一种快速旋转不变图像检索新方法*

提出了一种新型快速旋转不变图像检索新方法.该方法首先对图像进行傅里叶变换和功率谱分解,提取功率谱的扇形区域能量和环形区域能量参数,并将其均值和标准差作为图像纹理特征.然后,利用谱能量分布特征把纹理的主方向旋转到0°,提取旋转后图像的共生矩阵参数和小波分解各子带图像统计参数作为基本特征.利用所提出的特征提取方法在两组分别包含25类单色自然纹理的图像库上进行检索试验.结果表明,该方法获得了良好的检索效果.     

Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis

In this paper a novel rotation invariant multi-resolution based texture retrieval technique is proposed. The rotation invariance is achieved by aligning the direction of maximum variation of intensity gradient (defined as principal texture direction) along the reference axis. The principal direction is determined using eigen value analysis of gradient image. Wavelet transform based techniques are applied on the rotated image. The independent representation of textural energies along various directions enhances the retrieval performance over the existing rotation invariant wavelet based techniques which achieve rotation invariance by averaging the direction sensitive components. Extensive experiments on Brodatz database support this postulate.     

Rotation-invariant texture image retrieval using rotated complex wavelet filters.

This paper proposes a novel approach for rotation-invariant texture image retrieval by using set of dual-tree rotated complex wavelet filter (DT-RCWF) and DT complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. Two-dimensional RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Robust and efficient isotropic rotationally invariant features are extracted from DT-RCWF and DT-CWT decomposed subbands. This paper demonstrates the effectiveness of this new set of features on four different sets of rotated and nonrotated databases. Experimental results indicate that the proposed method improves retrieval accuracy from 83.17% to 93.71% on a small size (208 images) nonrotated database D1, from 82.71% to 90.86% on a small size (208 images) rotated database D2, from 72.18% to 76.09% on a medium-size (640 images) rotated database D3, and from 64.17% to 78.93% on a large size (1856 images) rotated database D4, compared with the discrete wavelet transform-based approach. New method also retains comparable levels of computational complexity.     

Texture image retrieval using new rotated complex wavelet filters.

A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWFs) are designed with complex wavelet filter coefficients, which gives texture information strongly oriented in six different directions (45 degrees apart from complex wavelet transform). The 2-D RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for texture image retrieval by using a set of dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree-complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. The information provided by DT-RCWF complements the information generated by DT-CWT. Features are obtained by computing the energy and standard deviation on each subband of the decomposed image. To check the retrieval performance, texture database D1 of 1856 textures from Brodatz album and database D2 of 640 texture images from VisTex image database is created. Experimental results indicates that the proposed method improves retrieval rate from 69.61% to 77.75% on database D1, and from 64.83% to 82.81% on database D2, in comparing with traditional discrete wavelet transform based approach. The proposed method also retains comparable levels of computational complexity.     

Log-polar wavelet energy signatures for rotation and scale invariant texture classification

Classification of texture images is important in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n /spl middot/ log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.     

二元树复小波变换及其在图象方向滤波中的应用

复小波变换虽然具有良好的方向选择性和平移不变性 ,但不具备完全重构性条件 ,而二元树复小波变换(DTCWT)正好解决了这一难题 .在分析二元树复小波分解后的 12个高频子带方向性的基础上 ,利用其良好的方向选择性提出了一种对线形纹理图象进行增强滤波的方法 .该方法借助于小波变换域的方向解析性 ,在各子带中保留图象中各局部主方向的信息而滤除其他方向的噪声 .利用该方法进行滤波还可以避免对信号和噪声频率特性和统计特性进行估计 ,从而大大减小了滤波的复杂程度 .以指纹图象为例的实验结果表明 ,该方法效果较好 ,便于实现 ,尤其适用于噪声特性复杂的纹理图象的滤波 .     

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.     

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

A novel approach for content-based texture image retrieval system using fuzzy logic classifier is proposed in this paper. The novelty of this method is demonstrated by handling the complexity issues in texture image retrieval arising from rotation and scale variance. These issues are divided into four groups as non rotated non scaled, rotation invariant, scale invariant and scale and rotation invariant texture retrieval for retrieval performance analysis. Features of texture images are obtained using discrete wavelet transform based statistical features and gray level co-occurrence matrix based co-occurrence features. The fuzzy logic classifier is developed with Gaussian membership function with mean and standard deviations of the features. The retrieval performance improvement is carried out by considering various combinations of the features. The average retrieval rates for the four issues have been achieved at 99.40% with 40 features, 91% with 80 features, 65.2% with 40 features, and 63.4% with 65 features respectively. This method outperforms the existing methods in terms of average retrieval rate. The scale and rotation invariant texture retrieval is an incomparable work that has been demonstrated in the present paper.     

