perpendicular direction by larger v2 neurons employing a contrast variant centre surround kernel we further introduce a feedback connection from higher level visual areas to the lower ones the results of our model on three benchmark datasets show a big improvement compared to the current non learning and biologically inspired state of the art algorithms while being competitive to the learning based methods

simultaneous cast shadows illumination and geometry inference using hypergraphs the cast shadows in an image provide important information about illumination and geometry in this paper we utilize this information in a novel framework in order to jointly recover the illumination environment a set of geometry parameters and an estimate of the cast shadows in the scene given a single image and coarse initial 3d geometry we model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order markov random field mrf illumination model while we also introduce a method to obtain approximate image evidence for cast shadows capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous valued variables which make inference challenging taking advantage of domain knowledge we provide a two stage minimization technique for the mrf energy of our model we evaluate our method in different datasets both synthetic and real our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates allowing a generic coarse 3d model to represent a whole class of objects for the task of illumination estimation or the estimation of geometry parameters to refine our initial knowledge of scene geometry simultaneously with illumination estimation

efficient graph based image segmentation this paper addresses the problem of segmenting an image into regions we define a predicate for measuring the evidence for a boundary between two regions using a graph based representation of the image we then develop an efficient segmentation algorithm based on this predicate and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties we apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph and illustrate the results with both real and synthetic images the algorithm runs in time nearly linear in the number of graph edges and is also fast in practice an important characteristic of the method is its ability to preserve detail in low variability image regions while ignoring detail in high variability regions

recursive nearest agglomeration rena fast clustering for approximation of structured signals in this work we revisit fast dimension reduction approaches as with random projections and random sampling our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis such dimension reduction can be very efficient when the signals of interest have a strong structure such as with images we focus on this setting and investigate feature clustering schemes for data reductions that capture this structure an impediment to fast dimension reduction is then that good clustering comes with large algorithmic costs we address it by contributing a linear time agglomerative clustering scheme recursive nearest agglomeration rena unlike existing fast agglomerative schemes it avoids the creation of giant clusters we empirically validate that it approximates the data as well as traditional variance minimizing clustering schemes that have a quadratic complexity in addition we analyze signal approximation with feature clustering and show that it can remove noise improving subsequent analysis steps as a consequence data reduction by clustering features with rena yields very fast and accurate models enabling to process large datasets on budget our theoretical analysis is backed by extensive experiments on publicly available data that illustrate the computation efficiency and the denoising properties of the resulting dimension reduction scheme

enhanced figure ground classification with background prior propagation we present an adaptive figure ground segmentation algorithm that is capable of extracting foreground objects in a generic environment starting from an interactively assigned background mask an initial background prior is defined and multiple soft label partitions are generated from different foreground priors by progressive patch merging these partitions are fused to produce a foreground probability map the probability map is then binarized via threshold sweeping to create multiple hard label candidates a set of segmentation hypotheses is formed using different evaluation scores from this set the hypothesis with maximal local stability is propagated as the new background prior and the segmentation process is repeated until convergence similarity voting is used to select a winner set and the corresponding hypotheses are fused to yield the final segmentation result experiments indicate that our method performs at or above the current state of the art on several data sets with particular success on challenging scenes that contain irregular or multiple connected foregrounds

effective medium approximation in the localization theory saddle point in a lagrangian formulation abstract a new method of studying the metal insulator transition is proposed all physical quantities are represented in terms of a field theory model in an extended space which consists of the real space and the space of supermatrices the minimum of the effective lagrangian describing the model gives equations coinciding with the corresponding equations derived before on the bethe lattice and in high dimensionality a concept of a function order parameter is discussed in the saddle point approximation the density density correlation function both in the metal and insulator regions is calculated

computing loop corrections by message passing any spanning tree in a loopy interaction graph can be used for communicating the effect of the loopy interactions by introducing messages that are passed along the edges in the spanning tree this defines an exact mapping of the problem on the loopy interaction graph onto an extended problem on a tree interaction graph where the thermodynamic quantities can be computed by a message passing algorithm based on the bethe equations we propose an approximation loop correction algorithm for the ising model relying on the above representation of the problem the algorithm deals at the same time with the short and long loops and can be used to obtain upper and lower bounds for the free energy

