非晶硅太阳电池电极红外诊断技术研究

本文介绍了红外诊断技术用于探测物体内部结构性质的方法，用这种方法分辩并确定出被测物体现人部缺陷准确的位置，可以进行定量分析。通过对物体的红外辐射过程以及物体内部热传导特性的研究，提出了用经外诊断技术检测非晶硅太阳电池电极质量的新方法，并给出了诊断结果。     

利用声音和振动来探伤

使用超声波和X射线，可以在不破坏物体的前提下检查物体内部是否存在有裂纹和磨损，这种检测方式属于非破坏性检测。与之相比，利用声音和振动的非破坏性检测则更加多样，更富于变化。最为简单不需要设备和费用的非破坏性检测是用手指头敲击物体用耳朵听敲击声的检查方法。瓷碗的裂痕用这种方法可以100％的检查出来。本文介绍同样可以达到这一成功率的利用声音和振动的非破坏检测。     

用离散正交多项式进行深度图的特征检测

本文介绍一种利用离散正交多项式进行物体表面局部曲面拟合以检测物体的特征的方法.该方法的拟合涵数力双变量二阶多项式,用该方法对深度图进行特征检测,可避免直接对深度图数据进行数值差分,减少噪声对特征的影响.     

基于邻近轮廓片段的物体检测

本文提出一种新颖的物体特征：邻近轮廓片段。该特征能够很好的描述物体边缘特征，而且具有尺度不变性。利用该特征训练得到的SVM检测器，在物体检测过程中取得了很好的效果。经实验验证，该特征非常适合用于物体检测。     

一种全局优化的多面体凸分解匹配方法

提出了一种全局优化的多面体匹配方法，该方法对物体的拓扑关系和结构关系进行多级匹配,利用全局优化的松驰迭代方法减少匹配误差，并由匹配的一致性准则进行匹配结果的检测，根据物体的内在特征来进行匹配，从而解决非同构物体的匹配.可广泛应用于物体识别、三维物体重建、医学图象处理等领域，实验证明此方法质量好、自动化程度高，是一种有效的全局优化结构匹配方法．     

引入光流法的活动轮廓模型

本文在活动轮廓模型的基础上，引入了“图象统计势能”和光流法，提出了一种新的图象分割方法，该方法改进了活动轮廓模型的一些不足，能准确地检测出物体轮廓边界，且分割结果对初始位置不敏感，同时提高了对运动速度较快的物体轮廓检测的正确性，实验结果证实了该方法的有效性。     

基于分区编码坐标的目标物体6D位姿检测网络

<正>基于建立2D-3D坐标密集对应的两阶段方法是对目标物体进行6D位姿检测的一类解决方案。本文在前人的基础上改进，提出了ZC-Pose，一种基于分区编码坐标的目标物体6D位姿检测网络框架，其仅使用RGB数据和模型信息对目标位姿进行预测。本文在Linemod数据集上测试其表现，相较于该领域的一项经典工作——CDPN中的旋转分支部分有了可观的提升，并且与其他相关工作进行比较，也表现出了具有竞争力的结果。     

可变形物体的轮廓的提取

边缘信息一直被认为是计算机视觉的重要特性。因而，边缘的检测与轮廓的提取是图象分析的重要步骤。对于边缘的检测，近年来，人们的研究兴趣更多的转向了局部能量的方法，这是一种基于局部相位的方法，它的特点是通过局部能量的最大值可以同时得到不同类型的边缘。在初步得到物体边缘后，本文应用Ｍ．Ｋａｓｓ１９８７年提出的蛇行模型的方法获取物体轮廓，蛇行模型比较适合于可变形物体的轮廓的提取，如：红血球。     

视频监控中遗留物检测算法研究

本文首先介绍了混合高斯模型的算法原理,并且提出一种利用递归法检测多个前景物体的方法,然后提出一种基于慢高斯的遗留物检测算法,该算法利用混合高斯模型进行背景建模,利用递归法对检测到的前景物体分别计时,当计时器达到阈值时进行标记并报警。     

利用光学多道仪（OMA）实时在线测量微小物体尺寸的研究

本文介绍了一种利用光学多道仪测量微小物体尺寸的方法。从物体的散射光分布推算被测物体尺寸，并给出实测结果，研究表明应用此方法在研究和生产过程中对微小物体进行在线测量是可行的。     

红外偏振图像的仿真

为了在获得真实偏振图像之前获得偏振图像,采用仿真的方法进行了实验研究,即先通过实验获得各种物体的偏振度建立一个偏振数据库并进行理论研究论证,再利用K-均值聚类算法对红外图像中的物体进行聚类,最后将聚类后的图像通过计算得到偏振图像。结果表明,得到的仿真偏振图像效果很明显,能够很好地区分人造物和自然物,从而验证了仿真算法的可行性。图像仿真对于更好、更方便地研究物体的偏振特性和建立偏振数据库有着非常重要的意义。     

基于图像局部特征的红外小目标检测与跟踪算法

基于红外序列图像中小目标的典型特征,提出了一种新的红外小目标检测与跟踪算法.利用图像小目标的微分几何特性,计算图像的最小法向曲率,并以此为阈值,获得小目标的候选对象.之后利用多幅图像之间目标的相关性,在时间尺度上基于Kalman滤波算法,实现对目标噪声的过滤并实现目标的跟踪.最后对算法的有效性进行了验证.     

