基于莫尔条纹的三维物体的旋转不变识别

本文提出一种基于莫尔条纹的三维物体旋转不变识别方法。采用阴影莫尔法的装置和频域滤波的方法,可得到物体的莫尔条纹图。因为物体的莫尔条纹是物体的等高线,体现三维物体特征的高度函数以莫尔等高线的形式编码于强度图中,因此基于莫尔条纹的相关识别具有本征三维识别的特点。采用振幅调制和功率谱相减的联合变换相关,用实现旋转不变识别的综合鉴别函数(SDF)作为联合变换相关输入的参考图像,来获得具有识别目标的旋转不变性和高的相关识别率。计算机模拟试验结果证明了这种方法用于三维物体旋转不变性识别的有效性。     

微透镜阵列实现3维物体旋转不变实时识别

为了实现3维物体旋转不变实时识别,应用微透镜阵列的多视角成像特点,利用透射像阵列的高关联性,实现3维物体信息与2维透射像阵列信息之间的转换,从而可以利用光学2维图像识别技术实现3维物体的识别。对转换和识别过程进行了理论分析,用匹配滤波的方法进行了实验验证,实现了3维物体旋转不变实时识别。得到了良好的识别效果,并实现了旋转方向的准确定位和旋转角度大小的比较判别。结果表明,应用微透镜阵列可以实现旋转3维物体旋转不变实时识别。     

微透镜阵列实现3维物体旋转不变实时识别

为了实现3维物体旋转不变实时识别,应用微透镜阵列的多视角成像特点,利用透射像阵列的高关联性,实现3维物体信息与2维透射像阵列信息之间的转换,从而可以利用光学2维图像识别技术实现3维物体的识别.对转换和识别过程进行了理论分析,用匹配滤波的方法进行了实验验证,实现了3维物体旋转不变实时识别.得到了良好的识别效果,并实现了旋转方向的准确定位和旋转角度大小的比较判别.结果表明,应用微透镜阵列可以实现旋转3维物体旋转不变实时识别.     

旋转不变的三维物体识别

提出一种具有旋转不变性的三维物体识别的新方法。该方法通过结构照明的方法,使物体的高度分布以变形条纹的形式编码于二维强度像中。由于条纹图像包含有物体的高度分布信息,因而对条纹图的相关识别具有本征三维识别的特点。旋转不变性是通过使用多通道滤波器实现的,此滤波器可以由不同方向三维物体对应的变形条纹图像经计算机处理得到。相关识别方法可以用光学匹配空间滤波器实现。计算机模拟实验证明了这种方法的有效性,它不仅可以实现旋转不变的三维物体识别,还可以给出物体旋转角度的估计值。     

综合判别函数相关器实验研究

本文采用综合判别函数(SDF)构成了一个畸变不变相关滤波系统,并利用它进行了一系列实验研究,实验结果证明:以SDF技术与相干光空间匹配滤波相结合的光电混合相关器能够实现畸变(旋转、比例、方位和位移)不变相关识别.     

基于三维集成成像相机阵列获取的元素图像校正

利用相机阵列获取三维信息实现三维集成成像与显示时,为消除相机阵列空间位置偏差对元素图像阵列的影响,提高再现三维图像的质量,以相机阵列记录系统为基础提出了一种元素图像阵列校正方法。通过特征点位置坐标以及相机位置平移误差和旋转误差的计算,分析了相机阵列位置平移误差和旋转误差与元素图像间的关系,以及校正算法的精度。利用光学实验对该算法进行了验证,结果表明,此方法可有效消除相机阵列位置偏差对元素图像阵列的影响,并且校正后再现三维图像质量明显优于误差图像,峰值信噪比提高了33.6%,实现了基于三维集成成像相机阵列获取的元素图像校正,满足了集成成像的显示要求。     

基于集成成像的生物微小组织三维信息获取方法

提出一种将集成成像和光学显微镜结合实 现生物微小组织三维信息获取的新方法。以显 微镜为基础,结合集成成像特性,应用光学成像理论设计了与光路匹配的微透镜阵列,研制 了微弱光下的 三维信息获取系统。与已有方法相比,本文方法采用新的成像光路和三维信息获取器件,因 而放大倍数更高, 可记录更多的生物微小组织三维信息。针对生物微小组织,利用集成显微成像装置获得了 单元像阵列图像实验数据,分析了样本的三维结构和信息,验证了本文系统在微型组织三维 信息获取方面的有效性。     

用于三维成像的激光传感器旋转扫描系统

以实现物体三维成像为目的,设计用于三维成像的激光传感器旋转扫描系统。激光传感器采集物体光条图像,将光条图像传到上位机,使用旋转扫描测量模型获取物体光束的三维空间坐标数据,使用OpenGL开发图形库构建物体三维云图,将光条图像按照3D轮廓扫描顺序获取物体红、绿、蓝分量,将这些分量与物体3D空间坐标数据整合后,完成物体三维云图着色。实验结果表明：该系统具备较丰富的伺服控制功能,且标定物体光平面坐标误差仅为0.005 mm;物体三维成像逼真度较高,与实物颜色较为接近,三维成像效果优秀。     

集成成像同名像点自筛选技术

从集成成像三维获取技术出发,利用光线追迹的方 法,理论分析了元素图像阵列中同名像点之间的关联性,得到了同名像点之间的等差关系, 并利用此关系式对光学获取的元素图像阵列提取的同名像点的结果进行筛选,去除误差大的 配准结果,从 而提升同名像点的配准精度。光学实验结果显示,对于同一物体进行三维获取,经本文的集 成成像 同名像点自筛选技术得到的结果是500mm,相对误差为0.4% ;而传统技术重构得到的结果为502mm,相对 误差为0.8%。我们所提的这种方法有效提升了再现精度,提升幅度为 50%,为集成成像三维形貌获取技术实现高精度获取提供了有效的技 术支持。     

基于相位映射的三维主动视觉标定

通过在结构光发射单元和成像单元间施加几何约束条件,建立统一的坐标系.利用条纹投影和相位映射技术,构造了将编码相位图映射为物体的三维空间坐标的数学模型,进而获得深度图像空间坐标的计算值,然后将其与物体空间的三维标定数据基准进行比较,建立目标函数为误差平方和最小的非线性优化方程.两步法迭代求解这个优化方程,最终获得三维系统的结构参数.实验结果证明了本文提出的三维成像系统标定理论和方法的有效性.     