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

saliency region detection based on markov absorption probabilities in this paper we present a novel bottom up salient object detection approach by exploiting the relationship between the saliency detection and the markov absorption probability first we calculate a preliminary saliency map by the markov absorption probability on a weighted graph via partial image borders as background prior unlike most of the existing background prior based methods which treated all image boundaries as background we only use the left and top sides as background for simplicity the saliency of each element is defined as the sum of the corresponding absorption probability by several left and top virtual boundary nodes which are most similar to it second a better result is obtained by ranking the relevance of the image elements with foreground cues extracted from the preliminary saliency map which can effectively emphasize the objects against the background whose computation is processed similarly as that in the first stage and yet substantially different from the former one at last three optimization techniques content based diffusion mechanism superpixelwise depression function and guided filter are utilized to further modify the saliency map generalized at the second stage which is proved to be effective and complementary to each other both qualitative and quantitative evaluations on four publicly available benchmark data sets demonstrate the robustness and efficiency of the proposed method against 17 state of the art methods

slic superpixels compared to state of the art superpixel methods computer vision applications have come to rely increasingly on superpixels in recent years but it is not always clear what constitutes a good superpixel algorithm in an effort to understand the benefits and drawbacks of existing methods we empirically compare five state of the art superpixel algorithms for their ability to adhere to image boundaries speed memory efficiency and their impact on segmentation performance we then introduce a new superpixel algorithm simple linear iterative clustering slic which adapts a k means clustering approach to efficiently generate superpixels despite its simplicity slic adheres to boundaries as well as or better than previous methods at the same time it is faster and more memory efficient improves segmentation performance and is straightforward to extend to supervoxel generation

image segmentation by map ml estimations image segmentation plays an important role in computer vision and image analysis in this paper image segmentation is formulated as a labeling problem under a probability maximization framework to estimate the label configuration an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori map estimation and the maximum likelihood ml estimation the map estimation problem is modeled with markov random fields mrfs and a graph cut algorithm is used to find the solution to the map estimation the ml estimation is achieved by computing the means of region features in a gaussian model our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance its results match image edges very well and are consistent with human perception comparing to six state of the art algorithms extensive experiments have shown that our algorithm performs the best

the tractability of segmentation and scene analysis one of the fundamental problems in computer vision is the segmentation of an image into semantically meaningful regions based only on image characteristics a single segmentation can be determined using a linear number of evaluations of a uniformity predicate however minimising the number of regions is shown to be an np complete problem we also show that the variational approach to segmentation based on minimising a criterion combining the overall variance of regions and the number of regions also gives rise to an np complete problem when a library of object models is available segmenting the image becomes a problem of scene analysis a sufficient condition for the reconstruction of a 3d scene from a 2d image to be solvable in polynomial time is that the scene contains no cycles of mutually occluding objects and that no range information can be deduced from the image it is known that relaxing the no cycles condition renders the problem np complete we show that relaxing the no range information condition also produces an np complete problem

statistical physics of loopy interactions independent loop approximation and beyond we consider an interacting system of spin variables on a loopy interaction graph identified by a tree graph and a set of loopy interactions we start from a high temperature expansion for loopy interactions represented by a sum of non negative contributions from all the possible frustration free loop configurations we then compute the loop corrections using different approximations for the nonlocal loop interactions induced by the spin correlations in the tree graph for distant loopy interactions we can exploit the exponential decay of correlations in the tree interaction graph to compute loop corrections within an independent loop approximation higher orders of the approximation are obtained by considering the correlations between the nearby loopy interactions involving larger number of spin variables in particular the sum over the loop configurations can be computed exactly by the belief propagation algorithm in the low orders of the approximation as long as the loopy interactions have a tree structure these results might be useful in developing more accurate and convergent message passing algorithms exploiting the structure of loopy interactions

