color texture analysis based on fractal descriptors color texture classification is an important step in image segmentation and recognition the color information is especially important in textures of natural scenes such as leaves surfaces terrains models etc in this paper we propose a novel approach based on the fractal dimension for color texture analysis the proposed approach investigates the complexity in r g and b color channels to characterize a texture sample we also propose to study all channels in combination taking into consideration the correlations between them both these approaches use the volumetric version of the bouligand minkowski fractal dimension method the results show a advantage of the proposed method over other color texture analysis methods

polynomial modeling for time varying systems based on a particle swarm optimization algorithm in this paper an effective particle swarm optimization pso is proposed for polynomial models for time varying systems the basic operations of the proposed pso are similar to those of the classical pso except that elements of particles represent arithmetic operations and variables of time varying models the performance of the proposed pso is evaluated by polynomial modeling based on various sets of time invariant and time varying data results of polynomial modeling in time varying systems show that the proposed pso outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming gp and dynamic gp an analysis of the diversity of individuals of populations in the proposed pso and gp reveals why the proposed pso obtains better results than those obtained by gp

robust rotation invariant texture classification using a model based approach in this paper a model based texture classification procedure is presented the texture is modeled as the output of a linear system driven by a binary image this latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function acf we show that features extracted from the acf of the binary excitation suffice to represent the texture for classification purposes specifically we employ a moment invariants based technique to classify the acf the resulting proposed classification procedure is thus inherently rotation invariant moreover it is robust with respect to additive noise experimental results show that this approach allows obtaining high correct rotation invariant classification rates while containing the size of the feature space

color texture classification by integrative co occurrence matrices abstract integrative co occurrence matrices are introduced as novel features for color texture classification the extended co occurrence notation allows the comparison between integrative and parallel color texture concepts the information profit of the new matrices is shown quantitatively using the kolmogorov distance and by extensive classification experiments on two datasets applying them to the rgb and the luv color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated the results are compared with two baselines gray scale texture analysis and color histogram analysis the novel features improve the classification results up to 20 and 32 for the first and second baseline respectively

improved delay dependent bounded real lemma for uncertain time delay systems this paper concerns reducing the conservatism of delay dependent bounded real lemma brl of time delay systems with uncertainty described by a linear fractional form by introducing an augmented lyapunov functional an improved brl is established in terms of linear matrix inequalities lmis theoretical comparisons show that the obtained brl is less conservative than some previous ones and the recent results in s y xu l james y zou new results on delay dependent robust h control for systems with time varying delays automatica 42 2006 343 348 t li l guo c lin y m zhang delay range dependent bounded real lemma for time delay systems asian journal of control 10 2008 708 717 are actually equivalent to the special cases of the obtained brl numerical examples are also given to demonstrate the effectiveness of the proposed result

do we need hundreds of classifiers to solve real world classification problems we evaluate 179 classifiers arising from 17 families discriminant analysis bayesian neural networks support vector machines decision trees rule based classifiers boosting bagging stacking random forests and other ensembles generalized linear models nearest neighbors partial least squares and principal component regression logistic and multinomial regression multiple adaptive regression splines and other methods implemented in weka r with and without the caret package c and matlab including all the relevant classifiers available today we use 121 data sets which represent the whole uci data base excluding the large scale problems and other own real problems in order to achieve significant conclusions about the classifier behavior not dependent on the data set collection the classifiers most likely to be the bests are the random forest rf versions the best of which implemented in r and accessed via caret achieves 94 1 of the maximum accuracy overcoming 90 in the 84 3 of the data sets however the difference is not statistically significant with the second best the svm with gaussian kernel implemented in c using libsvm which achieves 92 3 of the maximum accuracy a few models are clearly better than the remaining ones random forest svm with gaussian and polynomial kernels extreme learning machine with gaussian kernel c5 0 and avnnet a committee of multi layer perceptrons implemented in r with the caret package the random forest is clearly the best family of classifiers 3 out of 5 bests classifiers are rf followed by svm 4 classifiers in the top 10 neural networks and boosting ensembles 5 and 3 members in the top 20 respectively

