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。     

π相移技术在钢轨三维面型复原中应用效果分析

在傅里叶变换轮廓术(FTP)中,频谱中的基频分量包含物体的高度信息,基频分量的提取效果对物体三维形貌复原的精度有重要影响。通过π相移技术可以消除背景分量,即零频部分,从而能更好地提取出基频分量,减小测量误差。文中通过计算机仿真对含有噪声的光栅条纹图像进行三维恢复,证实了π相移技术对提高FTP测量精度的作用。并将该方法应用于钢轨表面轮廓的三维复原和测量,为钢轨磨耗和表面缺陷的测量提供有效方法。     

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。     

三维面形测量中小波变换和傅里叶变换的对比研究

傅里叶变换轮廓术（Fourier transform profilometry,简称FTP）进行三维面形测量时,若无频谱混叠,可以得到很好的测量效果。但由于FTP是全局变换,频域内丢失了空间信息。当被测物体形状复杂或被噪声严重污染时,频域中频谱分布展宽,可能发生频谱混叠,导致基频分量提取不完整,从而不能正确地恢复出被测物体。本文利用小波具有的局部分析能力和噪声抑制能力,采用小波变换的方法（Continuous Wavelet Transform,简称CWT）从混叠条纹和噪声条纹中提取出完整的基频分量。我们采用Morlet复小波函数对变形光栅条纹进行处理,详细研究了CWT和FTP两种方法在不同情况下的优缺点,并通过计算机模拟和实验证实理论分析的正确性。     

一种提高复合光栅实时三维测量精度的方法

提出了一种采用组合滤波窗提高复合光栅实时三维测量精度的新方法,通过对复合光栅相位测量轮廓术(PMP)原理的分析,发现当从采集的变形复合光栅中解调相移变形条纹时,解调精度与滤波窗的选择有关。通过对几种常见滤波窗函数的分析和比较,设计了一种将汉宁窗和矩形窗相结合的新型滤波窗。由于组合窗的滤波精度与物体频谱分布情况(面型情况)有关,在复合光栅三维实时测量中,对频谱成分适中的Peaks函数物体进行数字模拟,得到测量该类物体所需的组合窗口优化参数分布图,用得到的理论数据指导处理频谱成分适中的实物实验,有效提高了复合光栅实时三维测量精度。数字模拟和实验均证实了该方法的有效性和适用性。     

用条纹时问平均法分析薄膜振动模式

提出一种对薄膜的振动模式进行定性分析和识别的方法一结构光条纹时间平均法.该方法采用结构光三维传感技术,用低帧频商用CCD相机记录由光栅投影到振动薄膜而上、因薄膜振动导致条纹局邴模糊的一系列变形正弦条纹,经过傅里叶变换、频谱滤波、逆傅里叶变换、取模等处理得到被测薄膜的振动模式分布.本文给出了该方法的理论分析,推导了相应的计算公式.计算机模拟和实验验证了该方法的可行性:能够对在不同激励频率下薄膜的振动模式进行定性分析和识别.在实验中,如果连续改变振动物体的振动激励频率,可以清楚地观察到物体振动模式的变换过程.实验证明,本文方法具有数据获取速度快,全场测量,实验装置简单等优点.     

基于相移法的多目标运动物体三维重构

相移法可实现静态物体三维形貌的高精度重构,对于运动物体形貌重构则误差较大。其根本原因为相移法需要多个条纹图进行物体重构,而传统相移法理论没有包含物体的运动信息,无法描述物体运动对相位的影响。导致当物体在条纹图间发生运动时测量误差较大。针对以上问题,提出了一种利用物体运动信息对多个二维运动物体进行三维重构的新方法。不同的被测物体可具有不同的运动轨迹。首先,对多个被测物体进行识别并确定目标区域;然后,采用KCF算法对物体进行跟踪并使用SIFT算法提取物体运动前后的特征点,分别估计描述物体运动的旋转平移矩阵。将运动信息带入条纹描述方程中,获得包含运动信息的三维重构模型,最终采用最小二乘法提取正确的相位值。结果证明:该方法能有效地减少由物体运动引起的测量误差,扩展了三维测量的应用范围,具有较高的工业应用价值。     

基于结构光投影的三维物体识别

本文提出了一种三维物体识别的新方法,将一正弦条纹投影到物体表面,摄像机得到的是有变形条纹的二维强度像。取出有变形条纹的二维强度像的基频分量的位相部分构造一个纯相位数字滤波器,以受到不同物体表面高度调制的有变形条纹的二维强度像作为输入图像,在频域中完成基频分量之间的相关,最后根据输出的相关峰值大小即可判别不同的物体。     

绝对相位展开方法研究

提出一种新光学三维传感方法。该方法通过投影多组周期不同的正弦光栅条纹到被测物体表面,通过相移算法,反解各组投影条纹的截断相位,并采用所提绝对相位展开方法,对截断相位进行了解调,实现了对物体表面的测量。与双频光栅投影测量方法及传统时间相位展开方法相比,该方法投影条纹组数灵活、测量效率高,实验证明该方法的适用性。     

