基于成像传感器特征的相机分类器设计

在对现有成像设备源辨识算法分析研究的基础上,提出一种利用成像传感器特征进行相机源辨识的鲁棒性方法.基于模式分类的原理,首先分析数码相机成像的特点,提取传感器噪声信息的统计特征,设计一种鲁棒的分类器来确定相机的品牌/型号.所提取的图像特征包括图像去噪差值和小波域分析.结果表明:所设计的分类器可以有效地正确辨识相机品牌/型...     

一种基于成像传感器噪声的相机源辨识算法

通过支持向量机对数字图像的源相机问题进行研究.提出了一种基于成像传感器噪声特征的源辨识算法.利用去噪算法对数字图像的单个颜色通道图像进行去噪处理,用原始图像减去滤波图像得到传感器的噪声信息,再提取噪声信息的统计特征;将统计特征生成一个特征向量的形式来设计源辨识算法;借助支持向量机工具来确定相机的品牌或型号.实验结果表明...     

利用彩色图像信息的源相机辨识策略

目前基于模式噪声的源相机辨识方法大都只在单颜色通道图像上进行,或是对三个单色通道图像上的检测结果简单地求均值,这显然不能全面反映成像传感器的特征。通过分析相机图像生成过程中马赛克图像插值过程的特点,提出三种新的基于彩色图像信息的源相机辨识策略。实验结果表明,所提策略具有更好的辨识效果且需要的特征点少。     

利用模式噪声主分量信息的源相机辨识技术

针对数码相机源辨识问题,提出一种利用成像传感器模式噪声主要分量（或称大分量）信息进行源设备辨识的新方法。首先围绕模式噪声的抽取和预处理,讨论去噪滤波器和去颜色滤波器阵列（CFA）插值对提高模式噪声质量的影响,然后重点讨论选取传感器模式噪声部分大分量对改善相关性检测器性能的作用。该方法不仅能很好地分离两台相机所拍摄的照片,而且还大幅度减小了检测的计算量。     

基于图像内容和噪音特征的扫描仪源辨识

以扫描仪获得的数字扫描图像为研究对象,提出一种基于图像内容和噪音特征的扫描仪源辨识方法,提取数字扫描图像中的颜色特征、质量特征和邻或预测特征,生成一个72维的特征向量以辨识扫描仪的来源,并借助支持向量机确定扫描仪的品牌或型号.实验结果表明,该方法具有较高的分类精度,并且在数字扫描图像被压缩或剪切的情况下均具有较好的鲁棒...     

典型源相机分类算法性能研究

现有文献中的源相机分类算法很少讨论测试图像在受到轻微图像处理后算法性能的变化。利用支持向量机,对源相机分类算法的性能和鲁棒性进行了分析,比较了测试图像遭受处理前后分类算法的检测准确率,并研究了图像特征的鲁棒性。由于基于模式分类的算法通常需要精简特征个数以提高执行效率,因此,还讨论了精简模式下相机分类算法的性能以及特征选择对辨识算法鲁棒性的影响。     

基于RetinaNet的手机主板缺陷检测研究

不同型号手机的主板图像具有多分辨率的成像模式,使缺陷元件是多尺度的。常规缺陷检测方法主要有图像融合方法和提取统计模型的方法,但这些方法的鲁棒性仍需要提高。针对该问题,提出了一种自动检测网络模型,即RetinaNet目标检测器。首先使用特征金字塔网络(FPN)提取缺陷元件的多尺度特征分类和位置,然后引入MobileNetV2以压缩和加速RetinaNet模型,最后使用焦点损失解决类不平衡和难以检测样本对损失贡献程度的问题。实验结果表明, RetinaNet能有效地检测不同尺度的缺陷元件,具有很高的检测精度;与其他目标检测器相比,RetinaNet实现了超过95%的平均精度(mAP)。这些结果表明了本文所提模型的有效性。     