illuminant and device invariant colour using histogram equalisation colour can potentially provide useful information for a variety of computer vision tasks such as image segmentation image retrieval object recognition and tracking however for it to be helpful in practice colour must relate directly to the intrinsic properties of the imaged objects and be independent of imaging conditions such as scene illumination and the imaging device to this end many invariant colour representations have been proposed in the literature unfortunately recent work second workshop on content based multimedia indexing has shown that none of them provides good enough practical performance in this paper we propose a new colour invariant image representation based on an existing grey scale image enhancement technique histogram equalisation we show that provided the rank ordering of sensor responses are preserved across a change in imaging conditions lighting or device a histogram equalisation of each channel of a colour image renders it invariant to these conditions we set out theoretical conditions under which rank ordering of sensor responses is preserved and we present empirical evidence which demonstrates that rank ordering is maintained in practice for a wide range of illuminants and imaging devices finally we apply the method to an image indexing application and show that the method out performs all previous invariant representations giving close to perfect illumination invariance and very good performance across a change in device

a statistical approach to texture classification from single images we investigate texture classification from single images obtained under unknown viewpoint and illumination a statistical approach is developed where textures are modelled by the joint probability distribution of filter responses this distribution is represented by the frequency histogram of filter response cluster centres textons recognition proceeds from single uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional classification performance is compared with the filter banks and methods of leung and malik ijcv 2001 schmid cvpr 2001 and cula and dana ijcv 2004 and it is demonstrated that superior performance is achieved here classification results are presented for all 61 materials in the columbia utrecht texture database we also discuss the effects of various parameters on our classification algorithm such as the choice of filter bank and rotational invariance the size of the texton dictionary as well as the number of training images used finally we present a method of reliably measuring relative orientation co occurrence statistics in a rotationally invariant manner and discuss whether incorporating such information can enhance the classifier s performance

path planning for multiple mobile robots under double warehouse abstract this work investigates the conflict free path planning problems for efficient guidance of multiple mobile robots under the dynamic double warehouse environment a challenging problem that appears recurrently in a wide range of applications such as the service robots moving in a multistory building united with two symmetrical transfer elevators double warehouse consists of two parallel warehouses within each warehouse the polynomial based paths are subject to constraints such as motion boundaries kinematics constraints obstacle avoidance limited resource of elevators and smoothness we formulate the shortest path planning problems as one time varying nonlinear programming problem tnlpp while restricted to the above constraints and apply the multi phase strategy to reduce their difficulty we present the new variant algorithms of pso named constriction factor and random perturb pso con per pso and the simulating annealing pso sa pso to achieve the solution numerical simulations verify that our approach can fulfill multiple mobile robots path planning problems under double warehouse successfully

choice of a pertinent color space for color texture characterization using parametric spectral analysis this article presents a comparison of different color spaces including rgb ihls and email protected a b for color texture characterization this comparison is based on the fusion of the independent spatial structure and color feature cues in ihls and l a b two channel complex color images are created from the luminance and the chrominance values for such images two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done this comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations three dimensional histograms are used as color feature cues then to classify color textures kullback leibler divergence based symmetric distance measures are calculated for pure color luminance structure and chrominance structure feature cues individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets the proposed color texture classification method performs better than the state of the art methods in certain cases the l a b color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets experimental results on pixel classification of color textures are also presented and discussed

homogenization of fully overdamped frenkel kontorova models in this paper we consider the fully overdamped frenkel kontorova model this is an infinite system of coupled first order odes each ode represents the microscopic evolution of one particle interacting with its neighbors and submitted to a fixed periodic potential after a proper rescaling a macroscopic model describing the evolution of densities of particles is obtained we get this homogenization result for a general class of frenkel kontorova models the proof is based on the construction of suitable hull functions in the framework of viscosity solutions

gaussian mrf rotation invariant features for image classification features based on markov random field mrf models are sensitive to texture rotation this paper develops an anisotropic circular gaussian mrf acgmrf model for retrieving rotation invariant texture features to overcome the singularity problem of the least squares estimate method an approximate least squares estimate method is designed and implemented rotation invariant features are obtained from the acgmrf model parameters using the discrete fourier transform the acgmrf model is demonstrated to be a statistical improvement over three published methods the three methods include a laplacian pyramid an isotropic circular gmrf icgmrf and gray level cooccurrence probability features

