generalized drinfeld sokolov reductions and kdv type hierarchies generalized drinfeld sokolov ds hierarchies are constructed through local reductions of hamiltonian flows generated by monodromy invariants on the dual of a loop algebra following earlier work of de groot et al reductions based upon graded regular elements of arbitrary heisenberg subalgebras are considered we show that in the case of the nontwisted loop algebra l gl n graded regular elements exist only in those heisenberg subalgebras which correspond either to the partitions ofn into the sum of equal numbersn pr or to equal numbers plus onen pr 1 we prove that the reduction belonging to the grade 1 regular elements in the casen pr yields thep p matrix version of the gelfand dickeyr kdv hierarchy generalizing the scalar casep 1 considered by ds the methods of ds are utilized throughout the analysis but formulating the reduction entirely within the hamiltonian framework provided by the classical r matrix approach leads to some simplifications even forp 1

vertex operator representation of some quantum tori lie algebras we are defining the trigonometric lie subalgebras in bar x infty bar a infty bar b infty bar c infty bar d infty which are the natural generalization of the well known sin lie algebra the embedding formulas into bar x infty are introduced these algebras can be considered as some lie algebras of quantum tori an irreducible representation ofa b series of trigonometric lie algebras is constructed special cases of the trigonometric lie factor algebras which can be considered as a quantum preserving lie algebra structure deformation of the kac moody algebras are considered

colour constancy beyond the classical receptive field the problem of removing illuminant variations to preserve the colours of objects colour constancy has already been solved by the human brain using mechanisms that rely largely on centre surround computations of local contrast in this paper we adopt some of these biological solutions described by long known physiological findings into a simple fully automatic functional model termed adaptive surround modulation or asm in asm the size of a visual neuron s receptive field rf as well as the relationship with its surround varies according to the local contrast within the stimulus which in turn determines the nature of the centre surround normalisation of cortical neurons higher up in the processing chain we modelled colour constancy by means of two overlapping asymmetric gaussian kernels whose sizes are adapted based on the contrast of the surround pixels resembling the change of rf size we simulated the contrast dependent surround modulation by weighting the contribution of each gaussian according to the centre surround contrast in the end we obtained an estimation of the illuminant from the set of the most activated rfs outputs our results on three single illuminant and one multi illuminant benchmark datasets show that asm is highly competitive against the state of the art and it even outperforms learning based algorithms in one case moreover the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets that is mimicking how the human visual system operates these results suggest a dynamical adaptation mechanisms contribute to achieving higher accuracy in computational colour constancy

classical and quantum integrable systems with boundaries we study two dimensional classically integrable field theory with independent boundary conditions at each end and obtain three possible generating functions for integrals of motion when this model is an ultralocal one classically integrable boundary conditions can be found when solving boundary k equations in the quantum case we also find that the unitarity condition of the quantum r matrix is sufficient to construct commutative quantities with boundaries and its reflection equations are obtained

evaluating combinational illumination estimation methods on real world images illumination estimation is an important component of color constancy and automatic white balancing a number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature these combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions the existing combinational methods are surveyed and analyzed here with the goals of determining 1 the effectiveness of fusing illumination estimates from multiple subordinate methods 2 the best method of combination 3 the underlying factors that affect the performance of a combinational method and 4 the effectiveness of combination for illumination estimation in multiple illuminant scenes the various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high level scene content cues e g indoor versus outdoor extensive tests and enhanced analyzes using three data sets of real world images are conducted for consistency in testing the images were labeled according to their high level features 3d stages indoor outdoor and this label data is made available on line the tests reveal that the trained combinational methods direct combination by support vector regression in particular clearly outperform both the non combinational methods and those combinational methods based on scene content cues

a comparison of computational color constancy algorithms ii experiments with image data for pt i see ibid vol 11 no 9 p 972 84 2002 we test a number of the leading computational color constancy algorithms using a comprehensive set of images these were of 33 different scenes under 11 different sources representative of common illumination conditions the algorithms studied include two gray world methods a version of the retinex method several variants of forsyth s 1990 gamut mapping method cardei et al s 2000 neural net method and finlayson et al s color by correlation method finlayson et al 1997 2001 hubel and finlayson 2000 we discuss a number of issues in applying color constancy ideas to image data and study in depth the effect of different preprocessing strategies we compare the performance of the algorithms on image data with their performance on synthesized data all data used for this study are available online at http www cs sfu ca spl sim color data and implementations for most of the algorithms are also available http www cs sfu ca spl sim color code experiments with synthesized data part one of this paper suggested that the methods which emphasize the use of the input data statistics specifically color by correlation and the neural net algorithm are potentially the most effective at estimating the chromaticity of the scene illuminant unfortunately we were unable to realize comparable performance on real images here exploiting pixel intensity proved to be more beneficial than exploiting the details of image chromaticity statistics and the three dimensional 3 d gamut mapping algorithms gave the best performance