不同曝光值图像的直接融合方法

提出了一种直接从同一场景多次不同曝光值下成像的LDR(low dynamic range)图像序列中提取每个像素位置最佳成像信息的图像融合方法,可以在无需任何拍摄相机参数及场景先验信息的情况下,快速合成适合在常规设备上显示的HDR(high dynamic range)图像.该方法利用特殊设计的鲁棒性曲线拟合算法建立LDR图像序列中每个像素位置像素值曲线的数学模型,并由此给出评价单个像素成像时曝光合适程度的标准和融合最佳成像像素信息的方法.对不同场景的大量实验结果显示,该方法的计算结果与传统HDR成像技术经过复杂的HDR重建和色调映射计算后得到的结果相当,但具有更高的计算效率,并同时对图像噪声、相机微小移动和运动目标的影响具有较好的鲁棒性.     

融合点线特征的视觉惯性SLAM算法

为使移动机器人在昏暗场景中获得更高的定位精度以实现更好的建图效果,针对特征点稀疏、相机移动过快所导致的SLAM算法初始化困难、定位精度较低等问题,提出了一种融合点线特征的视觉惯性SLAM算法。通过EDLines线特征提取匹配算法来引入丰富的线特征,进而提升特征跟踪的鲁棒性;利用惯性传感器和视觉传感器的互补优势,通过视觉惯性分步联合初始化方法在初始化阶段分三步进行优化,进而提升初始化的精度和速度。实验表明,该算法所使用的线特征提取匹配算法相比传统的LSD算法具有了更快的匹配速度和更低的误匹配率,并且该算法在视觉惯性数据集中相机位姿的估计精度更高、鲁棒性更强。     

基于共面圆的距离传感器与相机的相对位姿标定

近年来, 距离传感器与摄像机的组合系统标定在无人车环境感知中得到了广泛的研究与应用, 其中基于平面特征的方法简单易行而被广泛采用. 然而, 目前多数方法基于点匹配进行, 易错且鲁棒性较低. 本文提出了一种基于共面圆的距离传感器与相机的组合系统相对位姿估计方法. 该方法使用含有两个共面圆的标定板, 可以获取相机与标定板间的位姿, 以及距离传感器与标定板间的位姿. 此外, 移动标定板获取多组数据, 根据计算得到两个共面圆的圆心在距离传感器和相机下的坐标, 优化重投影误差与3D对应点之间的误差, 得到距离传感器与相机之间的位姿关系. 该方法不需要进行特征点的匹配, 利用射影不变性来获取相机与三维距离传感器的位姿. 仿真实验与真实数据实验结果表明, 本方法对噪声有较强的鲁棒性, 得到了精确的结果.     

Intrinsic Sensor Noise Features for Forensic Analysis on Scanners and Scanned Images

A large portion of digital images available today are acquired using digital cameras or scanners. While cameras provide digital reproduction of natural scenes, scanners are often used to capture hard-copy art in a more controlled environment. In this paper, new techniques for nonintrusive scanner forensics that utilize intrinsic sensor noise features are proposed to verify the source and integrity of digital scanned images. Scanning noise is analyzed from several aspects using only scanned image samples, including through image denoising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Based on the proposed statistical features of scanning noise, a robust scanner identifier is constructed to determine the model/brand of the scanner used to capture a scanned image. Utilizing these noise features, we extend the scope of acquisition forensics to differentiating scanned images from camera-taken photographs and computer-generated graphics. The proposed noise features also enable tampering forensics to detect postprocessing operations on scanned images. Experimental results are presented to demonstrate the effectiveness of employing the proposed noise features for performing various forensic analysis on scanners and scanned images.      

适用于动态对焦的高精度灵活标定方法*

处于动态对焦过程及不同对焦状态下的相机内参会不断变化.文中通过在镜头对焦环上加装角度传感器的方式,提出基于动态对焦过程以主距增量为参数的相机模型,实现任意对焦状态和距离下相机内参数的求解.利用定制的三平面立体标定靶,设计一种高鲁棒性的标定方法.与传统模型相比,该模型基本未增加额外约束条件,可操作性较强.实验表明,文中模型及相应标定方法具有更强的图像校正能力,从而提高摄影测量精度.     