inference of differential equation models by genetic programming this paper describes an evolutionary method for identifying a causal model from the observed time series data we use a system of ordinary differential equations odes as the causal model this approach is known to be useful for practical applications e g bioinformatics chemical reaction models control theory etc to explore the search space more effectively in the course of evolution the right hand sides of odes are inferred by genetic programming gp and the least mean square lms method is used along with the ordinary gp we apply our method to several target tasks and empirically show how successfully gp infers the systems of odes we also describe an extension of the approach to the inference of differential equation systems with transcendental functions

gp coach genetic programming based learning of compact and accurate fuzzy rule based classification systems for high dimensional problems in this paper we propose gp coach a genetic programming based method for the learning of compact and accurate fuzzy rule based classification systems for high dimensional problems gp coach learns disjunctive normal form rules generated by means of a context free grammar coded as one rule per tree the population constitutes the rule base so it is a genetic cooperative competitive learning approach gp coach uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules the results obtained have been validated by the use of non parametric statistical tests showing a good performance in terms of accuracy and interpretability

texton theory revisited a bag of words approach to combine textons the aim of this paper is to revisit an old theory of texture perception and update its computational implementation by extending it to colour with this in mind we try to capture the optimality of perceptual systems this is achieved in the proposed approach by sharing well known early stages of the visual processes and extracting low dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages we propose several descriptors in a bag of words framework that are derived from different quantisation models on to the feature spaces our perceptual features are directly given by the shape and colour attributes of image blobs which are the textons in this way we avoid learning visual words and directly build the vocabularies on these low dimensional texton spaces main differences between proposed descriptors rely on how co occurrence of blob attributes is represented in the vocabularies our approach overcomes current state of art in colour texture description which is proved in several experiments on large texture datasets

a global local optimization approach to parameter estimation of rbf type models we present a hybrid global local optimization algorithm for parameter estimation of radial basis function rbf networks and the rbf type autoregressive models without exogenous inputs rbf ar or with exogenous inputs rbf arx the rbf ar x models are quasi linear time varying ar x models with gaussian rbf network style coefficients which have been used to effectively model the nonlinear behavior of various complex systems however the identification of these models is a difficult optimization problem because of the large number of local minima a hybrid approach is proposed in this paper to achieve better optimization results for these rbf type models the applied hybrid search strategy ea snpom is developed by combining an evolutionary algorithm ea with a gradient based algorithm known as the structured nonlinear parameter optimization method snpom this strategy makes use of the robustness of the ea to cover an entire global search space and the efficiency of the gradient search to converge to a local optimum several examples of time series modeling and system identification are presented the simulation results indicate that the performance of the proposed hybrid approach is better than the performance obtained from using each method ea or snpom alone furthermore the rbf ar x models estimated by the ea snpom achieve much better modeling accuracy relative to other neural networks or fuzzy models in the simulations

influence of normalization and color space to color texture classification abstract color texture classification has recently attracted significant attention due to its multiple applications the color texture images depend on the texture surface and its albedo the illumination the camera and its viewing position a key problem to get an acceptable performance is the ambient illumination which can vary the perceived structures in the surface given a color texture classification problem it would be desirable to know which is the best approach to solve the problem making the minimal assumptions about the illumination conditions the present work does an exhaustive evaluation of the state of the art color texture classification methods considering 5 different color spaces 12 normalization methods to achieve illumination invariances 19 texture feature vectors and 23 pure color feature vectors our experiments allow to conclude that parallel approaches are better than integrative approaches for color texture classification achieving the first positions in the friedman ranking multiresolution local binary patterns mlbp are the best intensity texture features followed by wavelet and gabor filters combined with luminance chrominance color spaces lab and lab2000hl and for pure color classification the best are first order statistics fos calculated in rgb color space for intensity texture features the learning methods work better on the four smallest datasets although they could not be tested in other four bigger datasets due to its huge computational cost nor with color texture classification normalization and color spaces slightly increase the average accuracy of color texture classification although the differences achieved using normalization are not statistically significant in a paired t test lab2000hl and rgb are the best color spaces but the former is the slowest one regarding elapsed time the best vector features mlbp for intensity texture daub4 daubechies filters using mean and variance statistics for color texture and fos for pure color are nearly the fastest or are in the middle interval of all the tested methods

