CBIR中基于纹理特征的检索概述

本文简要叙述了图像检索的发展和目前研究的热点。基于纹理的图像检索是进行图像检索的主要方法之一，本文主要概述的提取纹理特征的方法。在多种提取纹理特征的方法中，Gabor小波变换和纹理谱的方法更加符合人类的视觉特征，本文对这两种方法进行了理论上的探讨。     

Influence of crystallographic textures on tensile properties of 316L austenitic stainless steel

Role of cold rolling texture on the tensile properties of the cold rolled and cold rolled and annealed AISI 316L austenitic stainless steel is described here. The solution-annealed stainless steel plates were unidirectionally cold rolled to 50, 70 and 90% of reduction in thickness. The cold rolled material was annealed at 500–900 °C annealing temperatures. X-ray diffraction technique was employed to study the texture evolution in cold rolled as well as cold rolled and annealed conditions. The texture components that evolved were translated into slip transmission number ‘λ’ and Schmid factor ‘μ’. These two parameters were correlated with the tensile properties of the material. The tensile properties were evaluated under all processing conditions. Softening of the cold rolled material was observed after annealing with increasing annealing temperatures. From the stress–strain curves, strain hardening coefficient ‘n’ and strain hardening rate ‘θ’ were determined. It was found that the effect of texture on tensile behaviour could be understood clearly by strain hardening rate. Out of the two parameters, ‘n’ and ‘θ’, strain hardening rate was found to be more sensitive to type of texture in the material.     

基于Gabor小波变换的汉字识别方法

基于Gabor滤波器时频局域性和方向选择性对应在汉字图像上即是对笔划宽度和方向的选择这一认识,考虑到笔划宽度多峰值、笔划是方向的特点,利用二维Gabor小波的多分辨率特性,对汉字图像进行了多尺度多方向分析,提取多个子平面的滤波器输出系数作为统计特征,实现了字符图像的高性能识别,为开展低质量、低分辨率的汉字字符图像识别研究提供了新方法。实验结果表明,用这种方法识别低分辨率的字符图像,效果优于其他方法。     

Hybrid Ultrasonic TOFD Imaging of Weld Flaws Using Wavelet Transforms and Image Registration

Ultrasonic time of flight diffraction (TOFD) is an effective weld crack inspection technique. Due to the intensity of diffraction wave is rather weak compared with the lateral wave and the bottom echo wave, thus the signal-to-ratio (SNR) of TOFD image is low. A new dichotomous method is comprised of two steps that contains wavelet shrinkage and image registration is proposed in this paper to reduce the noise and improve the resolution of TOFD images as well. In order to evaluate the reliability of our proposed method in this paper, we have established the experiment system, and sampled a number of TOFD data with random distribution of noise characteristics. We adopted one-dimension wavelet transform and two-dimension wavelet transform in the very beginning of the first step of the proposed algorithm respectively. The SNR of the result obtained in this step is improved significantly compared with the classic algorithms. Next, the image registration is applied. After the registered images have been added to form a new one, then it comes to the final result that shows not only the SNR but also the definition of the image is enhanced effectively.     

一种基于Gabor滤波器的超声图像边缘检测方法

针对检测超声图像的边缘问题，介绍了一种基于Gabor奇部滤波器进行边缘检测的综合方法。为了去除图像噪声，首先进行基于小波变换和中值滤波的降噪处理，然后利用高斯函数平滑图像。在边缘检测过程中，使用Gabor奇部滤波器检测边缘。最后，使用非最大值抑制得到最终结果。结果表明该方法是对超声图像进行边缘检测的一种有用方法。当然，该方法也具有普遍性，可以应用到其他图像。     