a comparison of computational color constancy algorithms i methodology and experiments with synthesized data we introduce a context for testing computational color constancy specify our approach to the implementation of a number of the leading algorithms and report the results of three experiments using synthesized data experiments using synthesized data are important because the ground truth is known possible confounds due to camera characterization and pre processing are absent and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely the algorithms chosen for close study include two gray world methods a limiting case of a version of the retinex method a number of variants of forsyth s 1990 gamut mapping method cardei et al s 2000 neural net method and finlayson et al s color by correlation method finlayson et al 1997 2001 hubel and finlayson 2000 we investigate the ability of these algorithms to make estimates of three different color constancy quantities the chromaticity of the scene illuminant the overall magnitude of that illuminant and a corrected illumination invariant image we consider algorithm performance as a function of the number of surfaces in scenes generated from reflectance spectra the relative effect on the algorithms of added specularities and the effect of subsequent clipping of the data all data is available on line at http www cs sfu ca spl sim color data and implementations for most of the algorithms are also available http www cs sfu ca spl sim color code

a note on affine toda couplings the author proves the clebsch gordan property for the couplings of an untwisted affine toda field theory utilizing dorey s 1991 conjecture

two dimensional generalized toda lattice the zero curvature representation is obtained for the two dimensional generalized toda lattices connected with semisimple lie algebras the reduction group and conservation laws are found and the mass spectrum is calculated

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

multidimensional nonlinear evolution equations and inverse scattering abstract in this paper we will review some recent work done in the field of integrable nonlinear evolution equations and inverse scattering we will concentrate on the basic underlying areas and refer interested readers to suitable references for complete details specifically background material can be found in various texts on this subject e g 1 by ablowitz and segur more recent references will be given as necessary the outline of the paper is as follows 1 1 introductory remarks 2 2 a discussion of two separate but related issues namely a solving certain nonlinear evolutions equations in infinite space and b inverse scattering these are important problems having many physical applications moreover they are related to each other by what we refer to as the inverse scattering transform ist 3 3 at the end of the paper we will make some remarks on the possibility of solving nonlinear evolution equations in high dimensions i e equations with more than two spatial and one time variable by using the ist method as we now understand it

color constancy using natural image statistics and scene semantics existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images as a consequence no algorithm can be considered as universal however with the large variety of available methods the question is how to select the method that performs best for a specific image to achieve selection and combining of color constancy algorithms in this paper natural image statistics are used to identify the most important characteristics of color images then based on these image characteristics the proper color constancy algorithm or best combination of algorithms is selected for a specific image to capture the image characteristics the weibull parameterization e g grain size and contrast is used it is shown that the weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive an mog classifier is used to learn the correlation and weighting between the weibull parameters and the image attributes number of edges amount of texture and snr the output of the classifier is the selection of the best performing color constancy method for a certain image experimental results show a large improvement over state of the art single algorithms on a data set consisting of more than 11 000 images an increase in color constancy performance up to 20 percent median angular error can be obtained compared to the best performing single algorithm further it is shown that for certain scene categories one specific color constancy algorithm can be used instead of the classifier considering several algorithms

more non abelian loop toda solitons we find new solutions including soliton like ones for a special case of non abelian loop toda equations associated with complex general linear groups we use the method of rational dressing based on an appropriate block matrix representation suggested by the gradation under consideration we present solutions in the form of a direct matrix generalization of hirota s soliton solution already well known in the case of abelian loop toda systems

