feature extraction on local jet space for texture classification abstract the proposal of this study is to analyze the texture pattern recognition over the local jet space looking forward to improve the texture characterization local jets decompose the image based on partial derivatives allowing the texture feature extraction be exploited in different levels of geometrical structures each local jet component evidences a different local pattern such as flat regions directional variations and concavity or convexity subsequently a texture descriptor is used to extract features from 0th 1st and 2nd derivative components four well known databases brodatz vistex usptex and outex and four texture descriptors fourier descriptors gabor filters local binary pattern and local binary pattern variance were used to validate the idea showing in most cases an increase of the success rates

rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden markov model in this correspondence we have presented a rotation and gray scale transform invariant texture recognition scheme using the combination of quadrature mirror filter qmf bank and hidden markov model hmm in the first stage the qmf bank is used as the wavelet transform to decompose the texture image into subbands the gray scale transform invariant features derived from the statistics based on first order distribution of gray levels are then extracted from each subband image in the second stage the sequence of subbands is modeled as a hidden markov model hmm and one hmm is designed for each class of textures the hmm is used to exploit the dependence among these subbands and is able to capture the trend of changes caused by rotation during recognition the unknown texture is matched against all the models the best matched model identifies the texture class up to 93 33 classification accuracy is reported

new algorithms for euclidean distance transformation of an n dimensional digitized picture with applications abstract in this paper we propose a new method to obtain the euclidean distance transformation and the voronoi diagram based on the exact euclidean metric for an n dimensional picture we present four algorithms to perform the transformation which are constructed by the serial composition of n dimensional filters when performed by a general purpose computer they are faster than the method by h yamada for a two dimensional picture those algorithms require only one n dimensional array for storing input output pictures and a single one dimensional array for a work area if an input picture needs not be preserved

color texture classification using shortest paths in graphs color textures are among the most important visual attributes in image analysis this paper presents a novel method to analyze color textures by modeling a color image as a graph in two different and complementary manners each color channel separately and the three color channels altogether and by obtaining statistical moments from the shortest paths between specific vertices of this graph such an approach allows to create a set of feature vectors which were extracted from vistex usptex and tc00013 color texture databases the best classification results were 99 07 96 85 and 91 54 lda with leave one out 87 62 66 71 and 88 06 1nn with holdout and 98 62 96 16 and 91 34 lda with holdout of success rate percentage of samples correctly classified for these three databases respectively these results prove that the proposed approach is a powerful tool for color texture analysis to be explored

classification of noisy signals using fuzzy artmap neural networks this paper describes an approach to classification of noisy signals using a technique based on the fuzzy artmap neural network famnn the proposed method is a modification of the testing phase of the fuzzy artmap that exhibits superior generalization performance compared to the generalization performance of the standard fuzzy artmap in the presence of noise an application to textured gray scale image segmentation is presented the superiority of the proposed modification over the standard fuzzy artmap is established by a number of experiments using various texture sets feature vectors and noise types the texture sets include various aerial photos and also samples obtained from the brodatz album furthermore the classification performance of the standard and the modified fuzzy artmap is compared for different network sizes classification results that illustrate the performance of the modified algorithm and the famnn are presented

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

texture analysis using fractal descriptors estimated by the mutual interference of color channels fractal descriptors are used to describe color textures spatial structure and color distribution are analyzed without separating the image into channels the volumes of the dilated structure express the level of details at each scale based on the intensity color distribution the descriptors combine the efficiency of fractals in describing complex structures with the richness of color analysis the method outperformed other approaches whose effectiveness is widely attested in studies on texture analysis methods this work presents a method for color texture analysis based on fractal geometry the method is based on its predecessor 4 and consists of mapping each color channel onto a surface and dilating such surface by spheres with a variable radius the descriptors are obtained from the relation between the volumes of the dilated surfaces and the dilation radii the dilation process creates a mutual interference among the color channels the proposed descriptors measure the degree of such interference as well as the complexity of pixel intensity arrangements this combination provides a robust and precise texture description the efficiency of the method is assessed in a classification task of well known texture data sets and the results demonstrate that it outperforms the best approaches described in the literature

computing bit error probabilities for avalanche photodiode receivers by large deviations theory a bit error probability analysis of direct detection optical receivers is presented employing avalanche photodiodes an asymptotic analysis for large signal intensities is presented this analysis provides some useful insight into the balance between the poisson statistics the avalanche gain statistics and the gaussian thermal noise the conjugate distribution is developed it is obtained by applying the large deviation exponential twisting formula it is demonstrated that this conjugate distribution can be used to obtain numerically efficient monte carlo estimates of the bit error probability via the importance sampling method

coarse iris classification using box counting to estimate fractal dimensions this paper proposes a novel algorithm for the automatic coarse classification of iris images using a box counting method to estimate the fractal dimensions of the iris first the iris image is segmented into sixteen blocks eight belonging to an upper group and eight to a lower group we then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions finally all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions this classification method has been tested and evaluated on 872 iris cases and the proportions of these categories in our database are 5 50 38 54 21 79 and 34 17 the iris images are classified with two algorithms the double threshold algorithm which classifies iris images with an accuracy of 94 61 and the backpropagation algorithm which is 93 23 accurate when we allow for the border effect the double threshold algorithm is 98 28 accurate

