基于距离图像的定性识别

本文提出的定性描述图表示方法能在无模型指导的情况下有效地实现遮挡物体的分离,并将识别过程转化为图或子图同构的问题,还提出了用"提纯"与"搜索"相结合的递归过程来判定图或子图同构.实验证明了该方法的有效性和稳定性.     

使用范围数据的三维物体的识别

本文所使用的系统有二个工作阶段。在知识阶段,含有单个物体的景物每次一个地示给系统,景物的范围数据可利用范围测距仪获得。根据区域特性和各区域间的关系建立每一景物的描述并将这种描述存储作为物体的模型。在识别阶段,以与知识阶段同样的方式对未知景物的特征进行描述,然后把这一描述和物体模型匹配,结果可实现成堆物体按顺序地逐个识别。为提高匹配效率,可把数据驱动和模型驱动搜索过程紧密结合。实验结果表明,这一方法是很有希望的。     

显微图象中目标对象的识别

研究了显微图象中目标对象的识别问题。首先比较了显微图象和宏观图象之间的区别,总结了大部分显微图象具有的一般特征,讨论了宏观图象处理方法用于显微图象时存在的缺陷。然后提出了一种目标对象识别的新方法,该方法利用最优颜色通道识别显微图象中的目标对象,动态地调节识别阈值,减少了误识别象素的数量。对残存的误识别象素和脏点象素采用面积滤波的方式去除,进一步提高识别的效果。该文通过实验,识别了几种显微图象中的目标对象,实验发现,使用这一方法不仅能够提取背景中的目标对象,还可以大量减少误识别象素和脏点象素,说明该方法用于显微图象中目标对象的识别具有一定的优点。     

三维物体识别研究进展

出于工业和医疗等领域大量现实应用的需要,如今三维物体识别已成为一个很活跃的研究领域。一般来说,三维物体识别系统可以通过两个阶段的处理来完成三维物体的识别和定位,首先用传感器获取的场景输入数据来得到场景的表达;然后将它与数据库中存储的物体表达相匹配。为了推动该领域研究进一步发展,因而对近１０ａ年中该识别过程中必须解决的感传器类型、三维物体表达方法和匹配策略等３个方面问题的研究成果进行了综述,对主要方法进行分类和总结;并提出了一些三维视觉系统中还需要深入研究的问题,包括对所研究物体形状的限制、复杂背景的影响和表达以及识别中的“整体和局部”的矛盾等。     

由深度数据重建三维物体的一种方法

讨论由深度数据重建三维物体的一种新方法.其基本思想是根据由基于物体旋转的主动式三角法三维信息获取系统所获取的物体深度数据作为该物体的型值点.再根据型值点进行网格划分,以便形成满足一定约束条件的空间拓扑网格.最后使用有理Bezier三角曲面片与矩形曲面片相结合的方法进行曲面拟合与逼近.     

Volumetric Reconstruction of Objects and Scenes Using Range Images

This paper reviews volumetric methods for fusing sets of range images to create 3D models of objects or scenes. It also presents a new reconstruction method, which is a hybrid that combines several desirable aspects of techniques discussed in the literature. The proposed reconstruction method projects each point, or voxel, within a volumetric grid back onto a collection of range images. Each voxel value represents the degree of certainty that the point is inside the sensed object. The certainty value is a function of the distance from the grid point to the range image, as well as the sensor's noise characteristics. The super-Bayesian combination formula is used to fuse the data created from the individual range images into an overall volumetric grid. We obtain the object model by extracting an isosurface from the volumetric data using a version of the marching cubes algorithm. Results are shown from simulations and real range finders.     

Efficient Detection and Recognition of 3D Ears

The use of ear shape as a biometric trait is a recent trend in research. However, fast and accurate detection and recognition of the ear are very challenging because of its complex geometry. In this work, a very fast 2D AdaBoost detector is combined with fast 3D local feature matching and fine matching via an Iterative Closest Point (ICP) algorithm to obtain a complete, robust and fully automatic system with a good balance between speed and accuracy. Ear images are detected from 2D profile images using the proposed Cascaded AdaBoost detector. The corresponding 3D ear data is then extracted from the co-registered range image and represented with local 3D features. Unlike previous approaches, local features are used to construct a rejection classifier, to extract a minimal region with feature-rich data points and finally, to compute the initial transformation for matching with the ICP algorithm. The proposed system provides a detection rate of 99.9% and an identification rate of 95.4% on Collection F of the UND database. On a Core 2 Quad 9550, 2.83 GHz machine, it takes around 7.7 ms to detect an ear from a 640×480 image. Extracting features from an ear takes 22.2 sec and matching it with a gallery using only the local features takes 0.06 sec while using the full matching including ICP requires 2.28 sec on average.     