基于分等级对象语义图模型的复杂目标自动识别方法研究

目标自动识别是图像处理领域的研究热点。针对现有方法的不足,该文提出一种新的基于分等级对象语义图模型的复杂目标自动识别方法。该方法通过构建分等级对象语义图模型增强对目标与背景间、目标部件间语义约束的利用,引入置信对象网络统计局部特性,利用消息机制传递对象间相互影响,实现概率语义分析。训练中还将产生式和判别式方法结合,提高了目标识别的准确度。在自然和遥感部分目标类别数据集上的测试结果表明,该方法能完成对多种类型和复杂结构目标的识别和提取,具有一定的实用价值。     

Urban object reconstruction using airborne laser elevation image and aerial image

Creating three-dimensional (3-D) models of real urban objects is an important goal in a wide variety of applications. This paper describes a method that utilizes airborne laser elevation images and aerial images for the 3-D reconstruction of urban objects. Our modeling approach uses the vertical geometric pattern analysis of elevation images. These patterns correspond to object contours and, thus, enable the extraction of the object. In addition, to provide realistic textured details, textures are cut from aerial images and mapped onto 3-D models. Our texture-mapping approach can avoid geometry mismatching and enable the automatic registration to determine the most reliable correspondence between projected outlines of 3-D models and contours of real objects shown in aerial images. Edge pairs, which are matched with projected outlines, are detected from aerial images. In order to minimize mismatching, we apply the voting technique based on the generalized Hough transform. Experimental results show that 3-D reconstruction of urban objects is generally successful.     

基于特征转移金字塔网络的SAR图像跨尺度目标检测

SAR图像多尺度目标检测能够实现大场景SAR图像中关键目标的定位与识别,是SAR图像解译的关键技术之一。然而针对尺寸相差较大的SAR目标的同时检测,即跨尺度目标检测问题,现有目标检测方法难以实现。该文提出一种基于特征转移金字塔网络(FTPN)的SAR图像跨尺度目标检测方法。在特征提取阶段采用特征转移方法,实现各层特征图的有效连接,实现不同尺度特征图的提取;同时采用空洞卷积群方法,增大特征提取的感受野,促使网络提取到大尺度目标特征。上述环节能够有效保留不同尺寸目标特征,从而实现SAR图像中跨尺度目标的同时检测。基于高分三号SAR数据、SSDD数据集及高分辨率SAR舰船检测数据集-2.0等数据集的试验表明,该文方法能够实现SAR图像中机场、舰船等跨尺度目标的检测,在已有数据集上mAP达96.5%,较特征金字塔网络算法提升8.1%,并且整体性能优于现阶段最新的YOLOv4等目标检测算法。      

结合分类与迁移学习的薄云覆盖遥感图像地物信息恢复

利用多源多时相遥感图像,给出一种结合分类与迁移学习的薄云覆盖遥感图像地物信息恢复算法.首先利用多方向非抽样对偶树复小波变换对多源多时相遥感图像进行多分辨率分解,对分解后的薄云图像的高频系数利用贝叶斯方法进行地物初分类;再对每类地物的低频系数通过迁移最小方差支持向量回归模型进行域自适应学习,获取模型参数;最后利用所获的迁移回归模型,用无云参考图像的低频系数预测薄云覆盖图像的低频系数,去除薄云,恢复薄云覆盖图像的地物信息.实验结果表明,本文算法恢复的地物细节清楚,光谱失真较小.特别对地物季节性变化的薄云覆盖遥感图像,本文算法能有效恢复薄云覆盖区域的地物信息.     

An Object-Based Method for Sharing Secret High-Dynamic-Range Images

With the popularity of the Internet, how to securely transmit and store multimedia data, and effectively access them has become an issue of great concern. High-dynamic-range (HDR) images are predicted to be a future trend and have already received considerable attention. In this study, an object-based method for sharing secret HDR images is proposed. This method comprised three steps. First, HDR images were divided into several objects and the coordinates of the objects and related information were recorded. Second, a method for sharing secret images was used to share the produced objects and recorded information, which were ranked according to importance. Third, a Lagrange interpolation method was used to reconstruct the various objects. The method can be used to maintain high information security and provide a convenient and effective image-restoration method to users. Experimental results confirmed the effectiveness of the method proposed in this study.     

Detection and Segmentation of Concealed Objects in Terahertz Images

Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed objects in broadband 0.1–1 THz images. Due to the inherent physical properties of passive terahertz imaging and associated hardware, images have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to segment or detect concealed objects. Our approach relies on two stages. First, we remove the noise from the image using the anisotropic diffusion algorithm. We then detect the boundaries of the concealed objects. We use a mixture of Gaussian densities to model the distribution of the temperature inside the image. We then evolve curves along the isocontours of the image to identify the concealed objects. We have compared our approach with two state-of-the-art segmentation methods. Both methods fail to identify the concealed objects, while our method accurately detected the objects. In addition, our approach was more accurate than a state-of-the-art supervised image segmentation algorithm that required that the concealed objects be already identified. Our approach is completely unsupervised and could work in real-time on dedicated hardware.      

Bayesian image segmentation using local iso-intensity structural orientation.

Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model.     

Three-dimensional visualization of partially occluded objects using integral imaging

A three-dimensional (3-D) scene may contain foreground objects which partially occlude background objects. In this paper, we present experiments on reconstruction of the unobstructed view of the background objects from a partially occluded 3-D scene using an integral imaging system. We have reconstructed images of the scene at various distances of interest through computational and optical integral imaging reconstruction in order to obtain images of the occluding foreground objects and partially occluded background objects.