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

iris recognition based on bidimensional empirical mode decomposition and fractal dimension as the demand for information security increases more attention is being paid to biometrics based automated personal identification one of the most promising current biometric techniques is based on the human iris this paper attempts to detect shape information from the iris by analyzing local intensity variations of an iris image the methodology involves extraction of iris features using bidimensional empirical mode decomposition bemd and fractal dimension after the preprocessing procedure the normalized effective iris image is decomposed into 2d intrinsic mode function imf components at different spatial frequencies by bidimensional empirical mode decomposition then the texture features of each intrinsic mode function image are obtained via the differential box counting method to evaluate the efficacy of the proposed approach three different similarity measures used in recognition are adopted the experimental results using the casia and ice iris databases show that the schema presented achieves promising results for iris recognition

color spectral analysis for spatial structure characterization of textures in ihls color space in this article a linear prediction model based approach for color texture characterization and classification in the improved hue luminance and saturation color space is presented pure chrominance structure information is used in addition to the normally used luminance structure information for color texture classification hue and saturation channels of a color image in ihls color space are combined using a complex exponential to give a single channel which holds all the chrominance information of the image two dimensional complex multichannel versions of the non symmetric half plane autoregressive model the quarter plane autoregressive model and the gauss markov random field model are used to perform parametric power spectrum estimation of both luminance and the combined chrominance channels of the image the accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images spectral distance measures are calculated for the spectral information of luminance and chrominance channels individually as well as combined through a combination coefficient using these distance measures color texture classification is done with k nearest neighbor algorithm experimental results verify that the ihls color space exhibits better performance than the rgb color space indicating the significance of using ihls for such analysis they also show that color texture characterization and percentage classification obtained by combined luminance and chrominance structure information is better than the color texture classification done using only the luminance structure information

noisy texture classification a higher order statistics approach abstract in this paper a novel texture classification scheme using higher order statistics hos functions as discriminating features is proposed it is well known that such statistical parameters are insensitive to additive gaussian noise in particular third order statistical parameters i e third order cumulants and bispectrum are insensitive to any symmetrically distributed noise and also exhibit the capability of better characterizing non gaussian signals by exploiting these hos properties it is possible to devise a robust method for classifying textures affected by noise with different distributions and even with very low signal to noise ratios

rotation invariant texture classification using a complete space frequency model a method of rotation invariant texture classification based on a complete space frequency model is introduced a polar analytic form of a two dimensional 2 d gabor wavelet is developed and a multiresolution family of these wavelets is used to compute information conserving microfeatures from these microfeatures a micromodel which characterizes spatially localized amplitude frequency and directional behavior of the texture is formed the essential characteristics of a texture sample its macrofeatures are derived from the estimated selected parameters of the micromodel classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures in experiments comparatively high correct classification rates were obtained using large sample sets

information measures in scale spaces this article investigates renyi s 1976 generalized entropies under linear and nonlinear scale space evolutions of images scale spaces are useful computer vision concepts for both scale analysis and image restoration we regard images as densities and prove monotony and smoothness properties for the generalized entropies the scale space extended generalized entropies are applied to global scale selection and size estimations finally we introduce an entropy based fingerprint description for textures

the adaptive hough transform we introduce the adaptive hough transform aht as an efficient way of implementing the hough transform ht method for the detection of 2 d shapes the aht uses a small accumulator array and the idea of a flexible iterative coarse to fine accumulation and search strategy to identify significant peaks in the hough parameter spaces the method is substantially superior to the standard ht implementation in both storage and computational requirements in this correspondence we illustrate the ideas of the aht by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2 d parameter spaces we show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape

perceptually uniform color spaces for color texture analysis an empirical evaluation rgb a nonuniform color space is almost universally accepted by the image processing community as the means for representing color on the other hand perceptually uniform spaces such as l a b as well as approximately uniform color spaces such as hsv exist in which measured color differences are proportional to the human perception of such differences this paper compares rgb with l a b and hsv in terms of their effectiveness in color texture analysis there has been a limited but increasing amount of work on the color aspects of textured images the results have shown that incorporating color into a texture analysis and recognition scheme can be very important and beneficial the presented methodology uses a family of gabor filters specially tuned to measure specific orientations and sizes within each color texture effectiveness is measured by the classification performance of each color space as well as by classifier independent measures experimental results are obtained with a variety of color texture images perceptually uniform spaces are shown to outperform rgb in many cases

on local intrinsic dimension estimation and its applications in this paper we present multiple novel applications for local intrinsic dimension estimation there has been much work done on estimating the global dimension of a data set typically for the purposes of dimensionality reduction we show that by estimating dimension locally we are able to extend the uses of dimension estimation to many applications which are not possible with global dimension estimation additionally we show that local dimension estimation can be used to obtain a better global dimension estimate alleviating the negative bias that is common to all known dimension estimation algorithms we illustrate local dimension estimation s uses towards additional applications such as learning on statistical manifolds network anomaly detection clustering and image segmentation

segmentation of color textures this paper describes an approach to perceptual segmentation of color image textures a multiscale representation of the texture image generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing the segmented clusters are then restructured in order to isolate core clusters i e patches in which the pixels are definitely associated with the same region the image pixels representing the core clusters are used to form 3d color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image the process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution

stochastic resonance in a bias monostable system with frequency mixing force and multiplicative and additive noise abstract the stochastic resonance in a bias monostable system subject to frequency mixing force and multiplicative and additive noise is investigated based on the adiabatic elimination theory the analytic expressions of the signal to noise ratio snr for the fundamental and higher harmonics are obtained it is shown that the snr is a non monotonic function of the intensities of the multiplicative and additive noise as well as the system parameter moreover the snr for the fundamental harmonic decreases with the increase of the system bias while the snr for the higher harmonics behaves non monotonically as the system bias varies

discrete schroedinger transform for texture recognition abstract this work presents a new procedure to extract features of grey level texture images based on the discrete schroedinger transform this is a non linear transform where the image is mapped as the initial probability distribution of a wave function and such distribution evolves in time following the schroedinger equation from quantum mechanics the features are provided by standard deviation of the distribution measured at different times the proposed method is applied to the classification of three databases of textures used for benchmark and compared to other well known texture descriptors in the literature such as textons local binary patterns multifractals among others all of them are outperformed by the proposed method in terms of percentage of images correctly classified the proposal is also applied to the identification of plant species using scanned images of leaves and again it outperforms other texture methods a test with images affected by gaussian and salt pepper noise is also carried out also with the best performance achieved by the schroedinger descriptors