一种新颖的虹膜识别方法

提出一种基于多纹理特征融合的新颖虹膜识别方法。该方法对虹膜图像做Gabor小波变换提取不同分辨力不同方向下的纹理特征作为虹膜的全局特征,在滤波后的子窗口图像上运用灰度级共现矩阵(COM)提取虹膜的局部特征。通过加权欧几里德距离和最小距离分别对全局特征和局部特征进行分类识别。设计了FIS(模糊推理系统)特征融合分类方法来提高虹膜识别的鲁棒性。实验结果表明本方法有效可行,可以达到98.5%的识别率,并在保持1.4%较低的FRR(拒绝率)的同时可以使FAR(误识率)减少到0.1%。     

A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition (VTR) is an important research topic due to its significance in intelligent transportation systems. However, recognizing vehicle type on the real-world images is challenging due to the illumination change, partial occlusion under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier (SKNNC). Further the more specific vehicle type such as bus, truck, sedan or van is recognized by the second stage classification, which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier (KSRC). A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR. To improve VTR efficiency, the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image. Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Automated defect detection system using wavelet packet frame and Gaussian mixture model

This paper proposes an approach for automated defect detection in homogeneous textiles using texture analysis. The texture features are extracted by the wavelet packet frame decomposition followed by the Karhunen-Loève transform. The texture feature vector for each pixel is used as an input to a Gaussian mixture model that determines whether or not each pixel is defective. The parameters of the Gaussian mixture model are estimated with nondefective textile images in supervised defect detection. An approach for unsupervised defect detection is also presented that can identify the heterogeneous subblocks on the basis of the Kullback-Leibler divergence between two Gaussian mixtures. The proposed method was evaluated on 25 different homogeneous textile image pairs, one of each pair with a defect and the other with no defect, and was compared with existing methods using texture analysis. The experimental results yielded visually good segmentation and an excellent detection rate with a low false alarm rate for both supervised and unsupervised defect detection. This confirms the validity of the proposed approach for automated defect detection and localization.     

Segmentation of SAR images using the wavelet transform

Multiresolution representation of images using the wavelet transform is a new approach for the analysis of image information content. The transform can be computed efficiently by a pyramidal algorithm based on convolution with quadrature mirror filters. The result is a set of subband images which consists of a lower resolution version of the original image and a sequence of detail images containing higher frequency spectral information. We used this representation for the supervised segmentation of polarimetric SAR images of the San Francisco Bay area acquired by the airborne JPL system for identifying various terrain covers. Since the wavelet transform generates the localized spatial and spectral information simultaneously, detailed knowledge of the texture variations within an image can be extracted from the data in the spectral subbands. The segmentation algorithm developed in this paper is formulated by taking into consideration both the intensity and the texture information. For polarimetric SAR images, the classification accuracy can be enhanced, if the combined data from copolarized and cross-polarized images are used in the discrimination process. In contrast to other texture segmentation approaches, this algorithm does not require extensive calculations.©1993 John Wiley & Sons Inc     

基于Gabor滤波器的指纹图像增强

通过对传统指纹图像增强算法的研究,提出一种基于Gabor滤波的指纹图像增强算法。根据指纹纹线间距均匀且局部平行的特点,建立了用于指纹图像增强的Gabor滤波函数的物理模型,并利用二维Gabor滤波器可分解为正交方向的一维高斯带通滤波器和一维高斯低通滤波器的特点,将二维滤波分解为两次一维滤波,从而解决了计算量过大的问题,降低了算法的复杂度。实验表明该算法具有良好的增强指纹图像的脊线和抑制噪声的作用。     

基于高光谱成像技术的中医舌纹分析算法

针对中医舌诊现代化中使用彩色或灰度图像进行舌纹分析困难的问题,采用高光谱成像系统代替数码相机进行舌图像采集,并提出基于Gabor滤波器的舌纹分析算法.这一算法充分利用高光谱舌图像图、谱两方面的信息进行舌纹分析,并根据特征向量之间归一化的距离对部分典型舌纹进行初步分类.在对474例具有典型舌纹的高光谱舌图像的分类中,除了在来蛇纹与去蛇纹、四直纹与曲虫纹、太阳纹和龟纹、左右撇纹和锯齿纹之间有一定的误分外,整体具有较好的分类精度.这些分类结果表明,基于高光谱图像的舌纹分析方法明显优于基于灰度图像的舌纹分析方法.     