a novel algorithm for color constancy color constancy is the skill by which it is possible to tell the color of an object even under a colored light i interpret the color of an object as its color under a fixed canonical light rather than as a surface reflectance function this leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible in this framework color constancy requires estimating the illuminant under which the image was taken the estimate is then used to choose one of a set of linear maps which is applied to the image to yield a color descriptor at each point this set of maps is computed in advance the illuminant can be estimated using image measurements alone because given a number of weak assumptions detailed in the text the color of the illuminant is constrained by the colors observed in the image this constraint arises from the fact that surfaces can reflect no more light than is cast on them for example if one observes a patch that excites the red receptor strongly the illuminant cannot have been deep blue two algorithms are possible using this constraint corresponding to different assumptions about the world the first algorithm crule will work for any surface reflectance crule corresponds to a form of coefficient rule but obtains the coefficients by using constraints on illuminant color the set of illuminants for which crule will be successful depends strongly on the choice of photoreceptors for narrowband photoreceptors crule will work in an unrestricted world the second algorithm mwext requires that both surface reflectances and illuminants be chosen from finite dimensional spaces but under these restrictive conditions it can recover a large number of parameters in the illuminant and is not an attractive model of human color constancy crule has been tested on real images of mondriaans and works well i show results for crule and for the retinex algorithm of land land 1971 land 1983 land 1985 operating on a number of real images the experimental work shows that for good constancy a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans

kac moody groups and integrability of soliton equations a new approach to integrability of affine toda field theories and closely related to them kdv hierarchies is proposed the flows of a hierarchy are explicity identified with infinitesimal action of the principal abelian subalgebra of the corresponding affine kac moody algebra on a homogeneous space

possible generalization of boltzmann gibbs statistics with the use of a quantity normally scaled in multifractals a generalized form is postulated for entropy namelys q k 1 i 1 w p i q q 1 whereq characterizes the generalization andp i are the probabilities associated withw microscopic configurations w the main properties associated with this entropy are established particularly those corresponding to the microcanonical and canonical ensembles the boltzmann gibbs statistics is recovered as theq 1 limit

color based object recognition abstract the purpose is to arrive at recognition of multicolored objects invariant to a substantial change in viewpoint object geometry and illumination assuming dichromatic reflectance and white illumination it is shown that normalized color rgb saturation s and hue h and the newly proposed color models c 1 c 2 c 3 and l 1 l 2 l 3 are all invariant to a change in viewing direction object geometry and illumination further it is shown that hue h and l1l2l3 are also invariant to highlights finally a change in spectral power distribution of the illumination is considered to propose a new color constant color model m1m2m3 to evaluate the recognition accuracy differentiated for the various color models experiments have been carried out on a database consisting of 500 images taken from 3 d multicolored man made objects the experimental results show that highest object recognition accuracy is achieved by l1l2l3 and hue h followed by c1c2c3 normalized color rgb and m1m2m3 under the constraint of white illumination also it is demonstrated that recognition accuracy degrades substantially for all color features other than m1m2m3 with a change in illumination color the recognition scheme and images are available within the pictoseek and pic2seek systems on line at http www wins uva nl research isis zomax

cumulative attribute space regression for head pose estimation and color constancy abstract two stage cumulative attribute ca regression has been found effective in regression problems of computer vision such as facial age and crowd density estimation the first stage regression maps input features to cumulative attributes that encode correlations between target values the previous works have dealt with single output regression in this work we propose cumulative attribute spaces for 2 and 3 output multivariate regression we show how the original ca space can be generalized to multiple output by the cartesian product cartca however for target spaces with more than two outputs the cartca becomes computationally infeasible and therefore we propose an approximate solution multi view ca mvca where cartca is applied to output pairs we experimentally verify improved performance of the cartca and mvca spaces in 2d and 3d face pose estimation and three output rgb illuminant estimation for color constancy

local conservation laws for the two dimensional periodic su n 1 toda lattices an infinite number of local conservation laws are derived for the two dimensional periodic su n 1 toda lattice equations following the well known method for the sine gordon equation

schrodinger equation from an exact uncertainty principle an exact uncertainty principle formulated as the assumption that a classical ensemble is subject to random momentum fluctuations of a strength which is determined by and scales inversely with uncertainty in position leads from the classical equations of motion to the schrodinger equation

on the squared eigenfunction symmetry of the toda lattice hierarchy the squared eigenfunction symmetry for the toda lattice hierarchy is explicitly constructed in the form of the kronecker product of the vector eigenfunction and the vector adjoint eigenfunction which can be viewed as the generating function for the additional symmetries when the eigenfunction and the adjoint eigenfunction are the wave function and the adjoint wave function respectively then after the fay like identities and some important relations about the wave functions are investigated the action of the squared eigenfunction related to the additional symmetry on the tau function is derived which is equivalent to the adler shiota van moerbeke formulas