Capture and fusion of 3d surface texture

Image fusion is a process that multiple images of a scene are combined to form a single image. The aim of image fusion is to preserve the full content and retain important features of each original image. In this paper, we propose a novel approach based on wavelet transform to capture and fusion of real-world rough surface textures, which are commonly used in multimedia applications and referred to as3D surface texture. These textures are different from 2D textures as their appearances can vary dramatically with different illumination conditions due to complex surface geometry and reflectance properties. In our approach, we first extract gradient/height and albedo maps from sample 3D surface texture images as their representation. Then we measure saliency of wavelet coefficients of these 3D surface texture representations. The saliency values reflect the meaningful content of the wavelet coefficients and are consistent with human visual perception. Finally we fuse the gradient/height and albedo maps based on the measured saliency values. This novel scheme aims to preserve the original texture patterns together with geometry and reflectance characteristics from input images. Experimental results show that the proposed approach can not only capture and fuse 3D surface texture under arbitrary illumination directions, but also has the ability to retain the surface geometry properties and preserve perceptual features in the original images.     

In depth analysis and evaluation of saliency-based color image indexing methods using wavelet salient features

Nowadays, most of the research works in the area of image retrieval try to build an image signature by considering the image as a whole. In this paper, we proposed an alternative based on the detection of some salient points in the image. For this purpose, we propose a new efficient salient point detector based on a wavelet transform. The efficiency of our detector lies in the representation of the wavelet coefficients by a zerotree data structure and by a saliency formulation that does not favor any direction. Thus, the detected salient points are located on sharp region boundaries whatever their direction. From the detected salient points, we build a color/texture signature by using jointly the well-known color correlogram extended to salient features and rotated wavelet filter responses. Experimental results conducted by adopting a global salient approach and a local salient approach show the effectiveness of the proposed scheme.     

Texture classification using partial differential equation approach and wavelet transform

Textures and patterns are the distinguishing characteristics of objects. Texture classification plays fundamental role in computer vision and image processing applications. In this paper, texture classification using PDE (partial differential equation) approach and wavelet transform is presented. The proposed method uses wavelet transform to obtain the directional information of the image. A PDE for anisotropic diffusion is employed to obtain texture component of the image. The feature set is obtained by computing different statistical features from the texture component. The linear discriminant analysis (LDA) enhances separability of texture feature classes. The features obtained from LDA are class representatives. The proposed approach is experimented on three gray scale texture datasets: VisTex, Kylberg, and Oulu. The classification accuracy of the proposed method is evaluated using k-NN classifier. The experimental results show the effectiveness of the proposed method as compared to the other methods in the literature.     

基于不完全小波树型结构的图像纹理特征研究

图像纹理特征的研究作为一个热点和难点问题为图像检索、景物识别和目标定位等应用领域提供了一种重要途径。近年来,小波域的图像处理和分析受到关注,但基于小波域图像特征的研究相对滞后。通过分析图像纹理及其空间域特性,同时对图像不完全树型小波结构的分解过程和存在的问题进行讨论,对基于不完全小波树型结构的纹理特征进行研究,给出了小波域描述图像纹理的若干特征,提出了描述图像方向性的两个特征,同时对这些特征在图像检索中的应用过程和应用范围进行了说明。实验结果验证了所提出纹理特征的有效性。     

小波域多方向信息融合的纹理图像检索

由于能提供较多的方向信息,双树复小波变换在纹理图像检索中的检索率高于传统小波变换,但传统小波变换与双树复小波变换得到的方向子带不同。针对该问题,提出一种融合传统小波和双树复小波变换的一阶统计信息从而提取特征进行纹理图像检索的方法。对Brodatz图像库的仿真实验表明,该方法优于传统小波和双树复小波方法。     

基于方向极傅里叶频谱2DPCA 的尾迹检测

针对航空图像中的水面尾迹, 提出了一种基于方向极傅里叶频谱二维主成分分析(Two-dimensional principal component analysis, 2DPCA)的尾迹自动检测算法. 该方法根据子图像的纹理方向, 对傅里叶频谱进行极坐标变换, 使得到的方向极傅里叶频谱具有平移和旋转不变性. 相对于文献中对极频谱的直接划分作为纹理特征, 本文对它进行一次列二维主成分分析, 一次行二维主成分分析和两次二维主成分分析, 实验结果表明本文方法具有更高的分类识别率, 其中两次二维主成分分析的分类识别率最高. 对40幅图像的测试结果表明, 本文的方法能够有效地自动检测航空图像中的水面尾迹纹理.     