a quad tree approach to image segmentation which combines statistical and spatial information abstract a new approach to the problem of image segmentation is presented by combining a nonparametric classifier based on a clustering algorithm with a quad tree representation of the image the scheme is both simple to implement and performs well giving satisfactory results at signal to noise ratios well below 1 the results of an analysis of the algorithm are borne out by a comprehensive set of tests on gaussian images and synthetic textures which demonstrate its principal features

scaled and rotated texture classification using a class of basis functions abstract three classes of basis functions are considered for classifying scaled and rotated textured images the first is the orthonormal compactly supported daubechies and the discrete haar bases the second is the biorthogonal basis and the third is the non orthogonal gabor basis textures are scaled and rotated and the basis functions are used to expand them features are computed on a combination of inter resolution coefficients experimental results show that the daubechies orthonormal basis perform well in recognizing transformed textures followed by the haar basis the concept of multiresolution representation and orthogonality are shown to be useful for invariant texture classificaiton

new method for feature extraction based on fractal behavior in this paper a novel approach to feature extraction based on fractal theory is presented as a powerful technique in pattern recognition this paper presents a new fractal feature that can be applied to extract the feature of two dimensional objects it is constructed by a hybrid feature extraction combining wavelet analysis central projection transformation and fractal theory new fractal feature and fractal signatures are reported a multiresolution family of the wavelets is also used to compute information conserving micro features we employed a central projection method to reduce the dimensionality of the original input pattern a wavelet transformation technique to transform the derived pattern into a set of sub patterns its fractal dimension can readily be computed and to use the fractal dimension as the feature vectors moreover a modified fractal signature is also used to distinguish the distinct handwritten signatures we expect that the proposed fractal method can also be used for improving the extraction and classification of features in pattern recognition

a new feature extractor invariant to intensity rotation and scaling of color images this paper proposes a new method for extracting the invariant features of an image based on the concept of principal component analysis and a competitive learning algorithm the proposed algorithm can be applied to binary gray level or colored texture images with a size greater than 256x256 pixels in addition to translation scaling and rotation invariant extraction the extraction of a feature invariant to color intensity can be implemented by using this method in our experiment the proposed method shows the capability to differentiate images having the same shape but different colored textures the experimental results report the effectiveness of this technique and its performance as measured by recognition accuracy rate and computational time these results are also compared with those obtained by classical techniques

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

on large deviations theory and asymptotically efficient monte carlo estimation it is shown how large deviations theory can be applied to construct an asymptotically efficient simulation distribution for monte carlo simulation using the importance sampling technique a sufficient and necessary condition is given for the asymptotic efficiency of the candidate simulation distributions this is done in the multidimensional setting that is required by many practical simulation problems the result obtained is applied primarily in two areas first the generalization of previous work dealing with functionals of markov chains is discussed a second area of application is the simulation of nonlinear systems with gaussian inputs as an example of a system with gaussian inputs the simulation of a digital communication channel with nonlinear isi intersymbol interference characteristic of satellite data links is considered in the case of linear isi the asymptotically efficient exponentially twisted distribution turns out to agree with a method previously proposed by d lu and k yao 1988 the large deviations point of view provides some useful insight on how to extent the lu and yao method to nonlinear channels

an investigation on the use of local multi resolution patterns for image classification abstract the subject of this study is the use of local multi dimensional patterns for image classification the contribution is both theoretical and experimental on the one hand the paper introduces a complete and general mathematical model for encoding multi resolution rotation invariant local patterns on the other experimentally evaluates the use of multi resolution patterns for image classification both from an information and performance based standpoint the results indicate that the joint multi resolution model proposed in the paper can actually convey an additional amount of information with respect to the marginal model but also that the marginal model i e concatenation of features computed at different resolutions can be a good enough approximation for practical applications

texture recognition based on diffusion in networks a texture recognition method is proposed the image is modeled into networks the texture is characterized by the diffusion over the networks it is demonstrated that directed networks are better to texture recognition the proposed method outperform the state of the art much work has been done in the field of texture analysis and classification while promising classification methods have been proposed most of them rely on classical image analysis approaches this paper presents a texture classification method based on diffusion in directed networks first an image is modeled as a directed network by mapping each pixel as a node and connecting two nodes up to a maximum distance r to reveal texture properties links between two nodes are removed based on the pixel intensity difference once such a network is obtained the activity of each node is estimated by random walks and combined into a histogram to describe the image the main contribution of this paper is the use of directed networks which tends to provide better performance than in undirected cases also we have shown that the activity induced on these networks can be effectively used as texture descriptor experimental results show that the proposed method is favorably compared to traditional texture methods on widely used texture datasets the proposed method is also found to be promising for plant species classification using samples of leaf texture

texture analysis using graphs generated by deterministic partially self avoiding walks texture is one of the most important visual attributes for image analysis it has been widely used in image analysis and pattern recognition a partially self avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results this approach uses walkers called tourists to exploit the gray scale image contexts in several levels here we present an approach to generate graphs out of the trajectories produced by the tourist walks the generated graphs embody important characteristics related to tourist transitivity in the image computed from these graphs the statistical position degree mean and dispersion entropy of two vertices with the same degree measures are used as texture descriptors a comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach

fuzzy aura matrices for texture classification the aura concept has been developed from the set theory and is an efficient tool to characterize texture images it is based on the notion of aura set and on the associated aura measure that involve the neighborhood of each image pixel in this paper we propose to extend this concept to the framework of fuzzy sets in order to take the imprecise nature of images into account we define the notions of fuzzy aura sets and of aura measures to compute fuzzy aura matrices as texture descriptors fuzzy aura measures assume no restrictions about the neighborhood shape size and spatial invariance extensive tests of texture classification on outex benchmark datasets show that fuzzy aura matrices computed with spatially variant neighborhoods often outperform other powerful texture descriptors on both gray level and color images highlightsto characterize texture images we extend the aura concepts to the fuzzy framework we define fuzzy aura sets and measures to describe textures by fuzzy aura matrices fuzzy aura measures can use spatially variant neighborhoods such adaptivity is interesting to classify both gray level and color texture images

