基于局部特征的三维物体识别方法研究

发布时间：2018-02-15 01:27

本文关键词： 局部特征特征点检测协方差描述子特征点匹配三维物体识别　出处：《中北大学》2017年硕士论文　论文类型：学位论文

【摘要】：三维物体识别在计算机视觉中是一项十分重要的基础性研究,它是对远程遥感、生物医疗、机器人在内的等众多领域进行研究的前提和基础,因此三维物体识别有着广泛的应用前景。3D物体识别方法大致可以分为两类:基于全局特征的识别方法和基于局部特征的识别方法。由于基于全局特征的识别方法忽略了物体的一些局部信息,所以在一些杂乱的、存在遮挡的场景中,不能很好的识别,而基于局部特征的三维物体识别技术有更大的优势。基于局部特征的三维物体识别方法大体上可以分为三个阶段:3D特征点的检测,特征点的描述和局部曲面的匹配。本文围绕这三个核心阶段,对基于局部特征的三维物体识别方法进行了深入的研究,主要做了以下几个方面的工作。(1)针对物体的尺度不变性特征以及传统算法对噪声敏感等问题,本文提出了一种基于散乱点云的多尺度特征点提取算法,通过改变局部邻域的大小来构造尺度空间进行多尺度分析,在不同的尺度下通过对局部邻域的协方差分析来计算曲面变化值,找到具有尺度不变性的特征点。同时还引入了基于形状索引值的点签名方法,增强了对噪声的鲁棒性。(2)针对传统描述子维数过大,匹配时间较长等问题。本文提出了一种几何协方差描述子,利用特征点与邻域点间法向量夹角、距离等几何特征构造协方差矩阵来描述特征点。实验表明,此描述子不仅具有较强的描述能力,而且具有旋转平移不变性,对噪声不敏感,对点云采样密度也具有较强的鲁棒性。(3)在曲面匹配过程中,针对特征点最近邻匹配后还存有一定的误匹配对,本文将典型相关分析引入到特征点的误匹配剔除上来,最终得到了较好的匹配效果。
[Abstract]:3D object recognition is a very important basic research in computer vision. It is the premise and foundation of remote sensing, biomedicine, robot and so on. Therefore, 3D object recognition has broad application prospects. 3D object recognition methods can be roughly divided into two categories: global feature based recognition method and local feature based recognition method. Because of the global feature based recognition method, 3D object recognition method can be divided into two categories: global feature based recognition method and local feature based recognition method. Ignoring some local information about the object, So in some cluttered, occluded scenes, they can't be recognized very well. The 3D object recognition method based on local features can be divided into three stages: detection of 3D feature points. The description of feature points and the matching of local surfaces. In this paper, the method of 3D object recognition based on local features is deeply studied around these three core stages. In this paper, a multi-scale feature extraction algorithm based on scattered point cloud is proposed to solve the problem of object scale invariance and the sensitivity of traditional algorithm to noise. By changing the size of the local neighborhood to construct the scale space for multi-scale analysis, the surface variation value is calculated by the covariance analysis of the local neighborhood at different scales. At the same time, a point signature method based on shape index value is introduced, which enhances the robustness to noise. In this paper, a geometric covariance descriptor is proposed, in which the normal vector angle and distance between feature points and neighborhood points are used to construct the covariance matrix to describe the feature points. This descriptor not only has strong description ability, but also has rotation translation invariance, is not sensitive to noise, and has strong robustness to point cloud sampling density. Because there are some mismatch pairs after nearest neighbor matching of feature points, this paper introduces the canonical correlation analysis into the feature points' mismatch elimination, and finally gets a better matching effect.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

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