Assessment of texture stationarity using the asymptotic behavior of the empirical mean and variance.

Given textured images considered as realizations of 2-D stochastic processes, a framework is proposed to evaluate the stationarity of their mean and variance. Existing strategies focus on the asymptotic behavior of the empirical mean and variance (respectively EM and EV), known for some types of nondeterministic processes. In this paper, the theoretical asymptotic behaviors of the EM and EV are studied for large classes of second-order stationary ergodic processes, in the sense of the Wold decomposition scheme, including harmonic and evanescent processes. Minimal rates of convergence for the EM and the EV are derived for these processes; they are used as criteria for assessing the stationarity of textures. The experimental estimation of the rate of convergence is achieved using a nonparametric block sub-sampling method. Our framework is evaluated on synthetic processes with stationary or nonstationary mean and variance and on real textures. It is shown that anomalies in the asymptotic behavior of the empirical estimators allow detecting nonstationarities of the mean and variance of the processes in an objective way.     

Wavelet-Based SAR Image Despeckling and Information Extraction, Using Particle Filter

This paper proposes a new-wavelet-based synthetic aperture radar (SAR) image despeckling algorithm using the sequential Monte Carlo method. A model-based Bayesian approach is proposed. This paper presents two methods for SAR image despeckling. The first method, called WGGPF, models a prior with Generalized Gaussian (GG) probability density function (pdf) and the second method, called WGMPF, models prior with a Generalized Gaussian Markov random field (GGMRF). The likelihood pdf is modeled using a Gaussian pdf. The GGMRF model is used because it enables texture parameter estimation. The prior is modeled using GG pdf, when texture parameters are not needed. A particle filter is used for drawing particles from the prior for different shape parameters of GG pdf. When the GGMRF prior is used, the particles are drawn from prior in order to estimate noise-free wavelet coefficients and for those coefficients the texture parameter is changed in order to obtain the best textural parameters. The texture parameters are changed for a predefined set of shape parameters of GGMRF. The particles with the highest weights represents the final noise-free estimate with corresponding textural parameters. The despeckling algorithms are compared with the state-of-the-art methods using synthetic and real SAR data. The experimental results show that the proposed despeckling algorithms efficiently remove noise and proposed methods are comparable with the state-of-the-art methods regarding objective measurements. The proposed WGMPF preserves textures of the real, high-resolution SAR images well.     

Rigid-motion-invariant classification of 3-D textures

This paper studies the problem of 3-D rigid-motion-invariant texture discrimination for discrete 3-D textures that are spatially homogeneous by modeling them as stationary Gaussian random fields. The latter property and our formulation of a 3-D rigid motion of a texture reduce the problem to the study of 3-D rotations of discrete textures. We formally develop the concept of 3-D texture rotations in the 3-D digital domain. We use this novel concept to define a "distance" between 3-D textures that remains invariant under all 3-D rigid motions of the texture. This concept of "distance" can be used for a monoscale or a multiscale 3-D rigid-motion-invariant testing of the statistical similarity of the 3-D textures. To compute the "distance" between any two rotations R(1) and R(2) of two given 3-D textures, we use the Kullback-Leibler divergence between 3-D Gaussian Markov random fields fitted to the rotated texture data. Then, the 3-D rigid-motion-invariant texture distance is the integral average, with respect to the Haar measure of the group SO(3), of all of these divergences when rotations R(1) and R(2) vary throughout SO(3). We also present an algorithm enabling the computation of the proposed 3-D rigid-motion-invariant texture distance as well as rules for 3-D rigid-motion-invariant texture discrimination/classification and experimental results demonstrating the capabilities of the proposed 3-D rigid-motion texture discrimination rules when applied in a multiscale setting, even on very general 3-D texture models.     

Wavelet-based texture retrieval using generalized Gaussian densityand Kullback-Leibler distance

We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step followed by computing the Kullback-Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures. Experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates, e.g., from 65% to 77%, compared with traditional approaches, while it retains comparable levels of computational complexity.     

Maximum likelihood approach to image texture and acoustic signalclassification

The authors describe a method of classifying natural textures based on the maximum likelihood parameter estimation technique. The novelty of the technique lies in the use of textural features that are derived from the subbands of a wavelet transformed image via the co-occurrence matrices. A maximum likelihood classifier is designed using a set of training texture samples. Ten different Brodotz (1965) textures have been classified using this procedure with an average classification accuracy of 99.7%. The main emphasis is to apply this technique to the classification of underwater acoustic signals. A time-frequency plot is obtained for each segment of the acoustic signal and then converted to an intensity pattern. The textural classification scheme is then applied to the intensity patterns of the acoustic signals. Eight different underwater acoustic signals have been classified by this procedure with an average accuracy of 99.99%     

