Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation, in which the content from the same image is used to forge it. Such manipulations are performed to hide the desired information. Therefore, forgery detection methods are required to identify forged areas. We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern (LTrP) features to detect the single and multiple copy-move attacks from the images. The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations. It also uses discrete wavelet transform (DWT) for dimension reduction. The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods. Finally, Jeffreys and Matusita distance is used for similarity measurement. For the evaluation of the results, three datasets are used, namely MICC-F220, MICC-F2000, and CoMoFoD. Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.     

A robust and forensic transform for copy move digital image forgery detection based on dense depth block matching

ABSTRACT

Copy-move forgery is one of the most popular tampering artefacts in digital images. However, tampering effect in digital images makes the authentication of the processing as untrustworthy. In this paper, a combination of Fourier-Mellin and Zernike moments (FMZM) Transform is proposed which detects the copy-move region with high-speed and low-computational complexity. Here, initially an image is segmented into various blocks using marker controlled watershed management and from that proposed FMZM feature extraction is used which detects duplication. The detected regions are matched with the Dense Depth Reconstruction based lexicographically sorting. Finally, tampered outliers presented at the data are removed through RANSAC (RANdom Sample Consensus) algorithm, in which removed false matches are verified with the morphological operators. The efficiency of proposed method is measured by various performance metrics and this method earned up to 97.56%, 99.98%, and 97.12% for precision, recall, and F1-score performance, respectively.     

基于双信息统计与引力聚类的图像篡改检测算法

目的 为了解决当前图像复制-粘贴篡改检测算法的鲁棒性与检测精准度不佳等问题。方法 将图像的颜色信息引入伪造检测过程,提出双信息统计机制耦合引力聚类的图像复制-粘贴篡改检测算法。首先,利用Hessian矩阵来准确提取图像的特征点。然后,利用图像的梯度直方图来描述图像的方向特征,并联合图像的颜色信息,构造双信息统计机制,获取图像的特征向量。计算特征向量间的欧氏距离,构造近似测量模型,对图像特征进行匹配。最后,利用引力聚类方法,实现图像特征点的聚类,精准检测复制-粘贴篡改内容。结果 与当前图像复制-粘贴篡改检测方法相比,所提算法具有更高的检测精准度,以及更好的鲁棒性。结论 所提方案可以准确检测并定位出伪造内容,在图像水印、信息安全领域具有一定的参考价值。     

Depth extraction of three-dimensional objects in space by the computational integral imaging reconstruction technique

A novel approach to extract the depth data of 3D objects in space by using the computational integral imaging reconstruction (CIIR) technique is proposed. With elemental images of 3D objects captured by the CCD camera through a pinhole array, depth-dependent object images can be reconstructed on the output plane by the CIIR technique. Only the images reconstructed on the output planes where 3D objects were located are clearly focused; so the depth data of 3D objects in space can be extracted by discriminating these focused output images from the others by using an image separation technique. A feasibility test of the proposed CIIR-based depth extraction method is carried out, and its results are discussed as well.     

曲面立体完整画隐线图的标记

提出了三面顶点“流形”曲面立体完整画隐线图的标记理论和方法。在三面顶点曲面立体完整画隐线图中合法的节点型式共有69种,其中Y型节点有8种,W型节点有16种,S型节点有11种,V型节点有34种。对曲面立体完整画隐线图进行标记,能够判断其是否可能为“流形”曲面立体的投影,区分正确与不正确画隐线图。提出的完整画隐线图标记方法也适用于平面立体画隐线图。算例证明曲面立体完整画隐线图标记方法是正确、可行和有效的。     

Phase shift migration for imaging layered objects and objects immersed in water

This paper proposes the use of phase shift migration for ultrasonic imaging of layered objects and objects immersed in water. The method, which was developed in reflection seismology, is a frequency domain technique that in a computationally efficient way restores images of objects that are isotropic and homogeneous in the lateral direction but inhomogeneous in depth. The performance of the proposed method was evaluated using immersion test data from a block with side-drilled holes with an additional scatterer residing in water. In this way, the method's capability of simultaneously imaging scatterers in different media and at different depths was investigated. The method was also applied to a copper block with flat bottom holes. The results verify that the proposed method is capable of producing high-resolution and low-noise images for layered or immersed objects.     