multi cue illumination estimation via a tree structured group joint sparse representation a multi cue illumination estimation method based on tree structured group joint sparse representation is proposed tests show that the proposed method works better than existing methods most of which are based on using only a single cue type for example a binarized color histogram or simple image statistic such as the mean rgb most existing illumination estimation methods make their estimates using only one of three kinds of cues they differ in which cue type they use but the chosen cue is either based on 1 properties of the low level rgb color distribution 2 mid level initial illuminant estimates provided by subordinate methods or 3 high level knowledge of scene content e g indoor versus outdoor scene the proposed multi cue method combines the information provided by cues of all three of these types within the framework of a tree structured group joint sparse representation tgjsr in tgjsr the training data is grouped into a tree of subgroups a test image under an unknown illuminant has its features reconstructed in terms of a joint sparse representation model derived from the grouped training data the test image s illumination is then estimated based on the weights involved in the joint sparse representation model as a general framework the proposed tgjsr framework can also easily be extended to incorporate any new features or cues that might be discovered in the future for illumination estimation

linearizing integral transform for the multicomponent lattice kp abstract we present a generalization of the direct linearization method for the kadomtsev petviashvili kp equation and its lattice analogues such as the two dimensional toda equation the generalization consists of a riemann hilbert type of integral transform which relates solutions of the associated spectral problem to any given solution of the spectral problem by dimensional reduction we obtain the integral transform for the korteweg de vries kdv equation and its lattice analogues the integral transform is well suited for the investigation of the finite matrix generalizations of the above mentioned equations such finite matrix generalizations may be of use in connection with partial difference equations for spin correlation functions in the two dimensional ising model

toda lattice mass transport in lagrangian mechanics and in a two dimensional system abstract this paper explores the connection between the hydrodynamic mass transport description and the thermodynamic description for a nonlinear range of the toda lattices particular attention is paid to the broken isotropy in the kdv and burgers equations the flow variable representation is established from the lagrangian mechanics for hydrodynamic mass transport based on the inverse scattering transform the gel fand levitan marchenko glm equation is formulated from the kdv equation expressed by the flow variable representation we found that a kernel of the glm equation is given by the concentration variable q x t a lagrangian is formulated for the kdv equation in state space q x t k x t next an extension of the flow variable representation is sought in a two dimensional system the lhs of the kadomtsev petviashvili kp equation takes the same form as in the second formalism of the kdv equation by setting up the flow variable representation of the kp equation the burgers equation in two dimensions is formulated these results contribute to an understanding of the broken isotropy for the nonlinear mass transport equations these results provide physical insight into various consequences of the generalized form of the kawasaki ohta equation from the viewpoint of mass transport

non extensive statistical mechanics and generalized fokker planck equation a non linear generalized fokker planck gfp equation is studied whose exact stationary solutions are the maximum entropy distributions introduced by tsallis in his generalization of statistical mechanics in the case of a constant or linearly varying drift the time dependent solutions of the gfp equation are seen to obey a suitable form of the celebrated h theorem the temporal changes of tsallis entropies are seen to be given in terms of the fisher s information measure particular time dependent solutions of the gfp equation of the maximum tsallis entropy form are obtained

miura map and bi hamiltonian formulation for restricted flows of the kdv hierarchy the first hamiltonian structure and the miura map are constructed for the restricted flows of korteweg de vries kdv hierarchy and its modification on this basis the second hamiltonian structure of kdv restricted flows is derived

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

a six stimulus theory for stochastic texture we report a six stimulus basis for stochastic texture perception fragmentation of the scene by a chaotic process causes the spatial scene statistics to conform to a weibull distribution the parameters of the weibull distribution characterize the spatial structure of uniform stochastic textures of many different origins completely in this paper we report the perceptual significance of the weibull parameters we demonstrate the parameters to be sensitive to orthogonal variations in the imaging conditions specifically to the illumination conditions camera magnification and resolving power and the texture orientation apparently the weibull parameters form a six stimulus basis for stochastic texture description the results indicate that texture perception can be approached like the experimental science of colorimetry

the determinant representation for quantum correlation functions of the sinh gordon model we consider the quantum sinh gordon model in this paper form factors in this model were calculated by mussardo and colleagues we sum up all contributions of form factors and obtain a closed expression for a correlation function this expression is a determinant of an integral operator similar determinant representations have been proven to be useful not only in the theory of correlation functions but also in matrix models