非平稳随机场模型的遥感图像分割

提出一种双随机场模型。通过结合局部的统计量，将一种因果的自回归随机场模型应用于非平稳图像纹理的描述，同时，用马尔可夫随机场给标记场建模可以取得较好的分割效果。通过对实验的分析发现，分割结果仍存在一些错误。因此，在这种模型的基础上，引入了一种新的分类特征(熵率)来改善目标分割的效果。它反映了纹理的高阶相关性，改进了随机场低阶模型的不足。所提出的算法用于遥感图像的城区提取实验。     

Oriented texture completion by AM-FM reaction-diffusion

We provide an automated method to repair broken, occluded oriented image textures. Our approach is based on partial differential equations (PDEs) and AM-FM image modeling. Reconstruction of the texture occurs via simultaneous PDE-generated diffusion and reaction. In the diffusion process, the image is adaptively smoothed, preserving important boundaries and features. The reaction process produces the reconstructed textural information in the occluded image regions. Gabor (1946) filters are designed and used in the reaction process using an AM-FM dominant component analysis. An AM-FM model of the texture image is constructed, making it possible to localize the reaction filters spatio-spectrally. In contrast to previous disocclusion techniques that depend on interpolation, on continuity of the connected components within the image level sets, or on texture estimation, the reaction-diffusion process proposed here yields a seamless transition between the recreated region and the unoccluded image regions. Using AM-FM dominant component analysis, we avoid the ad hoc parameter selection typified with other reaction-diffusion approaches. As a useful example, we focus on the repair of broken, occluded fingerprints. We also treat several exemplary natural textures to demonstrate the technique's generality     

Extreme learning machine with multi-scale local receptive fields for texture classification

Texture classification is a challenging task due to the wide range of natural texture types and large intra-class variations in texture images, such as different rotations, scales, positions and lighting conditions. Many existing methods for extracting texture features are designed carefully by user for specific applications. The extracted texture features are then used as input to various classification methods, such as support vector machines, to classify the textures. The system performance greatly depends on the feature extractor. Unfortunately, there is no systematic approach for feature extractor design. In this paper, we propose a method called extreme learning machine with multi-scale local receptive fields (ELM-MSLRF) to achieve feature learning and classification simultaneously for texture classification. In contrast to traditional methods, the proposed method learns the features by the network itself and can be applied to more general applications. Additionally, it is fast and requires few computations. Experiments on the ALOT texture dataset demonstrate the attractive performance of ELM-MSLRF even compared with the state-of-the-art algorithms. Moreover, the proposed ELM-MSLRF achieves the best performance on the NORB dataset.     

Regression on the basis of nonstationary Gaussian processes with Bayesian regularization

We consider the regression problem, i.e. prediction of a real valued function. A Gaussian process prior is imposed on the function, and is combined with the training data to obtain predictions for new points. We introduce a Bayesian regularization on parameters of a covariance function of the process, which increases quality of approximation and robustness of the estimation. Also an approach to modeling nonstationary covariance function of a Gaussian process on basis of linear expansion in parametric functional dictionary is proposed. Introducing such a covariance function allows to model functions, which have non-homogeneous behaviour. Combining above features with careful optimization of covariance function parameters results in unified approach, which can be easily implemented and applied. The resulting algorithm is an out of the box solution to regression problems, with no need to tune parameters manually. The effectiveness of the method is demonstrated on various datasets.     

Noise reduction of image sequences using motion compensation andsignal decomposition

In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filtering, nonstationarities, i.e., parts of the signal that are momentarily not stationary, can be reduced significantly. However, since not all nonstationarities can be accounted for by motion, a motion-compensated signal still contains nonstationarities. An adaptive algorithm based on order statistics is described that decomposes the motion-compensated signal into a noise-free nonstationary part and a noisy stationary part. An RLS filter is then used to filter the noise from the stationary signal. Our new method is experimentally compared with various noise filtering approaches from literature.     

一种基于圆形区块随机增长的多样图约束纹理合成算法

本文提出了一种基于圆形区块随机增长的多样图纹理合成算法改善了扫描线算法所带来的锯齿效应。算法利用高斯分布约束各个输入样图在合成结果中的比例,采用基于梯度引导的泊松平滑处理相邻纹理块之间的过渡区域,并在合成匹配函数中引入结构特征约束。实验结果表明,算法在引入比例约束的同时有效地保持了视觉效果和纹理结构的连续性。     

Exemplar-based image inpainting using adaptive two-stage structure-tensor based priority function and nonlocal filtering

For the exemplar-based image inpainting problem, the filling order and local intensity smoothness are two crucial factors that should be considered carefully. This work gives a new exemplar-based image inpainting method, preventing geometric structures from being destroyed and reconstructing textures well to obtain elegant-looking outputs. For a better filling order, we define a new adaptive two-stage structure-tensor based priority function. To promote the local intensity smoothness, we adopt a non-local way, and at the same time, propose a weighted filter based on a Gaussian-like function to generate the ideal filling patch by combining non-local patches. We compare the proposed method with some recent state-of-the-art image inpainting approaches on different tasks, such as texture and structure synthesis, object removal, and remote sensing images inpainting. Experimental results demonstrate the superiority of the proposed method, both visually and quantitatively.     

