Zernike矩结合Sobel算子的边缘检测

提出了一种亚像素级的噪声图像边缘检测算法。首先使用基于各向异性扩散的非线性正则化Perona-Malik模型,实现对图像的平滑滤波;然后用改进的Sobel算子对图像进行初步边缘检测,将检测到的包括真正的和少数伪边缘点的坐标生成链表记录下来,原灰度图像保留;最后利用Zernike矩进行亚像素级的边缘精确定位。实验结果表明,此算法解决了传统算法中伪边缘点过多和边缘检测结果较宽的问题,图像的质量接近于定位准确度为亚像素级的小波算法。     

一种用于枪声的多级检测识别技术

声音无处不在,声音的检测与识别一直是声音研究领域的重要内容。其中公共场所环境中的突发声音的检测一直是一个难题,本文提出了一种利用短时能量和短时平均过量率以及MFCC和DTW对枪声等特定声音的多级检测算法。实验结果表明,本算法具有较低的计算复杂度,易于实现,而且检测的漏检率和误检率都很理想。     

基于Zernike矩的三维目标多视点特性视图建模

目标的模型化是三维目标识别的一个重要环节。由于从多视点特征法建立的模型能够避免从二维图像中恢复目标的三维结构．在三维目标识别中应用比较广泛。使用镶嵌式多面体分割视觉空间以得到三维目标的多视点投影图像,采用较小的镶嵌单元尺寸以完备地描述目标各种姿态的信息。Zernike矩具有旋转不变性,并能够方便地由各阶矩重建图像。计算多面体各个单元的投影图像的Zernike矩．所需的矩的最高阶次由重建图像与原图像的差别确定,通过聚类方式合并那些图像的矩变化很小的投影对应的几个小单元．每个新单元可以提取一幅特性视图．实现较小数目的多视点特性视图的建模。实验证明了这种方法的可行性。     

Zernike moments and LDP-weighted patches for content-based image retrieval

This paper proposes a new algorithm using global and local features for content-based image retrieval. Global features are extracted using the magnitude of Zernike moments (ZMs). Local features are obtained through local directional pattern (LDP). Generally, LDP is used to extract texture-based features from an image. In this paper, LDP is used to encode both texture and shape information of an image to represent more meaningful features. To encode texture-based features, original image is used to compute the LDP features. To extract shape information from an image, dual-tree complex wavelet transform (DT-CWT) is applied on image which generates six directional wavelets. These six directional wavelets are superimposed in order to obtain shape-encoded image. LDP is then applied on this wavelet-based shape-encoded image. Further, to enhance retrieval accuracy, LDP features are extracted from patches of both original and shape-encoded images. These patches are assigned with weights based on average discrimination capability of features in a patch. Experiments are performed using three different standard databases with various variations such as pose, distortion, partial occlusion and complex structure. The proposed technique achieves 96.4 and 98.76 % retrieval accuracy at a recall of 50 %, for Kimia-99 and COIL-100 databases, respectively. For MPEG-7 CE-2 shape database, retrieval accuracy of 61.93 % is achieved in terms of average Bull’s eye performance (BEP). The proposed technique is also tested on Springer medical image database to explore its scope in other areas, wherein it attains average BEP of 69.68 % in comparison with 61.52 % with ZMs. It is observed that the proposed technique outperforms other well-known existing methods of image retrieval.     

A new technique for ARMA-system identification and rationalapproximation

The fundamental problem of identifying a linear time-invariant system from measured samples of its output response to a known input is addressed, utilizing a new and simple deterministic theory founded on well-established passive network concepts. The analysis, together with documented numerical results, demonstrates that the proposed method achieves two goals: stable rational minimum-phase transfer functions can be identified with a priori knowledge of either numerator or denominator degrees; and stable rational minimum-phase Pade-like approximations appear to be generated automatically in the nonrational case. To substantiate these claims, a detailed theoretical exposition of the basic ideas, an extensive discussion of numerical results, and a summary of related results are given     

Image forgery detection algorithm based on U-shaped detection network

Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.     

