CAJ | 학술논문

为了实现部分重叠且不同视角的测量数据配准,提出多尺度特征点检测算法,可以从大量的原始数据中提取少量特征点.该算法包括离散曲率计算、双边滤波和特征点计算等步骤,特征点个数可以由尺度参数粗略控制.提出局部形状谱描述器来描述每个特征点的局部形状特性,首先利用局域点的距离和曲率信息构造关系矩阵,然后通过计算关系矩阵的特征值来构造谱描述器,利用该描述器可以方便地计算不同点集中各个特征点的对应关系,进而实现两个数据点集的配准.通过实例验证了该算法有较好的抗噪性和运行速度.
위료실현부분중첩차불동시각적측량수거배준,제출다척도특정점검측산법,가이종대량적원시수거중제취소량특정점.해산법포괄리산곡솔계산、쌍변려파화특정점계산등보취,특정점개수가이유척도삼수조략공제.제출국부형상보묘술기래묘술매개특정점적국부형상특성,수선이용국역점적거리화곡솔신식구조관계구진,연후통과계산관계구진적특정치래구조보묘술기,이용해묘술기가이방편지계산불동점집중각개특정점적대응관계,진이실현량개수거점집적배준.통과실례험증료해산법유교호적항조성화운행속도.
In order to align partly overlapped data clouds measured from different view points, a multi-scale feature points detection algorithm was proposed. A few feature points can be extracted from large number of original data quickly. This algorithm consists of three steps: discrete curvature computing, bilateral filtering and feature points detecting. The number of feature points can be controlled by scale parameter approximately. For each feature point, the authors proposed local shape spectral descriptor to identify its local shape characteristic. Firstly, an affinity matrix was constructed using distance and curvature information of points in neighborhood of a feature point, and then a few of eigenvalues of affinity matrix were used to form a shape descriptor, with which the correspondence between different data sets can be computed easily. Some examples prove that the method is robust and efficient for aligning large number of data with noise.