Segmentation of 3D Range Images Using Pyramidal Data Structures

Given a 3D range image of a scene containing multiple arbitrarily shaped objects, we segment the scene into homogeneous surface patches. A new modular framework for the segmentation task has been proposed. In the first module, oversegmentation is achieved using zeroth and first-order local surface properties. The segmentation is then refined in the second module using high order surface representations dictated by the upper level vision tasks. The procedure has been applied successfully to many range images obtained from various institutions, 11 of which are presented here.     

基于形态图表示的三维物体识别算法

基于形态图表示的三维物体识别的基本思路是：首先建立待识别物体的模型库，找出模型集中所有模型物体的形态图和特征视图，并提取以它们的拓扑结构信息和几何信息；其次对物体真实图像作轮廓提取和0边界跟踪，得到二维图像的线架图，同时提取出它的拓扑结构信息和几何信息；最后将物体图像的拓扑结构信息和几何信息与模型库中模型物体的拓扑结构信息和几何信息匹配，从而达到识别的目的。文中提出了在生成线图链码时提取其拓扑结构信息和几何信息的方法，由拓扑结构信息和几何信息构造特征矩阵的方法，以及识别过程中特征矩阵的匹配算法。     

Invariant-Based Recognition of Complex Curved 3D Objects from Image Contours

This paper addresses the problem of recognizing three-dimensional objects bounded by smooth curved surfaces from image contours found in a single photograph. The proposed approach is based on a viewpoint-invariant relationship between object geometry and certain image features under weak perspective projection. The image features themselves are viewpoint-dependent. Concretely, the set of all possible silhouette bitangents, along with the contour points sharing the same tangent direction, is the projection of a one-dimensional set of surface points where each point lies on the occluding contour for a five-parameter family of viewpoints. These image features form a one-parameter family of equivalence classes, and it is shown that each class can be characterized by a set of numerical attributes that remain constant across the corresponding five-dimensional set of viewpoints. This is the basis for describing objects by “invariant” curves embedded in high-dimensional spaces. Modeling is achieved by moving an object in front of a camera and does not require knowing the object-to-camera transformation; nor does it involve implicit or explicit three-dimensional shape reconstruction. At recognition time, attributes computed from a single image are used to index the model database, and both qualitative and quantitative verification procedures eliminate potential false matches. The approach has been implemented and examples are presented.     

面向移动对象的高效预测范围聚集查询方法

预测范围聚集查询是移动对象数据库中重要的查询类型之一.提出了一种PRA树高效预测范围聚集查询索引,对速度域进行规则划分,根据速度矢量大小将移动对象映射到不同的速度桶中,针对每个速度桶,提出了一种聚集TPR树索引,通过在TPR树中间节点中加入聚集信息以减少预测范围聚集查询所需要的节点访问代价.PRA树索引增加了一个建于叶节点之上的Hash辅助索引结构,并采用自底向上的删除搜索算法,具有很好的动态性能和并发性.提出了一种增强预测范围聚集查询EPRA算法,采用更精确的剪枝搜索准则,减少了查询所需要访问的节点代价.实验结果与分析表明,基于PRA树索引的EPRA查询算法具有良好的查询性能,优于通用的TPR*树索引.     