基于Zernike矩及支持向量机的猪的姿态识别

为监控猪的行为,提出了基于Zernike矩及支持向量机的猪的行走姿态识别方法。首先对原始图像进行预处理,提取出原始图像中猪的轮廓图像。然后,根据标准矩对上述图像进行归一化,再对归一化后的二值轮廓图提取Zernike矩特征。在此基础上,利用支持向量机理论设计了多种姿态分类器,实现对猪的正常行走、低头行走、抬头行走、躺卧等四种姿态进行识别。实验结果表明,此方法对猪的姿态分类识别的准确度达到了95%以上。该项研究对猪的姿态识别方面具有显著价值。     

语音伪造及检测技术研究综述

语音承载着人类语言和说话人身份信息,通过语音伪造技术可以精确模仿目标说话人的声音以达到欺骗人或机器听觉的目的.目前,深度伪造(Deepfake)正在对全球的政治经济及社会稳定带来极大的威胁,其中语音伪造是Deepfake实现舆论操控的核心技术之一.近年来语音伪造技术在拟人度、自然度方面有了显著进步,使得语音伪造检测技术...     

基于Zernike矩和PSO算法的摄像机神经网络标定

提出了一种新的基于Zernike矩和粒子群(PSO)算法的摄像机BP神经网络标定方法。首先,利用Zernike矩和曲率不变性求取圆形标定模板中心的亚像素坐标,提高神经网络训练数据的精度;其次,利用PSO算法优化网络的初始权重和阈值,提高网络的收敛速度和泛化能力。实验结果表明,该方法在X轴和Y轴方向的测量误差小于0.06 mm,整个测试集均方根误差为0.194 mm,证明了该方法的有效性。     

Copy-move image forgery detection based on Gabor magnitude

With advancement of media editing software, even people who are not image processing experts can easily alter digital images. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. The most common method of tampering with a digital image is copy-move forgery, in which a part of an image is duplicated and used to substitute another part of the same image at a different location. In this paper, we present an efficient and robust method to detect such artifacts. First, the tampered image is segmented into overlapping fixed-size blocks, and the Gabor filter is applied to each block. Thus, the image of Gabor magnitude represents each block. Secondly, statistical features are extracted from the histogram of orientated Gabor magnitude (HOGM) of overlapping blocks, and reduced features are generated for similarity measurement. Finally, feature vectors are sorted lexicographically, and duplicated image blocks are identified by finding similarity block pairs after suitable post-processing. To enhance the algorithm’s robustness, a few parameters are proposed for removing the wrong similar blocks. Experiment results demonstrate the ability of the proposed method to detect multiple examples of copy-move forgery and precisely locate the duplicated regions, even when dealing with images distorted by slight rotation and scaling, JPEG compression, blurring, and brightness adjustment.     

基于扩展马尔科夫特征的Seam-Carving篡改检测

针对能够用于图像篡改的Seam-Carving技术,提出了一种基于扩展的马尔科夫特征的Seam-Carving篡改识别算法。该算法充分考虑了Seam-Carving操作导致的图像频域特征的变化,将传统的利用马尔科夫转移概率矩阵求取的图像特征和基于扩展的马尔科夫转移概率特征进行融合,而后利用支持向量机进行分类训练,从而达到有效识别基于Seam-Carving的图像篡改。实验结果表明,提出的方案性能优于传统的基于马尔科夫转移矩阵的特征选择方法以及现有的一些该类图像篡改检测方法。     

Zernike矩不变性分析及其改进

在分析了Zernike矩不变性后,提出了一种基于图像整个区域形状描述算子的改进Zernike矩。改进的Zernike矩不仅具有旋转不变性,而且还具有改进前不具有的比例不变性。试验结果证明,和改进前的Zernike矩相比,改进后的Zernike矩具有更好的不变特征,可用作基于区域的形状描述算子。     

