Local linear transforms for texture measurements

The Nth order probability density function for pixels in a restricted neighborhood may be characterized by a set of N histograms (or some corresponding moments) computed along appropriately chosen axes. The projections on those axes are obtained from a local linear transform of the local neighborhood vector. This approach is closely related to fulter bank analysis methods and gives a statistical justification for the extraction of texture properties by means of convolution operators or local matches. Optimal and sub-optimal linear operators are derived for texture analysis and classification. Experimental results indicate that the method is robust, flexible, and that it performs as well as standard co-occurrence based methods for texture classification. The proposed approach enables texture characterization with a lower number of features and it is also computationally more appealing.     

多尺度超像素纹理特征保持与融合的高光谱图像分类

高光谱图像的低空间分辨率特性往往导致全局纹理提取技术难以获取地物要素的精准纹理信息,同时,单一尺度的局部纹理提取技术难以达到有效识别地物的目的。基于此,该文设计了一种多尺度超像素纹理保持与融合(MSuTPF)的高光谱图像分类方法,主要架构如下:首先,利用2D Gabor滤波器对高光谱图像进行多方向与尺度的全局纹理提取,并通过融合各尺度的纹理特征,增强纹理结构表征能力;其次,融合纹理与光谱主成分特征以形成光谱-纹理联合判别特征;再次,采用形状自适应的超分割方法,作用至光谱-纹理联合特征进行局部纹理信息保持与融合,尤其是,为克服超像素邻域像元的隐性不相关问题,该文定义了基于密度最近邻相似性评价准则,使超像素纹理进一步趋于一致性;最后,将各更新的光谱-纹理联合特征输入像素级分类器获取其对应的类标签,并采用多数表决的决策融合机制取得最终分类结果。Indian Pines和Pavia University真实数据集的实验表明,该方法在小样本条件下的分类精度优于基准分类器(SVM)、深度学习方法(GFDN)以及最新的空-谱分类方法(S3-PCA)等8个对比方法,充分证明了该文所提方法的实用性和有效性。     

一种改进的Laplacian SVM的SAR图像分割算法

当有标识的样本数量有限时,Laplacian SVM算法需要加入尽量多的无标识样本,以提高分类精度.但同时当无标识样本数很大时,算法的时间和空间复杂度将难以接受.为了将Laplacian SVM应用于SAR图像分割这样的大规模分类问题中,提出了一种改进的Laplacian支持向量机算法(Improved Laplaci...     

基于局部边缘二值模式的图像检索

在定义局部边缘的基础上提出了局部边缘二值模式(LEBP),并结合Gabor滤波器将其扩展到多分辨率LEBP(MLEBP)。对传统的中心对称局部二值模式(CS-LBP)和方向局部二值模式(D-LBP)进行了改进,新描述符在不增加计算复杂度和提高特征维数的基础上,进一步融入了局部边缘信息。为验证新描述符的性能,采用3个通用的纹理图像库进行图像检索实验。结果表明,结合本文方法,明显提高了传统描述符的分辨能力。     

Image retrieval using VQ-based local gabor feature

A new method of retrieving images from large image databases is proposed. The method is based on vector quantisation (VQ) of local texture information extracted by a Gabor wavelet filter bank at interest points automatically detected in an image. The performance of the proposed method is tested on a trademark image database and compared with the performance of some existing methods     

Why illuminant direction is fundamental to texture analysis

The author shows that directed illumination used in the image acquisition process can act as a directional filter of three-dimensional texture. An image model of texture is presented which, given the illuminant vector, may be used to predict the directional characteristics of image texture. Simulations and the results of laboratory experiments are presented that confirm the predicted directional filtering effects. The image model is used to predict the output of a directional texture measure: Laws' L5E5 operator (K.I. Laws, 1980), Empirical results using four samples of isotropic texture confirm that the operator's output is significantly affected by changes in the angle of tilt of the illuminant. They also show that the model provides a good basis for predicting the behaviour of such operators. Finally the effect of changes in illuminant tilt on the distributions of the operator for two isotropic textures are presented. These results show that considerable misclassification would result in using an L5E5-based classifier if the illuminant tilt angle were changed between training and classification sessions     

The effect of speckle filtering on scale-dependent textureestimation of a forested scene

Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forest     

Image block classification using stochastic image segmentation

The authors propose a new image block classification method. The proposed algorithm incorporates image context into the classification via pixel-based segmentation. To obtain a segmented image they adopt the stochastic model-based unsupervised image segmentation algorithm. Since the block classifier considers the grey level distribution in the block, it can differentiate edges from textures. Also, since the segmentation is executed independently at each small block, a parallel processor can be applied to obtain a real-time block classification     