Blur-metric-based resolution enhancement of computationally reconstructed integral images

A computational integral imaging reconstruction technique can reconstruct a set of plane images of three-dimensional (3-D) objects along the output plane, in which only the plane object image (POI) reconstructed on the right planes where the objects were positioned is highly focused, whereas the other POIs reconstructed away from these planes are unfocused and blurred. In fact, these blurred POIs act as additional noises to other object images reconstructed on different output planes, so that the resolution of reconstructed object images should be considerably deteriorated. In this paper, a novel approach is proposed to effectively reduce the blurred images occurring in the focused POIs by employing a blur metric. From the estimated blur metric of each reconstructed POI, the right output planes where the objects were located can be detected. In addition, with an estimated blur metric, focused POIs can be adaptively eroded by a simple gray level erosion operation because it reduces regional expansion caused by the blur effect. The gray values of the eroded POIs are then finally remapped by referencing the original POIs. Some experiments revealed an average increase of 1.95 dB in the peak signal-to-noise ratio in the remapped POIs compared with that of the originally reconstructed POIs, and that the original forms of the object images in the remapped POIs could be preserved even after they had gone through an erosion operation. This feasibility test of the proposed scheme finally suggests a possibility of its application to robust detection and recognition of 3-D objects in a scene.     

Boundary-based object segmentation in X-ray imaging using spiral search

In this paper, we propose a boundary-based method for object segmentation by using only the edge information. The proposed method is especially applied to object segmentation of dangerous firearms and knives in the X-ray images of baggage, where no colour or texture features are available to describe the target object. The Canny edge detector is used to extract edge points from the X-ray image. These edges have cluttered backgrounds and may be discontinuous. A fast spiral search is proposed to connect neighbouring points, either continuous or discontinuous, and form closed contours for individual objects. The distance and direction angle of an edge point in the search process can be obtained from a pre-constructed spiral look-up-table. No computation of the geometric features is required. Thus, the search of the coherent neighbouring points for edge connection is very fast. The experimental results have shown that the proposed method can effectively and efficiently segment a variety of firearms and knives of different shapes and sizes in the X-ray images of baggage.     

A multimodal approach for individual tracking of people and their belongings

Abstract

In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object’s size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.     

Moving object detection and tracking in videos through turbulent medium

This paper addresses the problem of identifying and tracking moving objects in a video sequence having a time-varying background. This is a fundamental task in many computer vision applications, though a very challenging one because of turbulence that causes blurring and spatiotemporal movements of the background images. Our proposed approach involves two major steps. First, a moving object detection algorithm that deals with the detection of real motions by separating the turbulence-induced motions using a two-level thresholding technique is used. In the second step, a feature-based generalized regression neural network is applied to track the detected objects throughout the frames in the video sequence. The proposed approach uses the centroid and area features of the moving objects and creates the reference regions instantly by selecting the objects within a circle. Simulation experiments are carried out on several turbulence-degraded video sequences and comparisons with an earlier method confirms that the proposed approach provides a more effective tracking of the targets.     

Investigation of the Cauchy-Riemann equations for one-dimensional image recovery in intensity interferometry

A method of image recovery using noniterative phase retrieval is proposed and investigated by simulation. This method adapts the Cauchy-Riemann equations to evaluate derivatives of phase based on derivatives of magnitude. The noise sensitivity of the approach is reduced by employing a least-mean-squares fit. This method uses the analytic properties of the Fourier transform of an object, the magnitude of which is measured with an intensity interferometer. The solution exhibits the degree of nonuniqueness expected from root-flipping arguments for the one-dimensional case, but a simple assumption that restricts translational ambiguity also restricts the space of solutions and permits essentially perfect reconstructions for a number of non-symmetric one-dimensional objects of interest. Very good reconstructions are obtained for a large fraction of random objects, within an overall image flip, which may be acceptable in many applications. Results for the retrieved phase and recovered images are presented for some one-dimensional objects and for different noise levels. Extensions to objects of two dimensions are discussed. Requirements for signal-to-noise ratio are derived for intensity interferometry with use of the proposed processing.     

基于宽光谱光学遥感图像的细分光谱光学遥感图像的模拟

本文分析了现有遥感图像模拟方法的特点,介绍了地物波谱知识库的建立情况与应用意义,论述了光学传感器的成像特点及地面实测数据与遥感反演数据之间的转换关系.本文在对TM图像的光谱波段进行细划分的基础上,提出利用波谱知识库的数据支持来模拟获取具有更高光谱分辨率的细分光谱光学遥感图像的方法.本方法依据光谱数据库等先验知识的支持,通过遥感成像时辐射传输的模拟运算,在宽光谱光学遥感数据提供的图像纹理信息和地类信息的基础上,获取细分光谱波段的模拟光学遥感图像,同时避免了难以接受的计算量.细分光谱模拟图像的实验结果表明,本方法能够较为可靠的模拟出真实高光谱分辨率图像的信息,模拟方法可信.     