a tree structured framework for purifying complex clusters with structural roles of individual data how can we find a natural clustering of a complex dataset which may contain an unknown number of overlapping clusters of arbitrary shape and be contaminated by noise a tree structured framework is proposed in this paper to purify such clusters by exploring the structural role of each data in practice each individual object within the internal organization of the data has its own specific role centroid hub or outlier due to distinctive associations with their respective neighbors adjacent centroids always interact on each other and serve as mediate nodes of one tree being members of some cluster hubs closed to some centroid become leaf nodes responsible for the termination of the growth of trees outliers that weakly touch with any centroid are often discarded from any trees as global noise all the data can thus be labeled by a specified criterion of centroids connected structural consistency ccsc free of domain specific information our framework with ccsc could widely adapt to many clustering related applications theoretical and experimental contributions both confirm that our framework is easy to interpret and implement efficient and effective in complex clustering

on the solvability of a class of second kind integral equations on unbounded domains we consider integral equations of the form x x k x y z y y dy in operator form kz where is some subset ofrn n 1 the functionsk z and are assumed known withz l and y the space of bounded continuous functions on the function yis to be determined the class of domains and kernelskconsidered includes the case rnandk x y x y with l1 rn in which case ifzis the characteristic function of some setg the integral equation is one of wiener hopf type the main theorems proved using arguments derived from collectively compact operator theory are conditions on a setw l which ensure that ifi kzis injective for allz wtheni kzis also surjective and moreover the inverse operators i kz 1onyare bounded uniformly inz these general theorems are used to recover classical results on wiener hopf integral operators of21and19 and generalisations of these results and are applied to analyse the lippmann schwinger integral equation

directed connected operators asymmetric hierarchies for image filtering and segmentation connected operators provide well established solutions for digital image processing typically in conjunction with hierarchical schemes in graph based frameworks such operators basically rely on symmetric adjacency relations between pixels in this article we introduce a notion of directed connected operators for hierarchical image processing by also considering non symmetric adjacency relations the induced image representation models are no longer partition hierarchies i e trees but directed acyclic graphs that generalize standard morphological tree structures such as component trees binary partition trees or hierarchical watersheds we describe how to efficiently build and handle these richer data structures and we illustrate the versatility of the proposed framework in image filtering and image segmentation

distribution matching with the bhattacharyya similarity a bound optimization framework we present efficient graph cut algorithms for three problems 1 finding a region in an image so that the histogram or distribution of an image feature within the region most closely matches a given model 2 co segmentation of image pairs and 3 interactive image segmentation with a user provided bounding box each algorithm seeks the optimum of a global cost function based on the bhattacharyya measure a convenient alternative to other matching measures such as the kullback leibler divergence our functionals are not directly amenable to graph cut optimization as they contain non linear functions of fractional terms which make the ensuing optimization problems challenging we first derive a family of parametric bounds of the bhattacharyya measure by introducing an auxiliary labeling then we show that these bounds are auxiliary functions of the bhattacharyya measure a result which allows us to solve each problem efficiently via graph cuts we show that the proposed optimization procedures converge within very few graph cut iterations comprehensive and various experiments including quantitative and comparative evaluations over two databases demonstrate the advantages of the proposed algorithms over related works in regard to optimality computational load accuracy and flexibility