基于分组SIFT的图像复制粘贴篡改快速检测算法

针对现有图像复制粘贴篡改检测算法计算复杂度过高的问题,提出了一种基于分组尺度不变特征变换的图像复制粘贴篡改快速检测算法。首先,利用简单线性迭代聚类将输入图像分割成非重叠且不规则的块;然后,根据图像块内结构张量属性将其分为平坦块、边缘块和角点块,提取图像块内的SIFT特征点作为块特征;最后,通过块特征的类间匹配定位篡改区域。所提算法通过图像块分类和类间匹配,在保证检测效果的同时,有效地降低了特征匹配定位篡改区域阶段的时间复杂度。实验结果表明,所提算法检测准确率为97.79%,召回率为90.34%,F值为93.59%;图像尺寸为1 024像素×768像素时算法时间复杂度为12.72 s,图像尺寸为3 000像素×2 000像素时算法时间复杂度为639.93s。与已有的复制粘贴算法相比,所提算法能够快速精准地定位篡改区域,且具有较好的稳健性。     

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.     

图像镜像复制粘贴篡改检测中的FI-SURF算法

针对数字图像版权中的复制粘贴篡改问题,提出FI-SURF (flip invariant SURF)算法。研究了当图像经过镜像翻转后SURF (speeded-up robust features)特征描述符的排列变化关系。提取SURF特征点后,将其特征描述符重新排序,即使复制粘贴区域经过镜像翻转,对应的特征点依然可以进行匹配。实验证明,FI-SURF算法在保留SURF算法运算速度快、顽健性强等优点的前提下,可有效检测出经过镜像翻转的复制粘贴区域,计算出复制粘贴区域的轮廓。     

A novel passive forgery detection algorithm for video region duplication

Forgery involving region duplication is one of the most common types of video tampering. However, few algorithms have been suggested for detecting this type of forgery effectively, especially for videos to which a mirroring operation was applied. In this paper, we summarize the properties of duplication forgery of video regions and propose a novel algorithm to detect this forgery. First, the algorithm extracts the feature points in the current frame. The tampered areas in the current frame are then searched, which is implemented in three steps. Finally, our algorithm detects the tampered areas in the remaining frames using spatio-temporal context learning and outputs the detection results. The experimental results demonstrate the satisfactory performance of our algorithm for detecting videos subjected to mirror operations and its higher efficiency than previous algorithms.     

Exact Zernike and pseudo-Zernike moments image reconstruction based on circular overlapping blocks and Chamfer distance

This study aims to explore a novel approach to reconstruct multi-gray-level images based on circular blocks reconstruction method using two exact and fast moments: Zernike (CBR-EZM) and pseudo-Zernike (CBR-EPZM): An image is first divided into a set of sub-images which are then reconstructed independently. We also introduced Chamfer distance (CD) to capitalize on the use of discrete distance instead of Euclidean one. The combination of our methods and CD leads to CBR-EZM-CD and CBR-EPZM-CD methods. Obviously, image partitioning offers significant advantages, but an undesirable circular blocking effect can occur. To mitigate this effect, we have implemented overlapping feature to our new methods leading to OCBR-EZM-CD and OCBR-EPZM-CD, by exploiting neighborhood information of the circular blocks. The main motivation of this novel approach is to explore new applications of Zernike and pseudo-Zernike moments. One of the fields is feature extraction for pattern recognition: Zernike and pseudo-Zernike moments are well known to capture only the global features, but thanks to the circular block reconstruction, we can now use those moments to extract also local features.     

Using Zernike moments for the illumination and geometry invariantclassification of multispectral texture

We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. A several step algorithm that includes scale estimation and correlation moment computation is used to achieve the invariance. The key to the method is the new result that illumination, rotation, and scale changes in the scene correspond to a specific transformation of correlation function Zernike moment matrices. These matrices can be estimated from a color image. This relationship is used to derive an efficient algorithm for recognition. The algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations.     

Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments

Image classification is a challenging problem of computer vision. This study reports a fuzzy system to semantic image classification. As it is a complex task, various information of digital image, including three color space components and two Zernike moments with different order, are gathered and utilized as an input of fuzzy inference system to materialize a robust rotation/lighting condition and size invariant image classifier. For better performance, all the membership functions are optimized by genetic algorithm after empirical design stage. 90.62 and 96.25 % classification rates for RGB and HSI color spaces confirm the reliability of optimized system in different image conditions given in this contribution.