一种快速图像融合新算法

针对小波变换的特点,研究了图像融合规则,定义了图像小波方向导数和方向能量、图像纹理和轮廓的小波特征度,并提出了一种全新的图像快速融合算法.算法融合判据有效,结构设计简单.通过对多聚焦和遥感图像融合实验表明:该算法能有效地融合源图像的信息,保持源图像的纹理和轮廓特征.与同类优秀算法相比,主观评价效果相近,客观评价效果更高,运算时间更短.     

结合微分特征和Haar小波分解的鲁棒纹理表达

针对传统的局部二值模式算子缺乏像素间深层次的相关性信息，且对图像中常见的模糊及旋转变化的鲁棒性较差的问题，提出了一种结合微分特征和Haar小波分解的鲁棒纹理表达算子。在微分特征通道上，通过各向同性的微分算子提取图像中的一阶和二阶微分特征，使图像的微分特征在本质上具有旋转不变性且对图像模糊具有较强的鲁棒性；基于小波变换在时域和频域同时具有良好的局部化的特点，在小波分解特征提取通道上采用多尺度的二维Haar小波分解提取图像中的模糊鲁棒特征；最后，串联两个通道上的特征直方图来描述图像的纹理特征。在特征判别性实验中，该算子在较复杂的UMD、UIUC和KTH-TIPS纹理库上的准确率分别达到了98.86%、98.2%和99.05%，与中值稳健扩展局部二值模式（MRELBP）算子相比，准确率分别提高了0.26%、1.32%和1.12%；在对旋转变化和图像模糊的鲁棒性分析实验中，该算子在仅存在旋转变化的TC10纹理库上的分类准确率达到99.87%，在添加了不同程度高斯模糊的TC11纹理库上的分类准确率降幅仅为6%；在计算复杂度实验中，该算子的特征维度仅为324维，在TC10纹理库上的平均特征提取时间为30.9 ms。实验结果表明，结合微分特征和Haar小波分解的方法具有很强的特征判别性，对旋转和模糊的鲁棒性较强，同时具有较低的计算复杂度，在样本数据较少的场合具有很好的适用性。     

结合微分特征和Haar小波分解的鲁棒纹理表达

针对传统的局部二值模式算子缺乏像素间深层次的相关性信息,且对图像中常见的模糊及旋转变化的鲁棒性较差的问题,提出了一种结合微分特征和Haar小波分解的鲁棒纹理表达算子。在微分特征通道上,通过各向同性的微分算子提取图像中的一阶和二阶微分特征,使图像的微分特征在本质上具有旋转不变性且对图像模糊具有较强的鲁棒性;基于小波变换在时域和频域同时具有良好的局部化的特点,在小波分解特征提取通道上采用多尺度的二维Haar小波分解提取图像中的模糊鲁棒特征;最后,串联两个通道上的特征直方图来描述图像的纹理特征。在特征判别性实验中,该算子在较复杂的UMD、UIUC和KTH-TIPS纹理库上的准确率分别达到了98.86%、98.2%和99.05%,与中值稳健扩展局部二值模式（MRELBP）算子相比,准确率分别提高了0.26%、1.32%和1.12%;在对旋转变化和图像模糊的鲁棒性分析实验中,该算子在仅存在旋转变化的TC10纹理库上的分类准确率达到99.87%,在添加了不同程度高斯模糊的TC11纹理库上的分类准确率降幅仅为6%;在计算复杂度实验中,该算子的特征维度仅为324维,在TC10纹理库上的平均特征提取时间为30.9 ms。实验结果表明,结合微分特征和Haar小波分解的方法具有很强的特征判别性,对旋转和模糊的鲁棒性较强,同时具有较低的计算复杂度,在样本数据较少的场合具有很好的适用性。     

硅太阳能电池纹理缺陷检测

为实现硅太阳能电池纹理缺陷检测,提出一种采用方向可变滤波器组并结合Hough变换的检测方法。通过方向可变滤波器提取图像边缘并采用Hough变换确定纹理方向,采用角度与纹理方向一致的方向可变滤波器滤波,实现消除规则直线纹理,保留纹理缺陷特征。对滤波后的纹理缺陷结果图像采用双阈值法,以确定纹理缺陷所在的位置。和Gabor滤波器及小波滤波器的比较实验结果表明：该方法比前两种方法能更有效地进行硅太阳能电池纹理缺陷检测。