a new image segmentation method based on multifractal detrended moving average analysis abstract in order to segment and delineate some regions of interest in an image we propose a novel algorithm based on the multifractal detrended moving average analysis mf dma in this method the generalized hurst exponent h q is calculated for every pixel firstly and considered as the local feature of a surface and then a multifractal detrended moving average spectrum mf dms d h q is defined by the idea of box counting dimension method therefore we call the new image segmentation method mf dms based algorithm the performance of the mf dms based method is tested by two image segmentation experiments of rapeseed leaf image of potassium deficiency and magnesium deficiency under three cases namely backward 0 centered 0 5 and forward 1 with different q values the comparison experiments are conducted between the mf dms method and other two multifractal segmentation methods namely the popular mfs based and latest mf dfs based methods the results show that our mf dms based method is superior to the latter two methods the best segmentation result for the rapeseed leaf image of potassium deficiency and magnesium deficiency is from the same parameter combination of 0 5 and d h 10 when using the mf dms based method an interesting finding is that the d h 10 outperforms other parameters for both the mf dms based method with centered case and mf dfs based algorithms by comparing the multifractal nature between nutrient deficiency and non nutrient deficiency areas determined by the segmentation results an important finding is that the gray value s fluctuation in nutrient deficiency area is much severer than that in non nutrient deficiency area

color texture classification based on gravitational collapse texture and color are essential attributes to be analyzed for any robust computer vision system this paper presents a novel method to analyze color texture images based on representing states of a simplified gravitational collapse from each image color channel and extracting information from each state using the bouligand minkowski fractal dimension and the lacunarity method in this approach we obtained the best classification results when the images of each channel evolved in times t 1 5 10 15 each time representing a state using radius r 3 4 5 6 for the bouligand minkowski method and box size l 2 3 4 5 6 for the lacunarity method the best classification results were 99 37 and 96 57 of success rate percentage of samples correctly classified for vistex and usptex databases respectively these results prove that the proposed approach opens a promising source of research in color texture analysis still to be explored

unsupervised texture segmentation using gabor filters abstract this paper presents a texture segmentation algorithm inspired by the multi channel filtering theory for visual information processing in the early stages of human visual system the channels are characterized by a bank of gabor filters that nearly uniformly covers the spatial frequency domain and a systematic filter selection scheme is proposed which is based on reconstruction of the input image from the filtered images texture features are obtained by subjecting each selected filtered image to a nonlinear transformation and computing a measure of energy in a window around each pixel a square error clustering algorithm is then used to integrate the feature images and produce a segmentation a simple procedure to incorporate spatial information in the clustering process is proposed a relative index is used to estimate the true number of texture categories

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

a simplified gravitational model to analyze texture roughness textures are among the most important visual attributes in image analysis this paper presents a novel method to analyze texture based on representing states of a simplified gravitational collapse from an image and extracting information from each state using fractal dimension in this approach an image evolves in times t 1 2 20 each time representing a state which is explored by the bouligand minkowski method using radius r 3 4 8 these parameters allow to create a set of feature vectors which were extracted from brodatz s textures and leaf textures the best classification results were 98 75 and 86 67 of success rate percentage of samples correctly classified for these two databases respectively these results prove that the proposed approach opens a promising source of research in texture analysis to be explored

classification of rotated and scaled textured images using gaussian markov random field models consideration is given to the problem of classifying a test textured image that is obtained from one of c possible parent texture classes after possibly applying unknown rotation and scale changes to the parent texture the training texture images parent classes are modeled by gaussian markov random fields gmrfs to classify a rotated and scaled test texture the rotation and scale changes are incorporated in the texture model through an appropriate transformation of the power spectral density of the gmrf for the rotated and scaled image a bona fide likelihood function that shows the explicit dependence of the likelihood function on the gmrf parameters as well as on the rotation and scale parameters is derived although in general the scaled and or rotated texture does not correspond to a finite order gmrf it is possible nonetheless to write down a likelihood function for the image data the likelihood function of the discrete fourier transform of the image data corresponds to that of a white nonstationary gaussian random field with the variance at each pixel i j being a known function of the rotation the scale the gmrf model parameters and i j the variance is an explicit function of the appropriately sampled power spectral density of the gmrf the estimation of the rotation and scale parameters is performed in the frequency domain by maximizing the likelihood function associated with the discrete fourier transform of the image data cramer rao error bounds on the scale and rotation estimates are easily computed a modified bayes decision rule is used to classify a given test image into one of c possible texture classes the classification power of the method is demonstrated through experimental results on natural textures from the brodatz album

segmentation of textured images based on fractals and image filtering this paper describes a new approach to the segmentation of textured gray scale images based on image pre ltering and fractal features traditionally lter bank decomposition methods consider the energy in each band as the textural feature a parameter that is highly dependent on image intensity in this paper we use fractal based features which depend more on textural characteristics and not intensity information to reduce the total number of features used in the segmentation the signi cance of each feature is examined using a test similar to the f test and less signi cant features are not used in the clustering process the commonly used k means algorithm is extended to an iterative k means by using a variable window size that preserves boundary details the number of clusters is estimated using an improved hierarchical approach that ignores information extracted around region boundaries 2001 pattern recognition society published by elsevier science ltd all rights reserved

