Rotation invariant classification of rough surfaces

Rotation of a rough, textured surface will not produce a simple rotation of the image texture. It follows that where image texture is a function of surface topography, existing rotation invariant texture classification algorithms are not robust to surface rotation. The effect of surface rotation on the observed image is analysed using an existing theory, a novel scheme to stabilise the classification accuracy is proposed and evaluated. The scheme uses photometric stereo to estimate the surface derivatives, which are then used as the input to a classifier. Simulations indicate that, where the level of image noise is moderate or low, the approach is successful in maintaining classification accuracy. Furthermore, in some circumstances, the extra information used by the algorithm allows classification accuracy superior to that based on one image alone, even without rotation     

Robust image hash in Radon transform domain for authentication

Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size.     

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.     

基于Radon和解析Fourier-Mellin变换的篡改图像盲检测算法

复制粘贴（Copy-Move）是一种极为常见的图像篡改方式。为了快速有效地检测图像经过旋转、缩放等操作后的篡改图像,本文提出了一种基于Radon和解析Fourier-Mellin变换的篡改图像盲检测方法。文章首先对图像进行分块,之后将图像块进行Radon和解析Fourier-Mellin变换,并提取计算变换结果后的矩特征值,最后计算矩特征值的相关性。本文算法不需要对灰度图像进行二值化与归一化处理,而是直接从图形的Radon变换与Fourier-Mellin变换的结果中提取不变特征,理论分析与实验结果表明,本文提出算法的检测结果优于基于正交矩的检测方法,而且对均值为0的白噪声的鲁棒性显著高于基于正交矩的检测方法。      

Robust rotation-invariant texture classification: wavelet, Gaborfilter and GMRF based schemes

Three novel feature extraction schemes for texture classification are proposed. The schemes employ the wavelet transform, a circularly symmetric Gabor filter or a Gaussian Markov random field with a circular neighbour set to achieve rotation-invariant texture classification. The schemes are shown to give a high level of classification accuracy compared to most existing schemes, using both fewer features (four) and a smaller area of analysis (16×16). Furthermore, unlike most existing schemes, the proposed schemes are shown to be rotation invariant demonstrate a high level of robustness noise. The performances of the three schemes are compared, indicating that the wavelet-based approach is the most accurate, exhibits the best noise performance and has the lowest computational complexity     

An improved LBP algorithm for texture and face classification

Local Binary Pattern (LBP) has achieved great success in texture classification due to its accuracy and efficiency. Traditional LBP method encodes local features by binarying the difference in local neighborhood and then represents a given image using the histogram of the binary patterns. However, it ignores the directional statistical information. In this paper, some directional statistical features—including the mean and standard deviation of the local absolute difference—are integrated into the feature extraction to improve the classification ability of the extracted features. In order to reduce estimation errors of the local absolute difference, we further utilize the least square estimate technique to optimize the weight and minimize the local absolute difference, which leads to more stable directional features. In addition, a novel rotation invariant texture classification approach is presented. Experimental results on several texture and face datasets show that the proposed approach significantly improves the classification accuracy of the traditional LBP.     

A Fast Algorithm for Recognizing Translated, Rotated, Reflected, and Scaled Objects From Only Their Projections

The subject of 2-D and higher dimensional object recognition finds widespread applications in areas such as image registration and pattern recognition. Radon transform is one technique used for efficient object matching (e.g., and ). However, so far as we know, no results have been obtained that solves the recognition problem completely in the projection domain due to coupling of transform parameters. We develop a novel method for such parameter decoupling and an improved phase correlation method for accurate practical shift estimation, resulting in a fast matching algorithm based on projection data only. Simulation results show that the proposed algorithm is much faster than similar state-of-the-art approaches such as that in with comparable estimation accuracy.      

Localized texture processing in vision: analysis and synthesis inthe Gaborian space

Recent studies of cortical simple cell function suggest that the primitives of image representation in vision have a wavelet form similar to Gabor elementary functions (EF's). It is shown that textures and fully-textured images can be practically decomposed into, and synthesized from, a finite set of EF's. Textured-images can be synthesized from a set of EF's using image coefficient library. Alternatively, texturing of contoured (cartoon-like) images is analogous to adding chromaticity information to contoured images. A method for texture discrimination and image segmentation using local features based on the Gabor approach is introduced. Features related to the EF's parameters provide efficient means for texture discrimination and classification. This method is invariant under rotation and translation. The performance of the classification appears to be robust with respect to noisy conditions. The results show an insensitivity of the discrimination to relatively high noise levels, comparable to the performances of the human observer.     