基于彩色条纹编码的三维重建系统的研究

提出了一种正弦光栅彩色条纹的编码新方法,用于物体的三维重建。该方法利用投影仪投射出彩色正弦光栅彩色条纹序列对物体进行照射,采用照相机拍摄物体表面的变形条纹图,对图片进行处理,进而恢复出物体的三维轮廓。经过模拟实验表明,该方法具有较高的重建精度和良好的实时性品质指标。     

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。     

集成成像系统实现三维物体旋转不变分类识别

提出一种基于集成成像系统和综合判别函数(SDF)实现三维物体旋转不变分类识别的新方法。方法利用集成成像系统获取旋转三维物体的单元像阵列图像,应用图像中各个单元像记录的三维物体信息之间的高关联特性,结合SDF,实现了三维物体的旋转不变分类识别。与已有方法相比,本文方法减小了识别过程的数据量,降低运算复杂度,提高了识别效率,且不受物体旋转角度的限制。针对多类旋转三维物体,利用本文方法实现了旋转不变分类识别,验证了方法的有效性。     

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 object recognition using 2-D views

We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood of each hypothesis is then computed using the probabilistic models of shape appearance. Only hypotheses ranked high enough are considered for further verification with the most likely hypotheses verified first. The proposed approach has been evaluated using both artificial and real data, illustrating promising performance. We also present preliminary results illustrating extensions of the AFoVs framework to predict the intensity appearance of an object. In this context, we have built a hybrid recognition framework that exploits geometric knowledge to hypothesize the location of an object in the scene and both geometrical and intesnity information to verify the hypotheses.     

A Fast Algorithm for Recognizing Translated, Rotated, Reflected, and Scaled Objects From Only Their Projections

The subject of 2-D and higher dimensional object recognition finds widespread applications in areas such as image registration and pattern recognition. Radon transform is one technique used for efficient object matching (e.g., and ). However, so far as we know, no results have been obtained that solves the recognition problem completely in the projection domain due to coupling of transform parameters. We develop a novel method for such parameter decoupling and an improved phase correlation method for accurate practical shift estimation, resulting in a fast matching algorithm based on projection data only. Simulation results show that the proposed algorithm is much faster than similar state-of-the-art approaches such as that in with comparable estimation accuracy.      

条纹投影动态3维测量中相位高精度估计

为了提高条纹投影动态3-D形貌测量精度, 采用加窗傅里叶分析辅助相移的方法来减小运动导致的相移误差。首先采用加窗傅里叶分析法估计变形条纹间的实际相移量, 然后采用最小二乘法估计出变形条纹的高精度相位分布, 最后由估计的相位计算得到场景3维形貌。理论分析了物体运动对相移量的影响, 通过仿真研究了所提方法的相移量估计精度, 并搭建了实验系统进行验证。结果表明, 实验中采用所提方法的相位恢复精度达到0.1673rad, 比现有方法有明显提高。该方法用来提高条纹投影动态3-D形貌测量中相位精度是有效的。     

Medical image segmentation by combining graph cuts and oriented active appearance models

In this paper, we propose a novel method based on a strategic combination of the active appearance model (AAM), live wire (LW), and graph cuts (GCs) for abdominal 3-D organ segmentation. The proposed method consists of three main parts: model building, object recognition, and delineation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the recognition part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW methods, resulting in the oriented AAM (OAAM). A multiobject strategy is utilized to help in object initialization. We employ a pseudo-3-D initialization strategy and segment the organs slice by slice via a multiobject OAAM method. For the object delineation part, a 3-D shape-constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT data set and also on the MICCAI 2007 Grand Challenge liver data set. The results show the following: 1) The overall segmentation accuracy of true positive volume fraction TPVF > 94.3% and false positive volume fraction can be achieved; 2) the initialization performance can be improved by combining the AAM and LW; 3) the multiobject strategy greatly facilitates initialization; 4) compared with the traditional 3-D AAM method, the pseudo-3-D OAAM method achieves comparable performance while running 12 times faster; and 5) the performance of the proposed method is comparable to state-of-the-art liver segmentation algorithm. The executable version of the 3-D shape-constrained GC method with a user interface can be downloaded from http://xinjianchen.wordpress.com/research/.     

IDENTIFICATION OF 3-D OBJECTS FROM THREE ORTHOGONAL SILHOUETTES USING NORMALIZED LINEAR QUAD-OCTREES

Linear octrees offer a volume representation of 3-D objects, which is quite compactand lends itself to traditional object processing operations. However, the linear octree structurefor generating the representation of 3-D objects from three orthogonal silhouettes by using thevolume intersection technique is dependent on viewpoints. The recognition achieved from match-ing object representations to model representations requires that the representations of objectsare independent of viewpoints. In order to obtain independent representations of viewpoints,the three principal axes of the object should be obtained from the moment of inertia matrix bycomputing its eigenvectors. The linear octree is projected onto the image planes of the three prin-cipal views (along the principal axes) to obtain the three normalized linear quadtrees. The objectmatching procedure has two phases: the first phase is to match the normalized linear quadtrees ofthe unknown object to a subset of models contained in a library utilizing a measure of symmetricdifference; the second phase is to generate the normalized linear octrees of the object and theseselected models and then to match the normalized linear octree of the unknown object with themodel having the minimum symmetric difference.