improved bi dimensional emd and hilbert spectrum for the analysis of textures an improved bi dimensional empirical mode decomposition ibemd is proposed structure of image extremas represents the important feature of images and is useful for the information extraction and analysis the image extrema are classified into the five different sets which are called as the structural extrema the structural extrema are used instead of the classical extrema and the bemd bi dimensional empirical mode decomposition algorithms based on the structural extrema are more accurate through interpolating the up and down envelopes specially the ibemd has the least nmse normalised mean square error and the biggest snr signal to noise ratio for the mode decomposition and greatly improves the robustness of the bemd moreover quaternion hilbert transform based space spatial frequency tool is improved and applied to the texture analysis the experiments of texture analysis show that the new approach is efficient for the application in texture analysis

an improved box counting method for image fractal dimension estimation fractal dimension fd is a useful feature for texture segmentation shape classification and graphic analysis in many fields the box counting approach is one of the frequently used techniques to estimate the fd of an image this paper presents an efficient box counting based method for the improvement of fd estimation accuracy a new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required thereby yielding more accurate estimates the experiments using synthesized fractional brownian motion images real texture images and remote sensing images demonstrate this new method can outperform the well known differential boxing counting dbc method

rotation invariant texture classification using feature distributions abstract a distribution based classification approach and a set of recently developed texture measures are applied to rotation invariant texture classification the performance is compared to that obtained with the well known circular symmetric autoregressive random field csar model approach a difficult classification problem of 15 different brodatz textures and seven rotation angles is used in experiments the results show much better performance for our approach than for the csar features a detailed analysis of the confusion matrices and the rotation angles of misclassified samples produces several interesting observations about the classification problem and the features used in this study

content based image retrieval using color difference histogram this paper presents a novel image feature representation method namely color difference histograms cdh for image retrieval this method is entirely different from the existing histograms most of the existing histogram techniques merely count the number or frequency of pixels however the unique characteristic of cdhs is that they count the perceptually uniform color difference between two points under different backgrounds with regard to colors and edge orientations in l a b color space this method pays more attention to color edge orientation and perceptually uniform color differences and encodes color orientation and perceptually uniform color difference via feature representation in a similar manner to the human visual system the method can be considered as a novel visual attribute descriptor combining edge orientation color and perceptually uniform color difference as well as taking the spatial layout into account without any image segmentation learning processes or clustering implementation experimental results demonstrate that it is much more efficient than the existing image feature descriptors that were originally developed for content based image retrieval such as mpeg 7 edge histogram descriptors color autocorrelograms and multi texton histograms it has a strong discriminative power using the color texture and shape features while accounting for spatial layout

segmentation based compression for graylevel images abstract this paper presents a segmentation based lossy image compression slic algorithm the segmentation scheme biswas and pal pattern recog lett 21 2000 entropy based and hierarchical in nature provides sub images of homogeneous regions the compression algorithm encodes a graylevel image through global approximations of sub images by 2 d bezier bernstein polynomial along with corrections if needed over regions in sub images by local approximation contours by 1 d bezier bernstein polynomial and texture if present by huffman coding scheme using hilbert scan on texture blocks order of the 2 d polynomials has been computed with the help of an image quality index iqi the proposed compression algorithm also examines the compression result by encoding contours through their approximation based on stretching of discrete circular arcs stretching is done by affine transformation compression results in both the cases have been compared with jpeg results attempts have been made to evaluate the quality of reconstructed images through a fidelity vector whose components are different objective measures

image coding based on a fractal theory of iterated contractive image transformations the author proposes an independent and novel approach to image coding based on a fractal theory of iterated transformations the main characteristics of this approach are that i it relies on the assumption that image redundancy can be efficiently exploited through self transformability on a block wise basis and ii it approximates an original image by a fractal image the author refers to the approach as fractal block coding the coding decoding system is based on the construction for an original image to encode of a specific image transformation a fractal code which when iterated on any initial image produces a sequence of images that converges to a fractal approximation of the original it is shown how to design such a system for the coding of monochrome digital images at rates in the range of 0 5 1 0 b pixel the fractal block coder has performance comparable to state of the art vector quantizers

solving convex programs by random walks minimizing a convex function over a convex set in n dimensional space is a basic general problem with many interesting special cases here we present a simple new algorithm for convex optimization based on sampling by a random walk it extends naturally to minimizing quasi convex functions and to other generalizations

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

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

conditional importance sampling estimators we give a unified presentation of the conditional importance sampling estimators we show that they are always better than their nonconditional counterparts we then present the large deviation theory associated with these estimators in particular we give conditional simulation distributions that are optimal in the sense that they are efficient interestingly enough these distributions will not in general be the usual exponential shifts we give examples showing how to use the theory developed

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

local convex and concave pattern aiming at the defect of local binary pattern lbp this paper presents a novel and efficient local second order derivative descriptor for image texture feature extraction based on local convex and concave pattern lccp lccp encodes local directional second order derivative feature of texture based on local convex and concave characteristic which can really capture the derivative information of texture than the center symmetric local derivative pattern cs ldp does the proposed multi resolution lccp not only encodes directional derivative information but also constructs spatial relationships in a given local region by bilinear interpolation extensions of multi resolution lccp based on uniform pattern and weighted pattern have also been discussed the performance of the proposed lccp and its extensions are assessed on the biometric recognition and texture classification under different challenges each image is divided into several regions from which histogram features are extracted and concatenate into an enhanced feature vector for match extensive experiments have validated the effectiveness of lccp and its extensions

nonconventional stochastic resonance it is argued on the basis of linear response theory lrt that new types of stochastic resonance sr are to be anticipated in diverse systems quite different from the one most commonly studied to date which has a static double well potential and is driven by a net force equal to the sum of periodic and stochastic terms on this basis three new nonconventional forms of sr are predicted sought found and investigated both theoretically and by analogue electronic experiment a in monostable systems b in bistable systems with periodically modulated noise and c in a system with coexisting periodic attractors in each case it is shown that lrt can provide a good quantitative description of the experimental results for sufficiently weak driving fields it is concluded that sr is a much more general phenomenon than has hitherto been appreciated