火箭发动机缺陷区域检测方法研究

固体火箭发动机的脱粘检测采用超声波聚焦探头反射回波法,但对于旋压壳体发动机,由于发动机表面有旋压纹理,造成脱粘图像产生纹理噪声,因此提出图像滤波的处理方法,有效去除脱粘图像的纹理噪声和提取脱粘区域。本文以旋压壳体固体火箭发动机为研究对象,并采用自适应的图像分割技术对脱粘图像进行二值化分割,运用种子填充算法计算二值化脱粘区域的面积,与实际缺陷面积相当,满足实际的检测需要。并且其检测分辨率达到了Φ3mm。     

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。     

分数阶微分在图像纹理增强中的应用

为了实现对模糊图像边缘及纹理的增强,使图像的细节信息更加清晰,需要对图像进行锐化增强。现有的增强方法一般是整数阶微分锐化及高通滤波,由于这些方法的处理效果仍不理想,文中引入了分数阶微分算子。分数阶微分不仅可以很好地增强图像的边缘和纹理信息,还可以保留平滑区域信息,抑制较大噪声。研究并分析了Tiansi分数阶微分算子的原理及特点,并对其进行了改进,提出了一种新的分数阶微分算子,可以更好地增强图像的边缘和纹理信息,同时保证图像的亮度不产生大幅度变化,而且可以抑制较小噪声的影响。从定量的角度分析,改进的分数阶微分算子的平均梯度最大可比原图像提高3.8倍。从而验证了改进的分数阶微分算子比整数阶微分算子如Laplace算子及Tiansi分数阶微分算子具有优越性,且算法简单易于实现,可应用于工程中的实时图像处理系统中。     

基于子波分解的多通道神经网络纹理分割方法

描述了一种体现多通道滤波技术的神经网络纹理分割方法，决策神经网络（ＤＢＮ）可提高纹理分类的精度，同时纹理的子波变换降低了图像数据间的相关性，提高了网络的学习效率，实验表明本文提出孤方法分类误差较低，获得了令人满意的纹理分割效果。     

Generalized symmetric extension for size-limited multirate filterbanks

A multirate (MR) filter bank is called size-limited if the total number of output samples equals the number of input samples. A method called symmetric extension improved performance in subband image compression systems compared to the earlier method of circular convolution. However, the symmetric extension method was developed only for two-band uniform filter banks, and required even-length linear phase analysis filters. The authors generalize the symmetric extension method to the M-band, possibly nonuniform filter banks, where M=/>2. The length restriction on the analysis filters is relaxed.     

Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures

As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures. It is demonstrated by the experiments that our proposed Bayesian classifier significantly improves the texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.     

Low-Order IIR Filter Bank Design

The advantage of infinite-impulse response (IIR) filters over finite-impulse response (FIR) ones is that the former require a much lower order (much fewer multipliers and adders) to obtain the desired response specifications. However, in contrast with well-developed FIR filter bank design theory, there is no satisfactory methodology for IIR filter bank design. The well-known IIR filters are mostly derived by rather heuristic techniques, which work in only narrow design classes. The existing deterministic techniques usually lead to too high order IIR filters and thus cannot be practically used. In this paper, we propose a new method to solve the low-order IIR filter bank design, which is based on tractable linear-matrix inequality (LMI) optimization. Our focus is the quadrature mirror filter bank design, although other IIR filter related problems can be treated and solved in a similar way. The viability of our theoretical development is confirmed by extensive simulation.     

Texture Segmentation and Edge Location of Regions in Multiband Images

Many texture classification schemes require an excessively large image area for texture analysis, use a large number of features to represent each texture or are computationally very demanding. This paper presents a segmentation method considering all image bands information that can be used on natural or synthetic texture, soft or rough motifs, it permits also distinction of different textures with few changes on the same type of patterns. The approach uses a proposed coefficient, CVE; to estimate region limits that can be used for very small to very large regions and permits correct real time texture bounder classification. The space positions among the pixels on the texel are considered. The bands or color informations are combined considering they mean value and its standard deviation by the new coefficient. This scheme is computationally very efficient and it is suitable for color texture, medical analysis or satellite image recognition. It can be used of all type of texture because the rules of what will be identified are completely given by the used and adapted to each situation.     

Texture feature analysis using a gauss-Markov model in hyperspectral image classification

Texture analysis has been widely investigated in the monospectral and multispectral imagery domains. At the same time, new image sensors with a large number of bands (more than ten) have been designed. They are able to provide images with both fine spectral and spatial sampling, and are called hyperspectral images. The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector texture model, using a Gauss-Markov random field (MRF). The MRF parameters allow the characterization of different hyperspectral textures. A possible application of this work is the classification of urban areas. These areas are not well characterized by radiometry alone, and so we use the MRF parameters as new features in a maximum-likelihood classification algorithm. The results obtained on Airborne Visible/Infrared Imaging Spectrometer hyperspectral images demonstrate that a better classification is achieved when texture information is included in the analysis.     

Rotation and gray-scale transform-invariant texture classificationusing 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%