数字全息图再现的有关参数选取

数字全息的再现过程是以数字的形式在计算机中完成的,对全息图有关参数的选取影响着数字全息再现像的结果。基于菲涅耳衍射理论和数字全息再现的原理,通过编程进行仿真实验,深入探讨了再现光波长、全息图像素元大小和再现距离等3个主要参数对再现像的影响。仿真实验结果表明,只要这些参数的取值处在合适的范围内,即可得到满足一定要求的清晰再现像。     

基于形态重构的豆粒分割算法

在实现大豆图像豆粒的分离时,针对分水岭算法中容易出现过分割等问题,综合利用H-Dome形态学重构以及距离变换改善梯度图局部多个极小值问题,再使用分水岭算法找到粘结边界点,从而将颗粒分离.实验结果验证了此方法的有效性.     

三种线性Bregman迭代重构算法性能分析

针对线性Bregman迭代在压缩感知稀疏信号重构中具有良好的重构性能和抗噪性能,从理论上对三种典型的线性Bregman迭代重构算法进行了分析比较,给出了算法的步骤和应用条件,然后基于CS重构指标进行了综合的仿真与分析,最后给出了三种算法的关系以及相应的结论。     

鄂尔多斯盆地下古生界构造-沉积演化、古地理重建及有利成藏区带划分

鄂尔多斯盆地下古生界是重要的天然气勘探层位,相继发现了奥陶系顶部风化壳、古隆起周缘中组合和马家沟组盐下等多套含气层系,证明了盆地下古生界具有良好的勘探开发潜力,也揭示出深化古地理重建工作的急迫性。在分析鄂尔多斯盆地构造-沉积演化过程的基础上,对早古生代重点时期的古地理进行了重建,进而提出了成藏区带划分的新思路。研究表明,鄂尔多斯盆地经历了太古宙—元古宙裂陷沉积、寒武纪—奥陶纪沉积-剥蚀、石炭纪—三叠纪拗陷稳定沉积、侏罗纪—白垩纪挤压成藏和晚白垩纪—现今调整定型等5大阶段。盆地下古生界古地理格局经历了早-中寒武世的古陆消减、沉积增长、边缘裂陷阶段和早奥陶世的隆-坳相间阶段,并控制了优质成藏区带的分布。结合近期勘探研究进展,依据源-储组合和空间分布,划分出了基于主控因素的4类成藏区带：潮缘滩白云岩成藏区、奥陶系盐下台内丘滩成藏区、西部双滩带白云岩和页岩成藏区以及岩溶-滩相白云岩成藏区。不同区带显示出了不同的成藏条件和主控因素,需要选择针对性的勘探策略。     

A novel grating matching method for 3D reconstruction

Automatic 3D surface reconstruction has been an important research topic in digital photogrammetry for many years. Shaping from stereoscopic information is one of the widely studied topics in computer vision. Its central part is to solve the stereo matching problem automatically. Most algorithms used to solve the matching problem can be categorized as either area-base techniques or feature-based techniques. Feature-based techniques have gained more and more popularity for and it is the method that supports activities in object recognition and image understanding. But none of the former research can ensure the 100% matching exactness, thus, cannot complete high precision 3D surface measurement. In order to improve the precision of 3D measurement, we design a novel grating matching method, which can ensure 100% matching accuracy. It does not need any other assistant symbol or flag, only select one of the gratings which have to be projected to the objects. On the beginning of the measurement, the background of the object is captured by a CCD camera. Later gratings include single grating and group gratings will be projected to the object in sequence. All the images include gratings will make a subtraction with the background. The difference of the two images will be treated as 3D cues to acquire the 3D shape. Because no recognizing work is needed, the location of the single grating and each one in the group can be positioned exactly. From a lot of experiments, the proposed grating matching method is proved and it is a technique with high precision, low costs, easy operation, and an automatically matching method. Furthermore, it can be widely used in most of 3D vision recovery systems.     

反向工程中测量点云配准的新方法

在光学非接触三维测量中，复杂对象的重构需要多组测量数据的配准。为了获得好的配准结果，提出一种新的配准方法——直接误差分析配准方法。该方法基于一种新型的双目CCD光栅投影测量系统进行测量和分析。实验结果表明该测量方法的配准误差大约0．2mm左右．从而证实该方法的可行性。     

基于逆向技术的奶勺模具设计与制造

以奶勺模具设计与制造为例,阐述了逆向工程的概念与特点,提出了模具逆向设计系统的架构,分析了其在模具设计中的应用技术,研究了基于Pro/Engineer开发的模具设计流程,对数据采集,曲面重构等关键问题做了详细分析.利用这种反求技术实现奶勺模具的设计与制造,可以大大缩短研发,制造周期,提高企业竞争力.     

Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

基于RGB-D数据的目标分割与实时重建方法

应用RGB-D传感器进行三维重建具有速度和成本的优势,并可获取纹理信息,但扫描数据易受干扰。针对有干扰的外部环境,提出一种基于RGB-D数据的目标分割与重建方法。首先为改善输入图像质量,采用多帧叠加的中值滤波对信息缺失的位置进行预处理。接着,利用静态减除和动态3D Mean-shift相结合的方法分割目标,实现对目标物体的实时定位。最后使用一种优化的点到面ICP方法进行配准,并在此基础上利用随机抽样算法对点云的配准进行加速。采用该方法实现的原型系统能够支持含有背景和一定干扰下的小物体配准,且能有效提高三维重建的速度和自动化程度。     

基于序贯蒙特卡洛算法的交通流事件重构

针对交通数据重构应用性差、缺乏对交通事件重构的研究等问题,结合交通流非线性非高斯的特点,提出一个基于序贯蒙特卡洛方法的交通流堵塞事件重构模型。该模型不断同化道路上的传感器数据,使仿真中的交通状态不断逼近真实路况,通过分析仿真数据以探测真实路网中存在的堵塞事件。模型能够对探测到的堵塞进行多粒子模拟来实现对真实道路上堵塞事件的重构。实验结果表明,该模型能够推测并重构出道路上的堵塞事件,对堵塞起始位置重构的平均误差为17m,对堵塞范围重构的平均覆盖率为82%。