【作者】 芦晓红；

【导师】 石祥滨；

【作者基本信息】 沈阳航空航天大学 ， 计算机应用技术， 2011， 硕士

【摘要】 近年来,三维重建技术在考古学、建筑学、地质学、虚拟现实、机器人导航、物体识别以及军事等领域得到越来越广泛的应用。同时,随着计算机技术及网络技术的迅速发展,利用计算机把图像或视频等二维结构信息重建出具有真实感的三维模型的三维重建技术也深入到人们生活的方方面面,成为当前新的研究热点。特征提取与匹配是三维重建中至关重要的一步,是欧氏重建和摄像机自标定等计算机视觉技术的基础,其匹配精度直接影响后续重建的效果。本文重点研究了特征提取与匹配技术中涉及的两个主要问题,其一是提高特征点描述子的独特性的研究,其二是序列图像中误匹配点剔除策略的研究。为了提高特征点描述子的独特性,本文提出一种基于圆环块邻域划分方式和规范化颜色照度不变量的描述子(CCD)。首先,在极坐标空间中采用圆环块结构划分特征点邻域,形成24个不重叠的子区域,并根据子区域与特征点的距离计算各子区域的权重;其次,根据各子区域内的规范化RGB三通道颜色强度差形成照度不变量;最后,结合权重与照度不变量,建立72维特征向量描述特征点。针对序列图像场景中存在大量相似结构而致使一些误匹配产生的问题,本文提出了一种基于几何约束关系的误匹配点剔除算法。在相同尺度下根据匹配点对的运动方向一致和运动距离相等的关系来剔除误匹配;在不同尺度下根据匹配点对间连线相交的关系来剔除误匹配。实验结果证明了在尺度缩放、视点变化、图像平移旋转等几何变换下,本文提出的相关算法均通过真实图像的实验加以验证,获得了满意的实验效果。更多还原

【Abstract】 In recent years, three-dimensional reconstruction technology is widely used in some applications such as archeology, architecture, geology, virtual reality, robot navigation, object recognition and military fields. Meanwhile, with the rapid development of computer technology and computer network, the 3D reconstruction can be seen in all aspects of life and also has become the new hotpot, the 3D reconstruction is the process converting two-dimensional structure information such as the images or videos to real three-dimensional model by the computers.Feature extraction and matching is the first and vital step of 3D reconstruction, and it is the base of Euclidean reconstruction and camera self-calibration etc. The precision of image matching directly affects the results of following steps in the whole process. This paper focuses on two problems of feature extraction and matching. One is the distinctiveness of descriptors, and the other is the strategy of mismatching elimination for sequence images.To improve the distinctiveness of descriptor, in this paper, a new descriptor based on circle-blocks neighborhood division and normalized color illumination invariant (CCD) is presented. First, it adopts circle-blocks structure to divide neighborhood of feature points into 24 non-overlapping sub-regions in the polar coordinate space, and then gives weight calculation method for evaluating the effect of every sub-region according to the distance between every sub-region and the feature point. Next, the illumination invariant is formed by the normalized RGB triple channels color intensity differences. Finally, a 72-dimensional feature vector will be established by combining the weights with illumination invariant.To solve the problem of mismatching caused by the large number of similar structures in sequence images, this paper presents a mismatching points elimination algorithm based on the geometric constraint conditions for sequence images. The mismatching points are eliminated according to the relationship of consistent motion orientations and equivalent motion distances between them in the same scale. And the mismatching points are eliminated according to the intersection relationship of lines between them in different scales.Experimental results on real images demonstrated that the new algorithms proposed in this paper are valid and robust under various geometric and photometric transformations application such as scale changes, viewpoint changes, translation transforms etc.更多还原