memetic multiobjective particle swarm optimization based radial basis function network for classification problems this paper presents a new multiobjective evolutionary algorithm applied to a radial basis function rbf network design based on multiobjective particle swarm optimization augmented with local search features the algorithm is named the memetic multiobjective particle swarm optimization rbf network mpson because it integrates the accuracy and structure of an rbf network the proposed algorithm is implemented on two class and multiclass pattern classification problems with one complex real problem the experimental results indicate that the proposed algorithm is viable and provides an effective means to design multiobjective rbf networks with good generalization capability and compact network structure the accuracy and complexity of the network obtained by the proposed algorithm are compared with the memetic non dominated sorting genetic algorithm based rbf network mgan through statistical tests this study shows that mpson generates rbf networks coming with an appropriate balance between accuracy and simplicity outperforming the other algorithms considered

texture classification by wavelet packet signatures this correspondence introduces a new approach to characterize textures at multiple scales the performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty five natural textures both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations where each wavelet packet channel reflected a specific scale and orientation sensitivity wavelet packet representations for twenty five natural textures were classified without error by a simple two layer network classifier an analyzing function of large regularity d sub 20 was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments d sub 6 in addition energy representations computed from the standard wavelet decomposition alone 17 features provided classification without error for the twenty five textures included in our study the reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation classification and subtle discrimination of texture

fractal dimension of color fractal images fractal dimension is a very useful metric for the analysis of the images with self similar content such as textures for its computation there exist several approaches the probabilistic algorithm being accepted as the most elegant approach however all the existing methods are defined for 1 d signals or binary images with extension to grayscale images our purpose is to propose a color version of the probabilistic algorithm for the computation of the fractal dimension to validate this new approach we also propose an extension of the existing algorithm for the generation of probabilistic fractals in order to obtain color fractal images then we show the results of our experiments and conclude this paper

a pde formalization of retinex theory in 1964 edwin h land formulated the retinex theory the first attempt to simulate and explain how the human visual system perceives color his theory and an extension the reset retinex were further formalized by land and mccann several retinex algorithms have been developed ever since these color constancy algorithms modify the rgb values at each pixel to give an estimate of the color sensation without a priori information on the illumination unfortunately the retinex land mccann original algorithm is both complex and not fully specified indeed this algorithm computes at each pixel an average of a very large set of paths on the image for this reason retinex has received several interpretations and implementations which among other aims attempt to tune down its excessive complexity in this paper it is proved that if the paths are assumed to be symmetric random walks the retinex solutions satisfy a discrete poisson equation this formalization yields an exact and fast implementation using only two ffts several experiments on color images illustrate the effectiveness of the retinex original theory

orthogonal least squares methods and their application to non linear system identification abstract identification algorithms based on the well known linear least squares methods of gaussian elimination cholesky decomposition classical gram schmidt modified gram schmidt householder transformation givens method and singular value decomposition are reviewed the classical gram schmidt modified gram schmidt and householder transformation algorithms are then extended to combine structure determination or which terms to include in the model and parameter estimation in a very simple and efficient manner for a class of multivariate discrete time non linear stochastic systems which are linear in the parameters

viewpoint invariant texture description using fractal analysis image texture provides a rich visual description of the surfaces in the scene many texture signatures based on various statistical descriptions and various local measurements have been developed existing signatures in general are not invariant to 3d geometric transformations which is a serious limitation for many applications in this paper we introduce a new texture signature called the multifractal spectrum mfs the mfs is invariant under the bi lipschitz map which includes view point changes and non rigid deformations of the texture surface as well as local affine illumination changes it provides an efficient framework combining global spatial invariance and local robust measurements intuitively the mfs could be viewed as a better histogram with greater robustness to various environmental changes and the advantage of capturing some geometrical distribution information encoded in the texture experiments demonstrate that the mfs codes the essential structure of textures with very low dimension and thus represents an useful tool for texture classification

feature based image patch approximation for lung tissue classification in this paper we propose a new classification method for five categories of lung tissues in high resolution computed tomography hrct images with feature based image patch approximation we design two new feature descriptors for higher feature descriptiveness namely the rotation invariant gabor local binary patterns rglbp texture descriptor and multi coordinate histogram of oriented gradients mchog gradient descriptor together with intensity features each image patch is then labeled based on its feature approximation from reference image patches and a new patch adaptive sparse approximation pasa method is designed with the following main components minimum discrepancy criteria for sparse based classification patch specific adaptation for discriminative approximation and feature space weighting for distance computation the patch wise labelings are then accumulated as probabilistic estimations for region level classification the proposed method is evaluated on a publicly available ild database showing encouraging performance improvements over the state of the arts