Sleeping Beauties in science

A ‘Sleeping Beauty in Science’ is a publication that goes unnoticed (‘sleeps’) for a long time and then, almost suddenly, attracts a lot of attention (‘is awakened by a prince’). We here report the -to our knowledge- first extensive measurement of the occurrence of Sleeping Beauties in the science literature. We derived from the measurements an ‘awakening’ probability function and identified the ‘most extreme Sleeping Beauty so far’. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于谱直方图表示和支持向量机的纹理分类

在纹理分类中采用谱直方图表示(SHR),每个图像窗表示一个包含滤波后图像直方图的特征向量,而直方图是图像谱表示的连接桥梁.在滤波器选择算法之前,结合每个图像分块和滤波器的独立谱表示和直方图,可以获得更加低层的局部特征.最后,时所有独立滤波器采用滤波器选择算法来得到所需的少量滤波器.为了保证分类的可靠性,选择高斯径向基函数(RBF)进行谱直方图表示,采用支持向量机(SVMs)作为分类函数.对本文方法和其它两种方法:Gabor滤波和独立成分分析(ICA)进行了纹理分类和脸部识别的比较实验.实验结果表明,本文方法具有更高的分类准确性,也证明了SVMs优秀的泛化能力.     

Automatic facial expression recognition based on pixel‐pattern‐based texture feature

PCA, ICA, and Gabor wavelet are considered as the important and powerful face representation methods. In this article, we propose a new approach for face representation, which is called a pixel‐pattern‐based texture feature (PPBTF) and apply it to the real‐time facial expression recognition. A gray scale image is transformed into a pattern map where edges and lines are used for characterizing the facial texture information. Based on the pattern map, a feature vector is comprised of the numbers of the pixels belonging to each pattern. We use the image basis functions obtained by principal component analysis as the templates for pattern matching. Adaboost and Support Vector Machine are adopted to classify facial expression. Extensive experiments on the Cohn‐Kanade Database, PIE Database, and DUT Database illustrate that the PPBTF is quite effective and insensitive to illumination. The comparison with Gabor show the PPBTF is speedy. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 253–260, 2010     

基于眼底高分辨力毛细血管图像的身份识别

视网膜识别技术稳定性好,安全性高.常规眼底相机获得的图像分辨力低,给图像编码和模式匹配带来困难,使得基于常规眼底相机的视网膜身份识别技术难以推广应用.利用自适应光学眼底视网膜高分辨力成像技术采集眼底视网膜高分辨力毛细血管图像,经归一化和滤噪后,采用Gabor滤波算法进行特征提取,汉明距离进行模式匹配,对实验样本进行了准确的身份识别,验证了该技术的有效性,拓展了自适应光学眼底视网膜高分辨力成像技术的应用范围,在某些要害部门可能发挥重要的安保作用.     

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.     

Towards a theory of granular plasticity

A theory of granular plasticity based on the time-averaged rigid-plastic flow equations is presented. Slow granular flows in hoppers are often modeled as rigid-plastic flows with frictional yield conditions. However, such constitutive relations lead to systems of partial differential equations that are ill-posed: they possess instabilities in the short-wavelength limit. In addition, features of these flows clearly depend on microstructure in a way not modeled by such continuum models. Here an attempt is made to address both short-comings by splitting variables into ‘fluctuating’ plus ‘average’ parts and time-averaging the rigid-plastic flow equations to produce effective equations which depend on the ‘average’ variables and variances of the ‘fluctuating’ variables. Microstructural physics can be introduced by appealing to the kinetic theory of inelastic hard-spheres to develop a constitutive relation for the new ‘fluctuating’ variables. The equations can then be closed by a suitable consitutive equation, requiring that this system of equations be stable in the short-wavelength limit. In this way a granular length-scale is introduced to the rigid-plastic flow equations.