Efficient object detection and tracking in video sequences

One of the most important problems in computer vision is the computation of the two-dimensional projective transformation (homography) that maps features of planar objects in different images and videos. This computation is required by many applications such as image mosaicking, image registration, and augmented reality. The real-time performance imposes constraints on the methods used. In this paper, we address the real-time detection and tracking of planar objects in a video sequence where the object of interest is given by a reference image template. Most existing approaches for homography estimation are based on two steps: feature extraction (first step) followed by a combinatorial optimization method (second step) to match features between the reference template and the scene frame. This paper has two main contributions. First, we detect both planar and nonplanar objects via efficient object feature classification in the input images, which is applied prior to performing the matching step. Second, for the tracking part (planar objects), we propose a fast method for the computation of the homography that is based on the transferred object features and their associated local raw brightness. The advantage of the proposed schemes is a fast matching as well as fast and robust object registration that is given by either a homography or three-dimensional pose.     

Enhanced reconstruction of three-dimensional shape and texture from integral photography images

A method for the reconstruction of 3D shape and texture from integral photography (IP) images is presented. Sharing the same principles with stereoscopic-based object reconstruction, it offers increased robustness to noise and occlusions due to the unique characteristics of IP images. A coarse-to-fine approach is used, employing what we believe to be a novel grid refinement step in order to increase the quality of the reconstructed objects. The proposed method's properties include configurable depth accuracy and direct and seamless triangulation. We evaluate our method using synthetic data from a computer-simulated IP setup as well as real data from a simple yet effective digital IP setup. Experiments show reconstructed objects of high-quality indicating that IP can be a competitive modality for 3D object reconstruction.     

Depth extraction of three-dimensional objects using block matching for slice images in synthetic aperture integral imaging

We describe a computational method for depth extraction of three-dimensional (3D) objects using block matching for slice images in synthetic aperture integral imaging (SAII). SAII is capable of providing high-resolution 3D slice images for 3D objects because the picked-up elemental images are high-resolution ones. In the proposed method, the high-resolution elemental images are recorded by moving a camera; a computational reconstruction algorithm based on ray backprojection generates a set of 3D slice images from the recorded elemental images. To extract depth information of the 3D objects, we propose a new block-matching algorithm between a reference elemental image and a set of 3D slice images. The property of the slices images is that the focused areas are the right location for an object, whereas the blurred areas are considered to be empty space; thus, this can extract robust and accurate depth information of the 3D objects. To demonstrate our method, we carry out the preliminary experiments of 3D objects; the results indicate that our method is superior to a conventional method in terms of depth-map quality.     

CAD系统中产品设计三维实体建模方法的研究

三维建模是应用CAD系统进行产品设计的重要步骤。现在市场上已有不少的CAD系统，但是不同系统中的三维建模方法却是基本相同的。在这些三维建模的方法中，往往对用户施加了许多的限制，使得用户无法建立复杂的三维实体。笔者提出了一种新的三维建模方法－折叠法，应用这种方法的概念和原理，可以开发出先进的三维建模CAD系统。折叠二维平面图形构成三维实体是作者提出的一种新概念。     

一种基于三维模型投影的人脸识别新方法

研究了使用三维人脸模型进行不同姿势下的人脸识别问题，提出了一种三维建模二维识别的人脸识别算法，首先使用该方法将三维模型向不同方向投影，进而将不同姿势的二维图像与不同方向的投影结果相匹配，进行人脸识别。研究了使用Minolta Vivid 910进行数据获取，创建三维模型的方法和过程。实验结果表明，在进行不同姿势的人脸识别时，该方法的识别速度快于三维可变形模型方法，识别率远优于使用二维正面图像作为模板的人脸识别方法。     

一种新的模板匹配算法及其在三维形貌测量中的应用

投影光栅法是进行三维形貌测量的有效手段，但当被测物体陡峭度较大或存在高度突变时会出现阴影问题。为此本提出一种基于互相关函数差值的模板匹配算法，该算法可以有效地解决这一问题，同时该算法也可用于大尺度物体三维形貌测量的拼接技术，中给出了算法的详细描述及实例计算，计算结果证明了该算法的正确性。     

摄像机旋转运动下的快速目标检测算法

论文提出了一种摄像机旋转运动下的快速目标检测算法。首先为图像的全局运动建立旋转参数模型,然后基于运动预测在相邻帧之间建立SIFT特征点对,利用RANSAC去除外点的影响,结合最小二乘法求解全局运动参数进行运动补偿,基于残差图像的更新策略实时更新特征点集,以适应背景的变化,最后使用帧差法获得运动目标。该算法不仅保持了SIFT本身的优越性能,而且极大地提高了检测速度。实验结果表明该算法可以实时准确的检测出运动目标。