completed local binary count for rotation invariant texture classification in this brief a novel local descriptor named local binary count lbc is proposed for rotation invariant texture classification the proposed lbc can extract the local binary grayscale difference information and totally abandon the local binary structural information although the lbc codes do not represent visual microstructure the statistics of lbc features can represent the local texture effectively in addition a completed lbc clbc is also proposed to enhance the performance of texture classification experimental results obtained from three databases demonstrate that the proposed clbc can achieve comparable accurate classification rates with completed local binary pattern

filtering for texture classification a comparative study in this paper we review most major filtering approaches to texture feature extraction and perform a comparative study filtering approaches included are laws masks 1980 ring wedge filters dyadic gabor filter banks wavelet transforms wavelet packets and wavelet frames quadrature mirror filters discrete cosine transform eigenfilters optimized gabor filters linear predictors and optimized finite impulse response filters the features are computed as the local energy of the filter responses the effect of the filtering is highlighted keeping the local energy function and the classification algorithm identical for most approaches for reference comparisons with two classical nonfiltering approaches co occurrence statistical and autoregressive model based features are given we present a ranking of the tested approaches based on extensive experiments

time series forecasting using a system of ordinary differential equations this paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations odes to predict the small time scale traffic measurements data we used the tree structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization pso algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations we also illustrate some experimental comparisons with genetic programming gene expression programming and a feedforward neural network optimized using pso algorithm experimental results reveal that the proposed method is feasible and efficient for forecasting the small scale traffic measurements data

a discrete particle swarm optimization for lot streaming flowshop scheduling problem we consider an n job m machine lot streaming problem in a flowshop with equal size sublots where the objective is to minimize the total weighted earliness and tardiness to solve this problem we first propose a so called net benefit of movement nbm algorithm which is much more efficient than the existing linear programming model for obtaining the optimal starting and completion times of sublots for a given job sequence a new discrete particle swarm optimization dpso algorithm incorporating the nbm algorithm is then developed to search for the best sequence the new dpso improves the existing dpso by introducing an inheritance scheme inspired by a genetic algorithm into particles construction to verify the proposed dpso algorithm comparisons with the existing dpso algorithm and a hybrid genetic algorithm hga are made computational results show that the proposed dpso algorithm with a two point inheritance scheme is very competitive for the lot streaming flowshop scheduling problem

image region description using orthogonal combination of local binary patterns enhanced with color information visual content description is a key issue for machine based image analysis and understanding a good visual descriptor should be both discriminative and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations in this paper we propose a new operator called the orthogonal combination of local binary patterns denoted as oc lbp and six new local descriptors based on oc lbp enhanced with color information for image region description the aim is to increase both discriminative power and photometric invariance properties of the original lbp operator while keeping its computational efficiency the experiments in three different applications show that the proposed descriptors outperform the popular sift cs lbp hog and surf and achieve comparable or even better performances than the state of the art color sift descriptors meanwhile the proposed descriptors provide complementary information to color sift because a fusion of these two kinds of descriptors is found to perform clearly better than either of the two separately moreover the proposed descriptors are about four times faster to compute than color sift

toward a unified color space for perception based image processing image processing methods that utilize characteristics of the human visual system require color spaces with certain properties to operate effectively after analyzing different types of perception based image processing problems we present a list of properties that a unified color space should have due to contradictory perceptual phenomena and geometric issues a color space cannot incorporate all these properties we therefore identify the most important properties and focus on creating opponent color spaces without cross contamination between color attributes i e lightness chroma and hue and with maximum perceptual uniformity induced by color difference formulas color lookup tables define simple transformations from an initial color space to the new spaces we calculate such tables using multigrid optimization considering the hung and berns data of constant perceived hue and the cmc cie94 and ciede2000 color difference formulas the resulting color spaces exhibit low cross contamination between color attributes and are only slightly less perceptually uniform than spaces optimized exclusively for perceptual uniformity we compare the ciede2000 based space with commonly used color spaces in two examples of perception based image processing in both cases standard methods show improved results if the new space is used all color space transformations and examples are provided as matlab codes on our website