三维工业物体的多重描述与识别处理

提出了一种三维工业物体的多重描述与识别方法。在描述中采用了整体形状描述，基元属性描述和关系描述相结合的多重描述策略；在识别过程中，首先根据物体的整体描述产生关于物体身份的初步假设，然后提出三种修剪算法，充分利用关系数据结构的多重约束作为知识启发来修剪搜索树，最后根据距离测度函数实现精匹配。实验验证了上述处理的正确性和一般性。     

三维工业物体的多重描述与识别处理

提出了一种三维工业物体的多重描述与识别方法，在描述中采用了整体形状描述，基元属性描述和关系 描述相结合的多重描述策略；在识别过程中#首先根据物体的整体描述产生关于物体身份的初步假设，然后提出三 种修剪算法，充分利用关系数据结构的多重约束作为知识启发来修剪搜索树#最后根据距离测度函数实现精匹配。 实验验证了上述处理的正确性和一般性。     

Least Committed Splines in 3D Modelling of Free Form Objects from Intensity Images

Generating 3D models of objects from video sequences is an important problem in many multimedia applications ranging from teleconferencing to virtual reality. In this paper, we present a method of estimating the 3D face model from a monocular image sequence, using a few standard results from the affine camera geometry literature in computer vision, and spline fitting techniques using a modified non parametric regression technique. We use the bicubic spline functions to model the depth map, given a set of observation depth maps computed from frame pairs in a video sequence. The minimal number of splines are chosen on the basis of the Schwartz's Criterion. We extend the spline fitting algorithm to hierarchical splines. Note that the camera calibration parameters and the prior knowledge of the object shape is not required by the algorithm. The system has been successfully demonstrated to extract 3D face structure of humans as well as other objects, starting from their image sequences.     

遥感图像三维目标多视点建模识别方法

研究利用遥感灰度图像识别三维目标时的问题，提出一种基于多视点模型的匹配识别方法。使用3D广义锥模型投影得到二维视面模型，用于描述不同视点下的目标视面，并用几何结构特征来检索模型库。匹配过程层次地使用全局的与局部的、统计的与结构的目标识别特征，分为检索和精确匹配两个步骤完成。通过实验验证了该方法的有效性。     

小物体三维重建中影像与几何模型的配准方法

本文提出了一种快速有效的二维影像与三维几何模型之间的配准方法。首先在平面标定板的辅助下获取所有影像的内外方位元素,其次利用少量的人工控制点做绝对定向,获取影像与三维几何模型之间的配准参数的初始值,最后利用整体互信息测度做Powell方法非线性优化,获取配准参数的精确值。实验表明,经过互信息优化可以获得子像素级的配准精度。本文的方法具有速度快、人工交互少、精度高的特点。     

飞行目标红外多光谱高效识别算法

随着新型诱饵的快速发展,在日益复杂的目标环境中探测识别真假目标是红外探测识别系统最难解决的技术问题之一.通过分析天空背景下红外小目标,干扰物,噪音及背景的光谱特性,利用人造飞行目标光谱辐射强度高且相邻波段光谱辐射强度连续性特点,提出了一种以多光谱辐射强度和梯度相组合的目标识别高效算法.就此给出了相应的仿真算例,验证了算法在获取的红外图像信噪比很低,背景高亮度,多个诱饵干扰的条件下也能准确识别目标,具有更强的自适应性,更高的识别率和更为快捷的处理能力.     

一个针对遥感图像特定目标的自动识别系统

介绍了一个应用于自动识别遥感图像中的特定目标的原型系统,包括系统结构与识别流程, 以及系统中所采用的基于直线特征的建模方法和图像与模型匹配算法。目标模型包括分类模型和 表述模型两种,前者描述目标的类别属性,用来快速判断目标的存在性与大概位置;后者是对目标 几何结构与物理特性的定量描述,用于鉴别同类目标中的特定个体。匹配算法中层次性地使用了几 何的和物理的多种特征,并考虑了直线提取时可能出现的错误结果,具有较好的稳定性和较高的匹 配效率。     

Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding

This article formulates a near-lighting shape-from-shading problem with a pinhole camera (perspective projection) and presents a solution to reconstruct the Lambertian surface of bones using a sequence of overlapped endoscopic images, with partial boundaries in each image. First we extend the shape-from-shading problem to deal with perspective projection and near point light sources that are not co-located with the camera center. Secondly we propose a multi-image framework which can align partial shapes obtained from different images in the world coordinates by tracking the endoscope. An iterative closest point (ICP) algorithm is used to improve the matching and recover complete occluding boundaries of the bone. Finally, a complete and consistent shape is obtained by simultaneously re-growing the surface normals and depths in all views. In order to fulfill our shape-from-shading algorithm, we also calibrate both geometry and photometry for an oblique-viewing endoscope that are not well addressed before in the previous literatures. We demonstrate the accuracy of our technique using simulations and experiments with artificial bones.