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

automatically finding clusters in normalized cuts normalized cuts is a state of the art spectral method for clustering by applying spectral techniques the data becomes easier to cluster and then k means is classically used unfortunately the number of clusters must be manually set and it is very sensitive to initialization moreover k means tends to split large clusters to merge small clusters and to favor convex shaped clusters in this work we present a new clustering method which is parameterless independent from the original data dimensionality and from the shape of the clusters it only takes into account inter point distances and it has no random steps the combination of the proposed method with normalized cuts proved successful in our experiments

regularized tree partitioning and its application to unsupervised image segmentation in this paper we propose regularized tree partitioning approaches we study normalized cut ncut and average cut acut criteria over a tree forming two approaches 1 normalized tree partitioning ntp and 2 average tree partitioning atp we give the properties that result in an efficient algorithm for ntp and atp in addition we present the relations between the solutions of ntp and atp over the maximum weight spanning tree of a graph and ncut and acut over this graph to demonstrate the effectiveness of the proposed approaches we show its application to image segmentation over the berkeley image segmentation data set and present qualitative and quantitative comparisons with state of the art methods

an optimal graph theoretic approach to data clustering theory and its application to image segmentation a novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated the data to be clustered are represented by an undirected adjacency graph g with arc capacities assigned to reflect the similarity between the linked vertices clustering is achieved by removing arcs of g to form mutually exclusive subgraphs such that the largest inter subgraph maximum flow is minimized for graphs of moderate size approximately 2000 vertices the optimal solution is obtained through partitioning a flow and cut equivalent tree of g which can be efficiently constructed using the gomory hu algorithm 1961 however for larger graphs this approach is impractical new theorems for subgraph condensation are derived and are then used to develop a fast algorithm which hierarchically constructs and partitions a partially equivalent tree of much reduced size this algorithm results in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices the new clustering algorithm is applied to the image segmentation problem the segmentation is achieved by effectively searching for closed contours of edge elements equivalent to minimum cuts in g which consist mostly of strong edges while rejecting contours containing isolated strong edges this method is able to accurately locate region boundaries and at the same time guarantees the formation of closed edge contours

self validated labeling of markov random fields for image segmentation this paper addresses the problem of self validated labeling of markov random fields mrfs namely to optimize an mrf with unknown number of labels we present graduated graph cuts ggc a new technique that extends the binary s t graph cut for self validated labeling specifically we use the split and merge strategy to decompose the complex problem to a series of tractable subproblems in terms of gibbs energy minimization a suboptimal labeling is gradually obtained based upon a set of cluster level operations by using different optimization structures we propose three practical algorithms tree structured graph cuts tsgc net structured graph cuts nsgc and hierarchical graph cuts hgc in contrast to previous methods the proposed algorithms can automatically determine the number of labels properly balance the labeling accuracy spatial coherence and the labeling cost i e the number of labels and are computationally efficient independent to initialization and able to converge to good local minima of the objective energy function we apply the proposed algorithms to natural image segmentation experimental results show that our algorithms produce generally feasible segmentations for benchmark data sets and outperform alternative methods in terms of robustness to noise speed and preservation of soft boundaries

image segmentation by a contrario simulation segmenting an image into homogeneous regions generally involves a decision criterion to establish whether two adjacent regions are similar decisions should be adaptive to get robust and accurate segmentation algorithms avoid hazardous a priori and have a clear interpretation we propose a decision process based on a contrario reasoning two regions are meaningfully different if the probability of observing such a difference in pure noise is very low since the existing analytical methods are intractable in our case we extend them to allow a mixed use of analytical computations and monte carlo simulations the resulting decision criterion is tested experimentally through a simple merging algorithm which can be used as a post filtering and validation step for existing segmentation methods

geometrically induced force interaction for three dimensional deformable models in this paper we propose a novel 3 d deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions this external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient the evolution of the deformable model is solved using the level set method so that topological changes are handled automatically the relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves the geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries and it gives the deformable model a high invariancy in initialization configurations the voxel interactions across the whole image domain provide a global view of the object boundary representation giving the external force a long attraction range the bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross boundary initializations and facilitates the handling of weak edges and broken boundaries in addition we show that by enhancing the geometrical interaction field with a nonlocal edge preserving algorithm the new deformable model can effectively overcome image noise we provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images and show that the proposed method achieves significant improvements against existing image gradient techniques