随机幅度多项式相位信号的时延估计方法

研究了一类非平稳信号具有随机幅度的多项式相位信号的时延估计问题。充分利用信号的高阶循环平稳性，提出了一种基于信号高阶循环矩的时延估计方法。该方法容易实现，估计精度能有效地抑制加性平稳非高斯和任何高斯噪声。试验结果证明了提出方法的正确性。     

Mutual information-based feature extraction on the time-frequencyplane

A method is proposed for automatic extraction of effective features for class separability. It applies to nonstationary processes described only by sample sets of stochastic signals. The extraction is based on time-frequency representations (TFRs) that are potentially suited to the characterization of nonstationarities. The features are defined by parameterized mappings applied to a TFR. These mappings select a region of the time-frequency plane by using a two-dimensional (2-D) parameterized weighting function and provide a standard characteristic in the restricted representation obtained. The features are automatically drawn from the TFR by tuning the weighting function parameters. The extraction is driven to maximize the information brought by the features about the class membership. It uses a mutual information criterion, based on estimated probability distributions. The framework is developed for the extraction of a single feature and extended to several features. A classification scheme adapted to the extracted features is proposed. Finally, some experimental results are given to demonstrate the efficacy of the method     

基于分块颜色直方图和GWLBP的图像检索算法

为了提高多特征融合图像检索的效果,本文提出了一种基于分块颜色直方图和GWLBP的图像检索算法。算法采用K-means均值聚类对RGB颜色空间进行颜色聚类,再将4×4均匀分块图像分成9个子块,提取每个子块的颜色体积直方图,并赋予不同权值计算颜色特征;利用Gabor滤波器组对输入图像进行不同分辨率和方向滤波,然后将不同方向上局部滤波器输出结果与全局滤波器输出结果的平均值进行比较,并进行二值化,据此提出3种不同的GWLBP算子来提取纹理特征。最后对图像的颜色和纹理特征高斯归一化,采用加权平均来融合颜色和纹理的特征距离。通过实验仿真可知,与其他3种算法相比,本算法对正常和有旋转倾向的图像都有较高的查全率和查准率。     

Active contours driven by local Gaussian distribution fitting energy

This paper presents a new region-based active contour model in a variational level set formulation for image segmentation. In our model, the local image intensities are described by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with a level set function and local means and variances as variables. The energy minimization is achieved by an interleaved level set evolution and estimation of local intensity means and variances in an iterative process. The means and variances of local intensities are considered as spatially varying functions to handle intensity inhomogeneities and noise of spatially varying strength (e.g. multiplicative noise). In addition, our model is able to distinguish regions with similar intensity means but different variances. This is demonstrated by applying our method on noisy and texture images in which the texture patterns of different regions can be distinguished from the local intensity variance. Comparative experiments show the advantages of the proposed method.     

光照突变下融合多类特征的场景分割方法

为解决场景模型在快速光照变化下失效的问题,提出了一种新的前景目标分割方法。该方法共包括三个步骤。首先,利用全局光照函数建立高斯混合模型;其次,提取当前帧中的纹理、ZNCC 及轮廓特征;最后,将提取到的特征分两阶段与高斯混合模型进行融合(第一阶段:融合纹理及ZNCC 特征;第二阶段:融合轮廓特征),得到最终的场景分割结果。实验结果表明:该算法具有较好的鲁棒性,并且相较于基于全局光照建模的方法具有更高的精度值及召回值。     

Texture synthesis: textons revisited.

This paper introduces a technique for synthesizing natural textures, with emphasis on quasiperiodic and structural textures. Textures are assumed to be composed of three components, namely illumination, structure, and stochastic. The contribution of this work is that, in contrast to previous techniques, it proposes a joint approach for handling the texture's global illumination, irregular structure, and stochastic component which may be correlated to the other two components. Furthermore, the proposed technique does not produce verbatim copies in the synthesized texture. More specifically, a top-down approach is used for extraction of texture elements (textons) in which, in contrast to previous texton-based approaches, no assumptions regarding perfect periodicity are made. The structure itself can be modeled as a stochastic process. Consequently, textons are allowed to have irregular and nonidentical shapes. In the synthesis stage, a new nonregular textural structure is designed from the original one that defines the place holders for textons. We call such place holders empty textons (e-textons). The e-textons are filled in by a representative texton. Since e-textons do not have identical shapes, a texton shape-matching procedure is required. After adding the illumination to the structural component, a strictly localized version of a block sampling technique is applied to add the stochastic component. The block sampling technique combined with the addition of the illumination component provides a significant improvement in the appearance of synthesized textures. Results show that the proposed method is successful in synthesizing structural textures visually indistinguishable to the original. Moreover, the method is successful in synthesizing a variety of stochastic textures.