color constancy using 3d scene geometry derived from a single image the aim of color constancy is to remove the effect of the color of the light source as color constancy is inherently an ill posed problem most of the existing color constancy algorithms are based on specific imaging assumptions e g gray world and white patch assumption in this paper 3d geometry models are used to determine which color constancy method to use for the different geometrical regions depth layer found in images the aim is to classify images into stages rough 3d geometry models according to stage models images are divided into stage regions using hard and soft segmentation after that the best color constancy methods are selected for each geometry depth to this end we propose a method to combine color constancy algorithms by investigating the relation between depth local image statistics and color constancy image statistics are then exploited per depth to select the proper color constancy method our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations experiments on state of the art data sets show that the proposed algorithm outperforms state of the art single color constancy algorithms with an improvement of almost 50 of median angular error when using a perfect classifier i e all of the test images are correctly classified into stages the performance of the proposed method achieves an improvement of 52 of the median angular error compared with the best performing single color constancy algorithm

mass transport theory for the toda lattices dispersive and dissipative abstract to establish mass transport theory on nonlinear lattices we formulate the korteweg devries kdv equation and the burgers equation using the flow variable representation so as to facilitate comparison with the boltzmann equation and with the cahn hilliard equation in classical statistical mechanics we also study toda lattice microdynamics using the flaschka representation and compare with the liouville equation like the linear diffusion equation the boltzmann equation and the liouville equation are to be solved for a distribution function which is intrinsically probabilistic transport theory in linear systems is governed by the isotropic motions of the kinetic equations in contrast the kdv perturbation equation derived from the toda lattice microdynamics expresses hydrodynamic mass transport the kdv equation in hydrodynamics and the burgers equation in thermodynamics do not involve a probability distribution function the nonlinear lattices do not retain isotropy of the mass transport equations in consequence it is proposed that in the presence of hydrodynamic flows to the left kdv wave propagation proceeds to the right this basic property of the kdv system is extended to thermodynamics in the burgers system these features arise because linear systems are driven towards an equilibrium by molecular collisions whereas the inhomogeneities of the nonlinear lattices are generated by the potential energy of interaction diffusion as expressed by the burgers equation is governed not only by a chemical potential but also by the toda lattice potential energy

a chemical response theory of the toda lattice in an external mechanical perturbation abstract in the preceding paper using the so called flow variable representation we reported the formulation of the korteweg devries kdv and the burgers equations to express mass transports the transport theories were constructed by pertaining to correspondence with the toda lattices our present purpose is to understand a connection between these nonlinear wave equations formulated independently from the generalized form of the kawasaki ohta equation for this purpose we formulate the burgers equation from the kdv in correspondence to a transition from the dispersive toda lattice to a dissipative model for this formulation we employ an external mechanical perturbation method the paper is concerned with a variation method as to how to prepare a perturbation lax bracket for the burgers formulation variations in one of the lax operators are assumed to be 1 as an obstruction operator against the convection flows expressed by in the repulsive potential field we conclude that the obstruction is caused by attractive interactions the main point is that the burgers equation is transformed to a nonlinear diffusion equation by the hopf cole transform the variation method and the argumentation for the burgers formulation are proved by this transformation

deformed logarithms and entropies by solving a differential functional equation inposed by the maxent principle we obtain a class of two parameter deformed logarithms and construct the corresponding two parameter generalized trace form entropies generalized distributions follow from these generalized entropies in the same fashion as the gaussian distribution follows from the shannon entropy which is a special limiting case of the family we determine the region of parameters where the deformed logarithm conserves the most important properties of the logarithm and show that important existing generalizations of the entropy are included as special cases in this two parameter class

weakly non local fluid mechanics the schrodinger equation a weakly non local extension of ideal fluid dynamics is derived from the second law of thermodynamics it is proved that in the reversible limit the additional pressure term can be derived from a potential the requirement of the additivity of the specific entropy function determines the quantum potential uniquely the relation to other known derivations of the schrodinger equation stochastic fisher information exact uncertainty is clarified

non linear kinetics underlying generalized statistics abstract the purpose of the present effort is threefold firstly it is shown that there exists a principle that we call kinetical interaction principle kip underlying the non linear kinetics in particle systems independently on the picture kramers boltzmann used to describe their time evolution secondly the kip imposes the form of the generalized entropy associated to the system and permits to obtain the particle statistical distribution both as stationary solution of the non linear evolution equation and as the state which maximizes the generalized entropy thirdly the kip allows on one hand to treat all the classical or quantum statistical distributions already known in the literature in a unifying scheme and on the other hand suggests how we can introduce naturally new distributions finally as a working example of the approach to the non linear kinetics here presented a new non extensive statistics is constructed and studied starting from a one parameter deformation of the exponential function holding the relation f x f x 1