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

automatic image segmentation by dynamic region merging this paper addresses the automatic image segmentation problem in a region merging style with an initially oversegmented image in which many regions or superpixels with homogeneous color are detected an image segmentation is performed by iteratively merging the regions according to a statistical test there are two essential issues in a region merging algorithm order of merging and the stopping criterion in the proposed algorithm these two issues are solved by a novel predicate which is defined by the sequential probability ratio test and the minimal cost criterion starting from an oversegmented image neighboring regions are progressively merged if there is an evidence for merging according to this predicate we show that the merging order follows the principle of dynamic programming this formulates the image segmentation as an inference problem where the final segmentation is established based on the observed image we also prove that the produced segmentation satisfies certain global properties in addition a faster algorithm is developed to accelerate the region merging process which maintains a nearest neighbor graph in each iteration experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm

globally optimal regions and boundaries as minimum ratio weight cycles we describe a new form of energy functional for the modeling and identification of regions in images the energy is defined on the space of boundaries in the image domain and can incorporate very general combinations of modeling information both from the boundary intensity gradients etc and from the interior of the region texture homogeneity etc we describe two polynomial time digraph algorithms for finding the global minima of this energy one of the algorithms is completely general minimizing the functional for any choice of modeling information it runs in a few seconds on a 256 spl times 256 image the other algorithm applies to a subclass of functionals but has the advantage of being extremely parallelizable neither algorithm requires initialization

multi region active contours with a single level set function segmenting an image into an arbitrary number of coherent regions is at the core of image understanding many formulations of the segmentation problem have been suggested over the past years these formulations include among others axiomatic functionals which are hard to implement and analyze and graph based alternatives which impose a non geometric metric on the problem we propose a novel method for segmenting an image into an arbitrary number of regions using an axiomatic variational approach the proposed method allows to incorporate various generic region appearance models while avoiding metrication errors in the suggested framework the segmentation is performed by level set evolution yet contrarily to most existing methods here multiple regions are represented by a single non negative level set function the level set function evolution is efficiently executed through the voronoi implicit interface method for multi phase interface evolution the proposed approach is shown to obtain accurate segmentation results for various natural 2d and 3d images comparable to state of the art image segmentation algorithms

image interpretation with a conceptual graph labeling over segmented images and detection of unexpected objects the labeling of the regions of a segmented image according to a semantic representation ontology is usually associated with the notion of understanding the high combinatorial aspect of this problem can be reduced with local checking of constraints between the elements of the ontology in the classical definition of finite domain constraint satisfaction problem it is assumed that the matching problem between regions and labels is bijective unfortunately in image interpretation the matching problem is often non univocal indeed images are often over segmented one object is made up of several regions this non univocal matching between data and a conceptual graph was not possible until a decisive step was accomplished by the introduction of arc consistency with bilevel constraint fdcsp b c however this extension is only adequate for a matching corresponding to surjective functions in medical image analysis the case of non functional relations is often encountered for example when an unexpected object like a tumor appears in this case the data cannot be mapped to the conceptual graph with a classical approach in this paper we propose an extension of the fdcsp b c to solve the constraint satisfaction problem for non functional relations

efficient finite element method for grating profile reconstruction this paper concerns the reconstruction of grating profiles from scattering data the inverse problem is formulated as an optimization problem with a regularization term we devise an efficient finite element method fem and employ a quasi newton method to solve it for the direct problems the fem stiff and mass matrices are assembled once at the beginning of the numerical procedure then only minor changes are made to the mass matrix at each iteration which significantly saves the computation cost numerical examples show that the method is effective and robust

