A High-Precision and High-Speed Stereo Matching Algorithm Based on Synergetics and its Application to Automatic Production of Rough 3 D City Area Map

为了解决高精度、高速度的立体匹配问题，本研究提出了一种新的方法，并将其应用于城市区域粗略地图的自动生成。1)新的立体匹配方法立体匹配问题的一个主要问题是高精度、高速度地获取立体图像中包括遮挡和反转位置在内的特征点的对应关系。提出了一种新的基于协同学的高精度、高速度的立体匹配算法，这是一个自治复杂系统的数学模型。2)基于双目立体数字地图的城市区域粗略地图的自动生成已从二维发展到三维，以满足高级导航的要求。三维数字地图生产的主要问题是降低其高昂的生产成本。在此基础上，研究了基于…的三维城市区域粗略地图自动生成方法越来越多的城市使用双目立体图像。该方法由以下步骤构成：(1)从一对立体图像中分割出物体、建筑物、街道、天空、路旁树木等区域；(2)通过立体匹配获得每个物体的三维坐标；(3)识别每个物体的多边形模型；(4)为每个物体粘贴纹理。对于步骤1)、步骤2)和步骤3)提出了一种实用的方法。3)双目立体物体识别自动生成三维城市区域地图需要根据物体的三维坐标来估计物体的形状。这自然扩展到从双目立体图像中识别对象。在此基础上，提出了一种基于K-均值聚类和带噪声模糊c-Varies的三维坐标噪声聚类方法，用于识别被摄体的个数和形状。本文提出的方法在被试个数小于4，且被试之间不接触的情况下是有效的。较少

This research proposes a new method in order to resolve a stereo matching problem with high-precision and high-speed, and applies to automatic production of rough 3 D city area map. The fruitful results are summarized as follows.1) New stereo matching methodA main issue of stereo matching problem is to obtain correspondence of characteristic points in stereo images including occlusion and reversal position with high-precision and high-speed. We propose a new stereo matching algorithm, which has the performance of high-precision and high-speed, based synergetics, which is a mathematical model for an autonomous complex system.2) Automatic production of rough 3 D city area map employing binocular stereoDigital map has been developed from 2 dimensions to 3 dimensions with requirement for advanced navigation. The main issue of the production of 3-dimensional digital map is to reduce its high production cost. Then, we investigated the automatic production method of rough 3D city area map tha … More t employs binocular stereo images for city area. The method is constructed by the followings: (1)region segmentation of subjects, building, street, sky, road side tree etc., from a pair of stereo images, (2)obtaining 3 dimensional coordinates of each subject by stereo matching, (3)identifying a polygon model for each subject, and (4)pasting texture for each subject. Here, the useful way, which is not complete yet, for steps 1),2), and 3) is proposed.3) Object recognition from binocular stereoThe automatic production of 3 D city area map needs to estimate the shapes of subjects from their 3 dimensional coordinates. This is naturally extended to recognize objects from binocular stereo images. Then we propose a new method, which consists of K-means clustering and fuzzy c-varieties with noise clustering for 3 dimensional coordinates including much noise, to recognize the number of subjects and the shapes of subjects. Here, the proposed method effectively works under the condition that the number of subjects is less than 4 and the subjects do not contact with each other. Less