For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by th...For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.展开更多
随着点云数据在虚拟现实、计算机视觉、机器人等领域中的广泛应用,点云获取与处理中的失真评价正成为一个重要的研究问题。考虑到点云三维信息对几何失真敏感、点云二维投影图包含丰富的纹理和语义信息,提出一种基于三维与二维特征融合...随着点云数据在虚拟现实、计算机视觉、机器人等领域中的广泛应用,点云获取与处理中的失真评价正成为一个重要的研究问题。考虑到点云三维信息对几何失真敏感、点云二维投影图包含丰富的纹理和语义信息,提出一种基于三维与二维特征融合的无参考点云质量评价方法,以有效结合点云的三维与二维特征信息,提高点云质量评价的准确性。对于三维特征提取,先对点云进行最远点采样,以选取的点为中心生成互不重叠的点云子模型,尽可能地覆盖整个点云模型,利用多尺度三维特征提取网络提取体素和点的特征。对于二维特征提取,先对点云进行正交6面投影,再通过多尺度二维特征提取网络提取纹理和语义信息。最后,考虑到人类视觉系统处理不同类型信息时会存在分割处理和交织融合的过程,设计一个对称跨模态注意模块融合三维和二维特征。在5个公开点云质量评价数据库上的实验结果显示,所提方法的皮尔逊线性相关系数(Pearson’s linear correlation coefficient,PLCC)分别达到0.9203、0.9463、0.9125、0.916和0.921,表明与现有的代表性点云质量评价方法相比,所提方法更优。展开更多
文摘For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.
文摘随着点云数据在虚拟现实、计算机视觉、机器人等领域中的广泛应用,点云获取与处理中的失真评价正成为一个重要的研究问题。考虑到点云三维信息对几何失真敏感、点云二维投影图包含丰富的纹理和语义信息,提出一种基于三维与二维特征融合的无参考点云质量评价方法,以有效结合点云的三维与二维特征信息,提高点云质量评价的准确性。对于三维特征提取,先对点云进行最远点采样,以选取的点为中心生成互不重叠的点云子模型,尽可能地覆盖整个点云模型,利用多尺度三维特征提取网络提取体素和点的特征。对于二维特征提取,先对点云进行正交6面投影,再通过多尺度二维特征提取网络提取纹理和语义信息。最后,考虑到人类视觉系统处理不同类型信息时会存在分割处理和交织融合的过程,设计一个对称跨模态注意模块融合三维和二维特征。在5个公开点云质量评价数据库上的实验结果显示,所提方法的皮尔逊线性相关系数(Pearson’s linear correlation coefficient,PLCC)分别达到0.9203、0.9463、0.9125、0.916和0.921,表明与现有的代表性点云质量评价方法相比,所提方法更优。