multiple resolution texture analysis and classification textures are classified based on the change in their properties with changing resolution the area of the gray level surface is measured at serveral resolutions this area decreases at coarser resolutions since fine details that contribute to the area disappear fractal properties of the picture are computed from the rate of this decrease in area and are used for texture comparison and classification the relation of a texture picture to its negative and directional properties are also discussed

efficient monte carlo procedures for generating points uniformly distributed over bounded regions we consider the monte carlo problem of generating points uniformly distributed within an arbitrary bounded measurable region the class of markovian methods considered generate points asymptotically uniformly distributed within the region computational experience suggests the methods are potentially superior to conventional rejection techniques for large dimensional regions

image segmentation using active contours driven by the bhattacharyya gradient flow this paper addresses the problem of image segmentation by means of active contours whose evolution is driven by the gradient flow derived from an energy functional that is based on the bhattacharyya distance in particular given the values of a photometric variable or of a set thereof which is to be used for classifying the image pixels the active contours are designed to converge to the shape that results in maximal discrepancy between the empirical distributions of the photometric variable inside and outside of the contours the above discrepancy is measured by means of the bhattacharyya distance that proves to be an extremely useful tool for solving the problem at hand the proposed methodology can be viewed as a generalization of the segmentation methods in which active contours maximize the difference between a finite number of empirical moments of the ldquoinsiderdquo and ldquooutsiderdquo distributions furthermore it is shown that the proposed methodology is very versatile and flexible in the sense that it allows one to easily accommodate a diversity of the image features based on which the segmentation should be performed as an additional contribution a method for automatically adjusting the smoothness properties of the empirical distributions is proposed such a procedure is crucial in situations when the number of data samples supporting a certain segmentation class varies considerably in the course of the evolution of the active contour in this case the smoothness properties of the empirical distributions have to be properly adjusted to avoid either over or underestimation artifacts finally a number of relevant segmentation results are demonstrated and some further research directions are discussed

exact expression for the diffusion propagator in a family of time dependent anharmonic potentials we have obtained the exact expression of the diffusion propagator in the time dependent anharmonic potential v x t 1 2a t x 2 b ln x the underlying euclidean metric of the problem allows us to obtain analytical solutions for a whole family of the elastic parameter a t exploiting the relation between the path integral representation of the short time propagator and the modified bessel functions we have also analyzed the conditions for the appearance of a nonzero flow of particles through the infinite barrier located at the origin b 0

texture classification using windowed fourier filters we define a distance between textures for texture classification from texture features based on windowed fourier filters the definition of the distance relies on an interpretation of our texture attributes in terms of spectral density when the texture can be considered as a gaussian random field the distance between textures is then defined as a symmetrized kullback distance which is a simple function of the attributes and does not require any normalization an experimental analysis using gabor filters and in particular a comparison to quadratic distances shows the efficiency and robustness of the method

quaternion michelson descriptor for color image classification in this paper we develop a simple yet powerful framework called quaternion michelson descriptor qmd to extract local features for color image classification unlike traditional local descriptors extracted directly from the original raw image space qmd is derived from the michelson contrast law and the quaternionic representation qr of color images the michelson contrast is a stable measurement of image contents from the viewpoint of human perception while qr is able to handle all the color information of the image holisticly and to preserve the interactions among different color channels in this way qmd integrates both the merits of michelson contrast and qr based on the qmd framework we further propose two novel quaternionic michelson contrast binary pattern descriptors from different perspectives experiments and comparisons on different color image classification databases demonstrate that the proposed framework and descriptors outperform several state of the art methods

the multifractal structure of contrast changes in natural images from sharp edges to textures we present a formalism that leads naturally to a hierarchical description of the different contrast structures in images providing precise definitions of sharp edges and other texture components within this formalism we achieve a decomposition of pixels of the image in sets the fractal components of the image such that each set contains only points characterized by a fixed strength of the singularity of the contrast gradient in its neighborhood a crucial role in this description of images is played by the behavior of contrast differences under changes in scale contrary to naive scaling ideas where the image is thought to have uniform transformation properties field 1987 each of these fractal components has its own transformation law and scaling exponents a conjecture on their biological relevance is also given

direct sequence spread spectrum multiple access communications with random signature sequences a large deviations analysis a direct sequence spread spectrum multiple access bit error probability analysis is developed using large deviations theory let m denote the number of interfering spread spectrum signals and let n denote the signature sequence length then the large deviations limit is as n to infinity with m fixed a tight asymptotic expression for the bit error probability is proven and in addition recent large deviations results with the importance sampling monte carlo estimation technique are applied to obtain accurate and computationally efficient estimates of the bit error probability for finite values of m and n the large deviations point of view is compared also to the conventional asymptotics of central limit theory and the associated gaussian approximation the gaussian approximation is accurate and the ratio m n is moderately large and all signals have roughly equal power in the near far situation however the gaussian approximation is quite poor in contrast large deviations techniques are more accurate in the near far situation and it is here that these methods provide some important practical insight

texture analysis by fractal descriptors over the wavelet domain using a best basis decomposition abstract this work proposes the development and study of a novel set of fractal descriptors for texture analysis these descriptors are obtained by exploring the fractal like relation among the coefficients and magnitudes of a particular type of wavelet decomposition to know the best basis selection the proposed method is tested in the classification of three sets of textures from the literature brodatz vistex and usptex the method is also applied to a challenging real world problem which is the identification of species of plants from the brazilian flora the results are compared with other classical and state of the art texture descriptors and demonstrate the efficiency of the proposed technique in this task

finite prolate spheroidal sequences and their applications ii image feature description and segmentation for pt i see ibid vol 9 no 6 p 787 1987 the problem of uncertainty in image feature description is discussed and it is shown how finite prolate spheroidal sequences can be used in the construction of feature descriptions that combine spatial and frequency domain locality in an optimal way methods of constructing such optimal feature sets which are suitable for graphical implementation are described and some generalizations of the quadtree concept are presented these methods are illustrated by examples from image processing applications including feature extraction and texture description the problem of image segmentation is discussed and the importance of scale invariance in overcoming the limitations imposed by uncertainty is demonstrated a novel texture segmentation algorithm that is based on a combination of the new feature description and multiresolution techniques is described and shown to give accurate segmentations on a range of synthetic and natural textures