nonlinear schrodinger equations and simple lie algebras we associate a system of integrable generalised nonlinear schrodinger nls equations with each hermitian symmetric space these nls equations are considered as reductions of more general systems this time associated with a reductive homogeneous space the nonlinear terms are related to the curvature and torsion tensors of the appropriate geometrical space the hamiltonian structure is investigated using r matrix techniques and shown to be canonical for all these equations throughout the reduction procedure this hamiltonian structure does not degenerate each of the above systems of equations is gauge equivalent to a generalised ferromagnet reductions of the latter are discussed in terms of the corresponding nls type equations

approaching the computational color constancy as a classification problem through deep learning abstract computational color constancy refers to the problem of computing the illuminant color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination in this paper we adopt a deep learning framework for the illumination estimation problem the proposed method works under the assumption of uniform illumination over the scene and aims for the accurate illuminant color computation specifically we trained the convolutional neural network to solve the problem by casting the color constancy problem as an illumination classification problem we designed the deep learning architecture so that the output of the network can be directly used for computing the color of the illumination experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets

color by correlation a simple unifying framework for color constancy the paper considers the problem of illuminant estimation how given an image of a scene recorded under an unknown light we can recover an estimate of that light obtaining such an estimate is a central part of solving the color constancy problem thus the work presented will have applications in fields such as color based object recognition and digital photography rather than attempting to recover a single estimate of the illuminant we instead set out to recover a measure of the likelihood that each of a set of possible illuminants was the scene illuminant we begin by determining which image colors can occur and how these colors are distributed under each of a set of possible lights we discuss how for a given camera we can obtain this knowledge we then correlate this information with the colors in a particular image to obtain a measure of the likelihood that each of the possible lights was the scene illuminant finally we use this likelihood information to choose a single light as an estimate of the scene illuminant computation is expressed and performed in a generic correlation framework which we develop we propose a new probabilistic instantiation of this correlation framework and show that it delivers very good color constancy on both synthetic and real images we further show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it the gray world and gamut mapping algorithms are presented in this framework and we also explore the relationship of these algorithms to other probabilistic and neural network approaches to color constancy

edge based color constancy color constancy is the ability to measure colors of objects independent of the color of the light source a well known color constancy method is based on the gray world assumption which assumes that the average reflectance of surfaces in the world is achromatic in this paper we propose a new hypothesis for color constancy namely the gray edge hypothesis which assumes that the average edge difference in a scene is achromatic based on this hypothesis we propose an algorithm for color constancy contrary to existing color constancy algorithms which are computed from the zero order structure of images our method is based on the derivative structure of images furthermore we propose a framework which unifies a variety of known gray world max rgb minkowski norm and the newly proposed gray edge and higher order gray edge algorithms the quality of the various instantiations of the framework is tested and compared to the state of the art color constancy methods on two large data sets of images recording objects under a large number of different light sources the experiments show that the proposed color constancy algorithms obtain comparable results as the state of the art color constancy methods with the merit of being computationally more efficient

lat local area transform for cross modal correspondence matching abstract establishing correspondences is a fundamental task in many image processing and computer vision applications in particular finding the correspondences between a non linearly deformed image pair induced by different modality conditions is a challenging problem this paper describes a simple but powerful image transform called local area transform lat for modality robust correspondence estimation specifically lat transforms an image from the intensity domain to the local area domain which is invariant under nonlinear intensity deformations especially radiometric photometric and spectral deformations experimental results show that latransformed images provide a consistency for nonlinearly deformed images even under random intensity deformations lat reduces the mean absolute difference by approximately 0 20 and the different pixel ratio by approximately 58 on average as compared to conventional methods furthermore the reformulation of descriptors with lat shows superiority to conventional methods which is a promising result for the tasks of cross spectral and modality correspondence matching lat gains an approximately 23 improvement in the correct detection ratio and a 10 improvement in the recognition rate for the tasks of rgb nir cross spectral template matching and cross spectral feature matching respectively lat reduces the bad pixel percentage by approximately 15 and the root mean squared errors by 13 5 in the task of cross radiation stereo matching lat also improves the cross modal dense flow estimation task in terms of warping error providing 50 error reduction

unique additive information measures boltzmann gibbs shannon fisher and beyond abstract it is proved that the only additive and isotropic information measure that can depend on the probability distribution and also on its first derivative is a linear combination of the boltzmann gibbs shannon and fisher information measures power law equilibrium distributions are found as a result of the interaction of the two terms the case of second order derivative dependence is investigated and a corresponding additive information measure is given