a reductive approach to hypergraph clustering an application to image segmentation in the last few years hypergraph based methods have gained considerable attention in the resolution of real world clustering problems since such a mode of representation can handle higher order relationships between elements compared to the standard graph theory the most popular and promising approach to hypergraph clustering arises from concepts in spectral hypergraph theory 53 and clustering is configured as a hypergraph cut problem where an appropriate objective function has to be optimized the spectral relaxation of this optimization problem allows to get a clustering that is close to the optimum but this approach generally suffers from its high computational demands especially in real world problems where the size of the data involved in their resolution becomes too large a natural way to overcome this limitation is to operate a reduction of the hypergraph where spectral clustering should be applied over a hypergraph of smaller size in this paper we introduce two novel hypergraph reduction algorithms that are able to maintain the hypergraph structure as accurate as possible these algorithms allowed us to design a new approach devoted to hypergraph clustering based on the multilevel paradigm that operates in three steps i hypergraph reduction ii initial spectral clustering of the reduced hypergraph and iii clustering refinement the accuracy of our hypergraph clustering framework has been demonstrated by extensive experiments with comparison to other hypergraph clustering algorithms and have been successfully applied to image segmentation for which an appropriate hypergraph based model have been designed the low running times displayed by our algorithm also demonstrates that the latter unlike the standard spectral clustering approach can handle datasets of considerable size

cluster variation method in statistical physics and probabilistic graphical models the cluster variation method cvm is a hierarchy of approximate variational techniques for discrete ising like models in equilibrium statistical mechanics improving on the mean field approximation and the bethe peierls approximation which can be regarded as the lowest level of the cvm in recent years it has been applied both in statistical physics and to inference and optimization problems formulated in terms of probabilistic graphical models the foundations of the cvm are briefly reviewed and the relations with similar techniques are discussed the main properties of the method are considered with emphasis on its exactness for particular models and on its asymptotic properties the problem of the minimization of the variational free energy which arises in the cvm is also addressed and recent results about both provably convergent and message passing algorithms are discussed

the numerical solution of scattering by infinite rough interfaces based on the integral equation method in this paper we describe a nystrom integration method for the integral operator t which is the normal derivative of the double layer potential arising in problems of two dimensional acoustic scattering by infinite rough interfaces the hypersingular kernel and unbounded integral interval of t are the key difficulties by using a mollifier we separately deal with these two difficulties and propose its nystrom integration method furthermore we establish convergence of the method finally we apply the method to the scattering problem by infinite rough interfaces and carry out some numerical experiments to show the validity

graph cuts and efficient n d image segmentation combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics this paper focusses on possibly the simplest application of graph cuts segmentation of objects in image data despite its simplicity this application epitomizes the best features of combinatorial graph cuts methods in vision global optima practical efficiency numerical robustness ability to fuse a wide range of visual cues and constraints unrestricted topological properties of segments and applicability to n d problems graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes geodesic active contours and level sets the segmentation energies optimized by graph cuts combine boundary regularization with region based properties in the same fashion as mumford shah style functionals we present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s t graph cuts after the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in boykov and jolly 2001 this idea was widely studied in computer vision and graphics communities we provide links to a large number of known extensions based on iterative parameter re estimation and learning multi scale or hierarchical approaches narrow bands and other techniques for demanding photo video and medical applications

automatic estimation and removal of noise from a single image image denoising algorithms often assume an additive white gaussian noise awgn process that is independent of the actual rgb values such approaches cannot effectively remove color noise produced by today s ccd digital camera in this paper we propose a unified framework for two tasks automatic estimation and removal of color noise from a single image using piecewise smooth image models we introduce the noise level function nlf which is a continuous function describing the noise level as a function of image brightness we then estimate an upper bound of the real nlf by fitting a lower envelope to the standard deviations of per segment image variances for denoising the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the rgb values in each segment then a gaussian conditional random field gcrf is constructed to obtain the underlying clean image from the noisy input extensive experiments are conducted to test the proposed algorithm which is shown to outperform state of the art denoising algorithms