brief review of invariant texture analysis methods this paper considers invariant texture analysis texture analysis approaches whose performances are not affected by translation rotation affine and perspective transform are addressed existing invariant texture analysis algorithms are carefully studied and classified into three categories statistical methods model based methods and structural methods the importance of invariant texture analysis is presented first each approach is reviewed according to its classification and its merits and drawbacks are outlined the focus of possible future work is also suggested

classification and segmentation of rotated and scaled textured images using texture tuned masks abstract a rotation and scale invariant texture classifier function is described for effective classification and segmentation of images involving textures of unknown rotation and scale changes the classifier used is the texture energy associated with a mask that has been tuned to be both discriminant between different textures and to be invariant to rotation and scale changes the mask tuning scheme utilized is based on task oriented criterion optimization via a guided random search procedure to incorporate the changes both a dynamic texture sample set using a two dimensional 2d linked list and a re ranking procedure are applied for training maximum feature dispersion of inter texture classes and high feature convergence of inner texture class samples associated with other statistical measures are suggested as key criteria in training in a study based on 15 distinct brodatz textures it is found that the tuning process although computationally intensive converges efficiently the mean classifier values of the classifier for a particular texture at different orientation and different scales are tightly clustered an objective measure of classification capability is determined by computing the standard deviation of the classifier over pure texture at definite orientation and scale examples are presented of the classifier function applied to the segmentation of collages of brodatz textures comprising regions of various orientation and scale

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

wavelet based rotational invariant roughness features for texture classification and segmentation we introduce a rotational invariant feature set for texture segmentation and classification based on an extension of fractal dimension fd features the fd extracts roughness information from images considering all available scales at once in this work a single scale is considered at a time so that textures with scale dependent properties are satisfactorily characterized single scale features are combined with multiple scale features for a more complete textural representation wavelets are employed for the computation of single and multiple scale roughness features because of their ability to extract information at different resolutions features are extracted in multiple directions using directional wavelets and the feature vector is finally transformed to a rotational invariant feature vector that retains the texture directional information an iterative k means scheme is used for segmentation and a simplified form of a bayesian classifier is used for classification the use of the roughness feature set results in high quality segmentation performance furthermore it is shown that the roughness feature set exhibits a higher classification rate than other feature vectors presented in this work the feature set retains the important properties of fd based features namely insensitivity to absolute illumination and contrast

automatic classification for solitary pulmonary nodule in ct image by fractal analysis based on fractional brownian motion model perfusion computed tomography ct method has been used to differentiate malignant pulmonary nodules from benign nodules based on the assessment for the change of the ct attenuation value within the pulmonary nodules instead of using the change of the ct attenuation value a set of fractal features based on fractional brownian motion model is proposed in this paper to automatically distinguish malignant nodules from benign nodules in a set of 107 ct images from 107 different patients with each image containing a solitary pulmonary nodule our experimental results obtained from a support vector machine classifier show that the accuracy sensitivity specificity positive predictive value negative predictive value and the area under the roc curve are 83 11 90 92 71 70 80 05 87 52 and 0 8437 respectively by using the proposed fractal based feature set such a result outperforms the conventional method of using the change of the ct attenuation value as the feature for classification when combining this conventional method with our proposed fractal based method the accuracy sensitivity specificity positive predictive value negative predictive value and the area under the roc curve can be promoted to 88 82 93 92 82 90 87 30 90 20 and 0 9019 respectively in other words a high performance of pulmonary nodule classification can be achieved with a single post contrast ct scan

inverse and approximation problem for two dimensional fractal sets the geometry of fractals is rich enough that they have extensively been used to model natural phenomena and images iterated function systems ifs theory provides a convenient way to describe and classify deterministic fractals in the form of a recursive definition as a result it is conceivable to develop image representation schemes based on the ifs parameters that correspond to a given fractal image in this paper we consider two distinct problems an inverse problem and an approximation problem the inverse problem involves finding the ifs parameters of a signal that is exactly generated via an ifs we make use of the wavelet transform and of the image moments to solve the inverse problem the approximation problem involves finding a fractal ifs generated image whose moments match either exactly or in a mean squared error sense a range of moments of the original image the approximating measures are generated by an ifs model of a special form and provide a general basis for the approximation of arbitrary images experimental results verifying our approach will be presented

morphology based multifractal estimation for texture segmentation multifractal analysis is becoming more and more popular in image segmentation community in which the box counting based multifractal dimension estimations are most commonly used however in spite of its computational efficiency the regular partition scheme used by various box counting methods intrinsically produces less accurate results in this paper a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal descriptors namely the local morphological multifractal exponents is defined to characterize the local scaling properties of textures a series of cubic structure elements and an iterative dilation scheme are utilized so that the computational complexity of the morphological operations can be tremendously reduced both the proposed algorithm and the box counting based methods have been applied to the segmentation of texture mosaics and real images the comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide more robust segmentations

texture segmentation using fractal dimension this paper deals with the problem of recognizing and segmenting textures in images for this purpose the authors employ a technique based on the fractal dimension fd and the multi fractal concept six fd features are based on the original image the above average high gray level image the below average low gray level image the horizontally smoothed image the vertically smoothed image and the multi fractal dimension of order two a modified box counting approach is proposed to estimate the fd in combination with feature smoothing in order to reduce spurious regions to segment a scene into the desired number of classes an unsupervised k means like clustering approach is used mosaics of various natural textures from the brodatz album as well as microphotographs of thin sections of natural rocks are considered and the segmentation results to show the efficiency of the technique supervised techniques such as minimum distance and k nearest neighbor classification are also considered the results are compared with other techniques