采用人工神经网络技术改善光学模式识别系统的识别效率

在采用光学模式识别技术,ＳＤＦ滤波技术进行实际场景中的三维目标畸变不变识别的时候,由于面对的晨在量的非训练像的相关识别,加上场景图像中的不同噪声,背景的干扰,以及硬件识别系统的各种非理想特征等因素,均不可避免带来相关平面的Ｓ／Ｎ的严重退化,从而使按通常的阈值技术进行相关信号分割的在而大大降低了ＯＰＲ系统的识别效率。     

地面目标激光回波特征实验研究

对地观测激光扫描成像直接利用激光回波信息生成目标的灰度像，进行目标分类识别，因此，需要研究并建立激光回波特征与地面目标特征之间的关系模型。运用对地观测激光成像原理试验系统的实验结果分析了地面目标的脉冲激光回波的功率与波形等主要特征，探讨了目标后向散射率特性。通过机载信息获取与数据处理得到了由激光回波生成的局部区域灰度像，并且与被动红外成像结果的比较初步得到了地面目标与其激光回波特征之间的关系，从而为地面目标的激光三维成像与分类识别提供了部分技术支持。     

智能型目标识别系统

介绍了图像目标识别技术中的图像分割，不变性参数提取和目标分类，利用图像目标的均匀性和相应知识自适应地分割和提取图像目标，被提取的每个图像目标的不变性参数由归一化过程和Ｚｅｒｎｉｋｅ矩提取，并利用ＭＰＮＮ模型将图像目标分类，实验结果该识别系统能识别光照不均匀或复杂背景下的图像目标。     

3-d object retrieval and recognition with hypergraph analysis

View-based 3-D object retrieval and recognition has become popular in practice, e.g., in computer aided design. It is difficult to precisely estimate the distance between two objects represented by multiple views. Thus, current view-based 3-D object retrieval and recognition methods may not perform well. In this paper, we propose a hypergraph analysis approach to address this problem by avoiding the estimation of the distance between objects. In particular, we construct multiple hypergraphs for a set of 3-D objects based on their 2-D views. In these hypergraphs, each vertex is an object, and each edge is a cluster of views. Therefore, an edge connects multiple vertices. We define the weight of each edge based on the similarities between any two views within the cluster. Retrieval and recognition are performed based on the hypergraphs. Therefore, our method can explore the higher order relationship among objects and does not use the distance between objects. We conduct experiments on the National Taiwan University 3-D model dataset and the ETH 3-D object collection. Experimental results demonstrate the effectiveness of the proposed method by comparing with the state-of-the-art methods.     

3-d modeling from a single view of a symmetric object

3-D technologies are considered as the next generation of multimedia applications. Currently, one of the challenges faced by 3-D applications is the shortage of 3-D resources. To solve this problem, many 3-D modeling methods are proposed to directly recover 3-D geometry from 2-D images. However, these methods on single view modeling either require intensive user interaction, or are restricted to a specific kind of object. In this paper, we propose a novel 3-D modeling approach to recover 3-D geometry from a single image of a symmetric object with minimal user interaction. Symmetry is one of the most common properties of natural or manmade objects. Given a single view of a symmetric object, the user marks some symmetric lines and depth discontinuity regions on the image. Our algorithm first finds a set of planes to approximately fit to the object, and then a rough 3-D point cloud is generated by an optimization procedure. The occluded part of the object is further recovered using symmetry information. Experimental results on various indoor and outdoor objects show that the proposed system can obtain 3-D models from single images with only a little user interaction.     

Hierarchical scale-based multiobject recognition of 3-D anatomical structures

Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1), we propose intensity weighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2), we integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition, we use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as well as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning, we also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship between the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.     

Background removal of multiview images by learning shape priors.

Image-based rendering has been successfully used to display 3-D objects for many applications. A well-known example is the object movie, which is an image-based 3-D object composed of a collection of 2-D images taken from many different viewpoints of a 3-D object. In order to integrate image-based 3-D objects into a chosen scene (e.g., a panorama), one has to meet a hard challenge--to efficiently and effectively remove the background from the foreground object. This problem is referred to as multiview images (MVIs) segmentation. Another task requires MVI segmentation is image-based 3-D reconstruction using multiview images. In this paper, we propose a new method for segmenting MVI, which integrates some useful algorithms, including the well-known graph-cut image segmentation and volumetric graph-cut. The main idea is to incorporate the shape prior into the image segmentation process. The shape prior introduced into every image of the MVI is extracted from the 3-D model reconstructed by using the volumetric graph cuts algorithm. Here, the constraint obtained from the discrete medial axis is adopted to improve the reconstruction algorithm. The proposed MVI segmentation process requires only a small amount of user intervention, which is to select a subset of acceptable segmentations of the MVI after the initial segmentation process. According to our experiments, the proposed method can provide not only good MVI segmentation, but also provide acceptable 3-D reconstructed models for certain less-demanding applications.