guided image filtering in this paper we propose a novel explicit image filter called guided filter derived from a local linear model the guided filter computes the filtering output by considering the content of a guidance image which can be the input image itself or another different image the guided filter can be used as an edge preserving smoothing operator like the popular bilateral filter 1 but it has better behaviors near edges the guided filter is also a more generic concept beyond smoothing it can transfer the structures of the guidance image to the filtering output enabling new filtering applications like dehazing and guided feathering moreover the guided filter naturally has a fast and nonapproximate linear time algorithm regardless of the kernel size and the intensity range currently it is one of the fastest edge preserving filters experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including edge aware smoothing detail enhancement hdr compression image matting feathering dehazing joint upsampling etc

rigid shape matching by segmentation averaging we use segmentations to match images by shape the new matching technique does not require point to point edge correspondence and is robust to small shape variations and spatial shifts to address the unreliability of segmentations computed bottom up we give a closed form approximation to an average over all segmentations our method has many extension yielding new algorithms for tracking object detection segmentation and edge preserving smoothing for segmentation instead of a maximum a posteriori approach we compute the central segmentation minimizing the average distance to all segmentations of an image for smoothing instead of smoothing images based on local structures we smooth based on the global optimal image structures our methods for segmentation smoothing and object detection perform competitively and we also show promising results in shape based tracking

nonlocal center surround reconstruction based bottom up saliency estimation many saliency models consider the feature extraction as the algorithmic core and the performance of their methods relies on the selection of the features to a great extent however there can hardly be a set of features effective to pop out the salient regions under various visual environments moreover because saliency is not tuned to certain visual features a location winning the spatial competition in any feature space can be defined as salient instead of seeking for or learning the features to highlight the difference between the salient areas and the background we focus more on the sparsity and uniqueness carried by the original image itself the source of all the features to propose a nonlocal reconstruction based saliency model in the proposed approach the saliency is measured by the sparse reconstruction residual of representing the central patch with a linear combination of its surrounding patches sampled in a nonlocal manner in addition this is generalized to model the global aspect saliency which provides a complement to the nonlocal saliency and improves the performance further as a generalization of itti et al s classical center surround comparison scheme the proposed approach performs well on images where itti et al s method fails as well as on general natural images numerical experiments show the proposed approach produces better results compared with the state of the art algorithms on three public databases highlightsa nonlocal reconstruction based bottom up saliency estimation method is proposed nonlocal image processing is employed to provide a better treatment of textures sparsity pursuit is very effective for removing duplicated similar image regions reconstruction residual is inherently coherent with the bottom up saliency our method is stable under three public datasets and consistent with human fixations

tree based reparameterization framework for analysis of sum product and related algorithms we present a tree based reparameterization trp framework that provides a new conceptual view of a large class of algorithms for computing approximate marginals in graphs with cycles this class includes the belief propagation bp or sum product algorithm as well as variations and extensions of bp algorithms in this class can be formulated as a sequence of reparameterization updates each of which entails refactorizing a portion of the distribution corresponding to an acyclic subgraph i e a tree or more generally a hypertree the ultimate goal is to obtain an alternative but equivalent factorization using functions that represent exact or approximate marginal distributions on cliques of the graph our framework highlights an important property of the sum product algorithm and the larger class of reparameterization algorithms the original distribution on the graph with cycles is not changed the perspective of tree based updates gives rise to a simple and intuitive characterization of the fixed points in terms of tree consistency we develop interpretations of these results in terms of information geometry the invariance of the distribution in conjunction with the fixed point characterization enables us to derive an exact expression for the difference between the true marginals on an arbitrary graph with cycles and the approximations provided by belief propagation more broadly our analysis applies to any algorithm that minimizes the bethe free energy we also develop bounds on the approximation error which illuminate the conditions that govern their accuracy finally we show how the reparameterization perspective extends naturally to generalizations of bp e g kikuchi 1951 approximations and variants via the notion of hypertree reparameterization