Robust feature extraction algorithm suitable for real-time embedded applications

Smart cameras integrate processing close to the image sensor, so they can deliver high-level information to a host computer or high-level decision process. One of the most common processing is the visual features extraction since many vision-based use-cases are based on such algorithm. Unfortunately, in most of cases, features detection algorithms are not robust or do not reach real-time processing. Based on these limitations, a feature detection algorithm that is robust enough to deliver robust features under any type of indoor/outdoor scenarios is proposed. This was achieved by applying a non-textured corner filter combined to a subpixel refinement. Furthermore, an FPGA architecture is proposed. This architecture allows compact system design, real-time processing for Full HD images (it can process up to 44 frames/91.238.400 pixels per second for Full HD images), and high efficiency for smart camera implementations (similar hardware resources than previous formulations without subpixel refinement and without non-textured corner filter). For accuracy/robustness, experimental results for several real-world scenes are encouraging and show the feasibility of our algorithmic approach.     

Robust visual tracking control system of a mobile robot based on a dual-Jacobian visual interaction model

This paper presents a novel design of a robust visual tracking control system, which consists of a visual tracking controller and a visual state estimator. This system facilitates human–robot interaction of a unicycle-modeled mobile robot equipped with a tilt camera. Based on a novel dual-Jacobian visual interaction model, a robust visual tracking controller is proposed to track a dynamic moving target. The proposed controller not only possesses some degree of robustness against the system model uncertainties, but also tracks the target without its 3D velocity information. The visual state estimator aims to estimate the optimal system state and target image velocity, which is used by the visual tracking controller. To achieve this, a self-tuning Kalman filter is proposed to estimate interesting parameters and to overcome the temporary occlusion problem. Furthermore, because the proposed method is fully working in the image space, the computational complexity and the sensor/camera modeling errors can be reduced. Experimental results validate the effectiveness of the proposed method, in terms of tracking performance, system convergence, and robustness.     

Determining Image Origin and Integrity Using Sensor Noise

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.     

基于完整可见性模型的改进鲁棒OctoMap

从移动机器人自主导航对3D地图精度的需求出发,在鲁棒OctoMap的基础上提出一种基于完整可见性模型的改进鲁棒OctoMap并应用于基于Kinect的RGB-D同时定位与地图创建（SLAM）中。首先,通过考虑相机和目标体素的相对位置关系及地图分辨率进行可连通性判断,获得满足可连通性的相邻体素的个数及位置;其次,根据不同的可连通性情况分别建立目标体素的可见性模型,从而构建普适性更强的完整可见性模型,有效克服了鲁棒OctoMap可见性模型的局限性,提高了建图精度;再次,使用基于高斯混合模型的Kinect深度误差模型代替简单深度误差模型,进一步克服传感器测量误差对地图精度的影响,降低了地图的不确定性;最后,结合贝叶斯公式和线性插值算法来更新八叉树中每个节点的实际占用概率,从而构建基于八叉树的立体占用地图。实验结果表明,所提方法有效克服了Kinect传感器深度误差对地图精度的影响,降低了地图的不确定性,其建图精度较鲁棒OctoMap有明显的提高。     

Optic flow estimation by a Hopfield neural network using geometrical constraints

Sparse optic flow maps are general enough to obtain useful information about camera motion. Usually, correspondences among features over an image sequence are estimated by radiometric similarity. When the camera moves under known conditions, global geometrical constraints can be introduced in order to obtain a more robust estimation of the optic flow. In this paper, a method is proposed for the computation of a robust sparse optic flow (OF) which integrates the geometrical constraints induced by camera motion to verify the correspondences obtained by radiometric-similarity-based techniques. A raw OF map is estimated by matching features by correlation. The verification of the resulting correspondences is formulated as an optimization problem that is implemented on a Hopfield neural network (HNN). Additional constraints imposed in the energy function permit us to achieve a subpixel accuracy in the image locations of matched features. Convergence of the HNN is reached in a small enough number of iterations to make the proposed method suitable for real-time processing. It is shown that the proposed method is also suitable for identifying independently moving objects in front of a moving vehicle. Received: 26 December 1995 / Accepted: 20 February 1997