王新征,卜雄洙,徐淼淼,于靖.基于快速Isomap的曲面超声图像优化展开[J].电子测量与仪器学报,2017,31(5):780-785
基于快速Isomap的曲面超声图像优化展开
Optimal flattening of surface ultrasonic image based on fast Isomap algorithm
  
DOI:10.13382/j.jemi.2017.05.018
中文关键词:  超声成像  曲面展开  Isomap
英文关键词:ultrasonic imaging  surface flattening  Isomap
基金项目:
作者单位
王新征 南京理工大学 机械工程学院南京210094 
卜雄洙 南京理工大学 机械工程学院南京210094 
徐淼淼 南京理工大学 机械工程学院南京210094 
于靖 南京理工大学 机械工程学院南京210094 
AuthorInstitution
Wang Xinzheng School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 
Bu Xiongzhu School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 
Xu Miaomiao School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 
Yu Jing School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 
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中文摘要:
      曲面工件超声成像检测时,采集点为三维数据,数量较大,观测时须进行旋转、移动操作,重构与显示缓慢,需对采集数据进行二维显示以便观测工件整体状况,实际检测中通常也要求结果为平面图像。基于此,提出了基于快速Isomap的曲面超声图像展开算法,首先对N个采集点进行Landmark点抽取,采用基于Fibonacci堆的Dijkstra算法计算Landmark点间的近似测地距离,利用多维尺度变换构造保持曲面拓扑结构的2D空间,根据剩余采集点到landmark点的距离确定其坐标,对曲面展开后各点的欧氏距离加权优化,使展开前后测量点间距尽可能保持相等。实验表明,该方法计算速度为2~4 s,误差约为0.1,与Isomap算法相比均有提高。
英文摘要:
      In the ultrasonic imaging detection of surface parts, the structure of determined data is three dimensional, thus the rotation, movement and translation operation are commonly needed to observe the defects. However, due to the large amount of determined data, the 3 D reconstruction and display speed are very slow. So, the determined data should be in 2 D form for defect inspection. And the plane image is usually required to describe the results in the actual detection. Based on the above request, a surface flattening method based fast Isomap algorithm is proposed. First, n points are selected as landmark points. The geodesic distances between the points are computed by the Dijkstra method with Fibonacci heap. Then, MDS method is used to map a set of points into a flat domain. The rest of points coordinates are determined by sample interval. Finally, the weighted optimization method is performed on the flattening meshes. The experimental results indicate that the proposed method can flattening the surface with less distortion. Compared with Isomap method, the proposed algorithm has lower error value and less time, which is about 0.1, 2~4 s, respectively.
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