dense subgraph partition of positive hypergraphs in this paper we present a novel partition framework called dense subgraph partition dsp to automatically precisely and efficiently decompose a positive hypergraph into dense subgraphs a positive hypergraph is a graph or hypergraph whose edges except self loops have positive weights we first define the concepts of core subgraph conditional core subgraph and disjoint partition of a conditional core subgraph then define dsp based on them the result of dsp is an ordered list of dense subgraphs with decreasing densities which uncovers all underlying clusters as well as outliers a divide and conquer algorithm called min partition evolution is proposed to efficiently compute the partition dsp has many appealing properties first it is a nonparametric partition and it reveals all meaningful clusters in a bottom up way second it has an exact and efficient solution called min partition evolution algorithm the min partition evolution algorithm is a divide and conquer algorithm thus time efficient and memory friendly and suitable for parallel processing third it is a unified partition framework for a broad range of graphs and hypergraphs we also establish its relationship with the densest k subgraph problem d k s an np hard but fundamental problem in graph theory and prove that dsp gives precise solutions to d k s for all k in a graph dependent set called critical k set to our best knowledge this is a strong result which has not been reported before moreover as our experimental results show for sparse graphs especially web graphs the size of critical k set is close to the number of vertices in the graph we test the proposed partition framework on various tasks and the experimental results clearly illustrate its advantages

vcells simple and efficient superpixels using edge weighted centroidal voronoi tessellations vcells the proposed edge weighted centroidal voronoi tessellations ewcvts based algorithm is used to generate superpixels i e an oversegmentation of an image for a wide range of images the new algorithm is capable of generating roughly uniform subregions and nicely preserving local image boundaries the undersegmentation error is effectively limited in a controllable manner moreover vcells is very efficient with core computational cost at o k nc n in which k nc and n are the number of iterations superpixels and pixels respectively extensive qualitative discussions are provided together with the high quality segmentation results of vcells on a wide range of complex images the simplicity and efficiency of our model are demonstrated by complexity analysis time and accuracy evaluations

a bottom up approach for pancreas segmentation using cascaded superpixels and deep image patch labeling robust organ segmentation is a prerequisite for computer aided diagnosis quantitative imaging analysis pathology detection and surgical assistance for organs with high anatomical variability e g the pancreas previous segmentation approaches report low accuracies compared with well studied organs such as the liver or heart we present an automated bottom up approach for pancreas segmentation in abdominal computed tomography ct scans the method generates a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading segments superpixels the system contains four steps 1 decomposition of ct slice images into a set of disjoint boundary preserving superpixels 2 computation of pancreas class probability maps via dense patch labeling 3 superpixel classification by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks and 4 simple connectivity based post processing dense image patch labeling is conducted using two methods efficient random forest classification on image histogram location and texture features and more expensive but more accurate deep convolutional neural network classification on larger image windows i e with more spatial contexts over segmented 2 d ct slices by the simple linear iterative clustering approach are adopted through model parameter calibration and labeled at the superpixel level for positive pancreas or negative non pancreas or background classes the proposed method is evaluated on a data set of 80 manually segmented ct volumes using six fold cross validation its performance equals or surpasses other state of the art methods evaluated by leave one patient out with a dice coefficient of 70 7 and jaccard index of 57 9 in addition the computational efficiency has improved significantly requiring a mere 6 8 min per testing case versus 10 h for other methods the segmentation framework using deep patch labeling confidences is also more numerically stable as reflected in the smaller performance metric standard deviations finally we implement a multi atlas label fusion malf approach for pancreas segmentation using the same data set under six fold cross validation our bottom up segmentation method significantly outperforms its malf counterpart 70 7 13 0 versus 52 51 20 84 in dice coefficients

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

the random subspace method for constructing decision forests much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes the dilemma between overfitting and achieving maximum accuracy is seldom resolved a method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity the classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector that is trees constructed in randomly chosen subspaces the subspace method is compared to single tree classifiers and other forest construction methods by experiments on publicly available datasets where the method s superiority is demonstrated we also discuss independence between trees in a forest and relate that to the combined classification accuracy