一种基于投影的小波矩及其在图像识别中的应用

该文提出了一种基于图像投影的小波不变量算法。首先将图像投影到一维空间,然后引入一种平移和比例不变自适应一维小波变换,利用该变换获得图像的平移和比例不变特征。由于图像的旋转导致特征矩阵发生行之间的循环平移,该文利用奇异值分解进行研究,得到的奇异值向量具有图像的3个不变性。实验分析表明,本文方法具有较好的图像识别效果。     

Robust rotation-invariant texture classification using a model based approach

In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space.     

旋转人脸的模板匹配定位与旋转角度估算方法

本文实现了一种依靠非旋转的人脸模板对平面内旋转的人脸图象进行匹配定位与角度估算的方法。相对于通过由不同旋转角度的模板构成的集合中的成员分别作用于目标图象进行匹配定位的方法，本文的方法减轻了计算负担。而且本文将圆投影法与Zernike矩法相结合实现了旋转人脸图象的定位与旋转角度估算。其匹配的准确性比较高，而且具有良好的抗噪声特性。     

无参数化的中心投影描绘子

中心投影类算法需对所得闭曲线参数化,这种参数化一方面增加了计算量,另一方面参数化的精度影响特征的提取效果。为此,本文提出一种无参数化的中心投影描绘子,其实质相当于将目标利用中心投影变换转化为闭曲线,然后将闭曲线所围区域进行白化变换。这种白化变换消除了仿射变换中的斜切,将仿射变换转化为仅有旋转和伸缩,最后利用Fourier描绘子提取不变特征。与现有算法不同处在于,这里的白化变换以公式形式给出,无需参数化。实验表明,所提描绘子的识别率接近100%,并且在抗噪性能上优于MSA和AMIs算法。     

Rotation-invariant texture classification using a two-stage waveletpacket feature approach

A novel two-stage wavelet packet feature approach for classification of rotated textured images is discussed. In the first stage, a set of sorted and dominant wavelet packet features is extracted from a texture image and a Mahalanobis distance classifier is employed to output N best classes. In the second stage, another set of wavelet packet features is extracted from the polarised form of the sample texture image and the most dominant wavelet packet features are selected and passed to the radial basis function (RBF) classifier with the N best classes to output the final matched class. Experimental results, based on a large sample data set of twenty distinct natural textures selected from the Brodatz album with different orientations, show that the proposed method outperforms the similar wavelet methods and the other rotation invariant texture classification schemes, and an overall accuracy rate of 91.4% was achieved     

基于轮廓线曲率特征的人脸分类及识别

文中提出一种人脸分类及识别方法。在对人脸图像进行预处理之后,提取出人脸的轮廓,再细化,去除边缘噪声,得到人脸的轮廓线。对此轮廓线逐点计算曲率得到曲率线。将不同的曲率线之间的互相关函数定义为曲率线之间的相似性,并由此对人脸进行分类和识别。由于曲率几何特征明显,具有位移和旋转不变性,所提出的方法有较好的鲁棒性,并且得到了良好的分类效果。     

Efficient analysis of hybrid directional lifting technique for satellite image denoising

Satellite images are often corrupted by noise in the acquisition and transmission process. While removing noise from the image by attenuating the high frequency image components, it removes some important details as well. In order to improve the visual appearance and retain the useful information of the images, an effective denoising technique is required to reduce the noise level. For denoising, many researches exploit the directional correlation in either spatial or frequency domain. However, the orientation estimation for directional correlation becomes inefficient and error prone in noised circumstances. This paper proposes a new hybrid directional lifting (HDL) technique for image denoising that involves pixel classification and orientation estimation, along with adding small amount of noise, in order to improve the performance efficiency of the technique. Experimental results show that the HDL technique improves both peak signal to noise ratio and visual quality of images with rich textures.     

Localized 2-D filter-based linear coherent noise attenuation

A novel localized two-dimensional (2-D) filter is proposed. The proposed filter derived from the frequency-wavenumber filter and Radon transform filter, with the filtering operation applied at the stage of Fourier projection, has good local property and less filtering distortion. An example of the proposed method to attenuate linear coherent noise in a seismic image is given. Comparisons of the results between our method and the conventional 2-D filters (including frequency-wavenumber filter and Radon transform filter) show that the new method outperforms both frequency-wavenumber method and Radon transform method.     

一种基于小波域的指纹图像增强算法

面对采集的指纹图像质量较差的问题,提出一种在小波域对指纹图像滤波增强算法。该算法在小波域进行Ga-bor滤波增强,通过Radon变换后的投影估计Gabor滤波的方向和频率,对滤波增强后的子图进行小波重构得到最后的结果,有效地抑制高频扰动对指纹图像质量的影响,提高运算速度和准确性。通过对FVC2000指纹库中的部分低质量指纹图像进行增强,表明该算法对指纹图像的增强效果明显,并且处理速度较快。