multilayer graph cuts based unsupervised color texture image segmentation using multivariate mixed student s t distribution and regional credibility merging this paper proposes an unsupervised color texture image segmentation method in order to enhance the effects of segmentation a new color texture descriptor is designed by integrating the compact multi scale structure tensor msst total variation tv flow and the color information due to the fact that msst does not work well for separating regions with large scale texture the total variation flow is used to auxiliarily describe the texture feature by extracting local scale information to segment the color texture image in an unsupervised and multi label way the multivariate mixed student s t distribution mmst is chosen for probability distribution modeling as mmst can describe the distribution of color texture features accurately since the valid class number is hard to adaptively determine in advance a component wise expectation maximization for mmst cem 3st algorithm is proposed which can effectively initialize the valid class number then we can build up the energy functional according to the valid class number and optimize it by multilayer graph cuts method however the problem of over error segmentation often happens to overcome this problem a strategy of regional credibility merging rcm is presented by integrating the regional adjacency relationship region size common edge between regions and regional color texture dissimilarity in order to terminate the whole segmentation process an adaptive iteration convergence criterion is designed which combines the negative logarithm of probability of all color texture features with the kullback leibler kl divergence for mmst experiments using a large number of synthesis color texture images and real natural scene images demonstrate the superiority of our proposed method such as the effective over error segmentation reduction high segmentation accuracy and outperforming visual entirety consistency

shape recognition using fractal geometry within this paper fractal transformations are presented as a powerful new shape recognition technique the motivation behind using fractal transformations is to develop a high speed shape recognition technique which will be scale invariant a review is given of the most popular existing shape recognition techniques there then follows a full mathematical analysis of the new technique together with a proof of the authors fractal invariance theorem the new theorem at the centre of the recognition technique through the mathematical analysis it becomes apparent that the fractal recognition technique possesses the remarkable property that it is able to distinguish between similar objects details are then given of the practical implementation of the technique together with an algorithm for making the technique rotationally invariant the technique is then applied to a selection of real world objects and a comparison made with the popular moment invariants technique this shows that the fractal technique is faster than the technique of moment invariants and also requires less initial information to be effective finally conclusions are drawn and further work detailed

correction to rotation and gray scale transform invariant texture classification using spiral resampling subband decomposition and hidden markov model this paper proposes a new texture classification algorithm that is invariant to rotation and gray scale transformation first we convert two dimensional 2 d texture images to one dimensional 1 d signals by spiral resampling then we use a quadrature mirror filter qmf bank to decompose sampled signals into subbands in each band we take high order autocorrelation functions as features features in different bands which form a vector sequence are then modeled as a hidden markov model bmm during classification the unknown texture is matched against all the models and the best match is taken as the classification result simulations showed that the highest correct classification rate for 16 kinds of texture was 95 14

effect of correlated dichotomous noises on stochastic resonance in a linear system a linear system driven by correlated asymmetric dichotomous noises and periodic signal was investigated in the overdamped case the exact expressions of output signal amplitude and signal to noise ratio snr of the system were derived by means of numerical calculations we found that i at some fixed multiplicative noise intensities the output signal amplitude with frequency exhibits the structure of a weak peak even no peak as the dichotomous noise is asymmetric ii in the case of asymmetric dichotomous noise the signal frequency can cause non monotonous behavior of the output signal amplitude with respect to multiplicative noise intensity iii the curve of snr with frequency has a weak peak and a trough in the case of symmetric dichotomous noise but no peak with asymmetric iv whether the multiplicative noise is symmetric or asymmetric the noise can enhance response of the system v the snr increases with the correlation strength between the two noises decreasing in addition the plane of multiplicative noise intensity versus noise symmetric parameter was plotted

object recognition using gabor co occurrence similarity we present an object recognition approach using co occurrence similarities of gabor magnitude textures in this paper a novel image descriptor multichannel gabor magnitude co occurrence matrices mgmcms is designed to characterize gabor textures for object representation and similarity matching the descriptor is a generalization of multichannel color co occurrence matrices mcms which focus on using robust and discriminative magnitude textures in filtered images our approach starts from gabor wavelet transformation of each object image an exploratory learning algorithm is proposed for learning channel adaptive magnitude truncation parameters and level parameters this allows us to design the magnitude quantization that can reduce overall biased and peaked levels of resulting feature distributions in each channel to avoid over sparse co occurrence distributions on average the direction based grouping is adopted for computational complexity reduction of mgmcms extraction under a specific neighborhood mode on the grouped rescaled magnitude images of per object image when each mgmcm is treated as a probability distribution lying on a multinomial manifold we represent per object image as a point on a product multinomial manifold using multinomial geometry and metric extension technique we construct the p order minkowski co occurrence information distance for similarity matching between the albums of gabor magnitude textures the feasibility and effectiveness of the approach is validated by the experimental results on the yale and feret face databases polyu palmprint database coil 20 object database and zurich buildings database

fractal dimension applied to plant identification this article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension leaves were analyzed according to the complexity of their internal and external shapes a computational program was developed to process analyze and extract the features of leaf images thereby allowing for automatic plant identification results are presented from two experiments the first to identify plant species from the brazilian atlantic forest and brazilian cerrado scrublands using fifty leaf samples from ten different species and the second to identify four different species from genus passiflora using twenty leaf samples for each class a comparison is made of two methods to estimate fractal dimension box counting and multiscale minkowski the results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique when estimating fractal dimension and identifying plants

an analytical study of stochastic resonance in a monostable non harmonic system abstract we present an analytical study of the enhancement of the signal to noise ratio in a monostable non harmonic potential we make use of the exact expression for the diffusion propagator obtained in a previous work finding a monotonically increasing response with the noise amplitude on the other hand for the first time we provide a cut off to such an increase which prevents a probability leakage out of the system