摘要
大型物体的三维测量中,使用编码标记点既解决了拼接累积误差,又大大降低了非编码点匹配的难度。首先采用一种自适应的二值化方法对图像进行分割,再根据几何特征和灰度特征提取标记点,然后对编码点和非编码点进行归类。编码点解码时,以灰度跳变点为解码起点,综合使用编码弧段对应的圆心角和弧长,提高了解码的准确性。模拟和实验结果表明:该算法受噪声影响小,标记点识别率高,解码正确率可达97%。
Coded points can be used to resolve the accumulative error in registration and the matching complexity of the uncoded points in large objects' 3D measurement is greatly reduced. An self-adaptive binary method is adopted to segment image, and the reference points are extracted based on their geometric and grayscale characteristics. Then the coded points and uncoded points are classified. By choosing the point with gray jumping in coded points as the start point to be decoded, and using both the length and its central angle of the arc, the decoding accuracy of the coded reference points is improved. Both simulation and experiments show that this method is robust to noise,its correct decoding rate is up to 97 %.
出处
《传感器与微系统》
CSCD
北大核心
2010年第8期74-77,81,共5页
Transducer and Microsystem Technologies
基金
四川省科技厅国际合作基金资助项目(2009HH0023)
国家中小企业创新基金资助项目(08C26225101346)
关键词
摄影测量
编码标记点
自适应二值化方法
photogrammetry
coded reference points
self-adaptive binary method