摘要
针对现有的特征轮廓提取算法依赖全局采样,效率不高,难以实现复杂模型的实时轮廓提取的问题,提出一种局部采样追踪算法.该算法利用多判别指标去识别特征,充分挖掘相对二面角、顶点缺角和法向投票张量的优点,提高特征识别的可信度;特征的追踪建立在初始特征边集合的基础上,依据三角面-三角面的邻接关系,在追踪过程中动态、自适应地检测各类特征,拓展特征边,实现局部采样覆盖全局特征的目标;最后从角点出发追踪弱特征轮廓曲线,保证轮廓的完整性.通过CAD和数字医学领域的应用实例对文中算法进行验证,结果表明,该算法是可行、高效的.
Most previous algorithms for feature contours extraction are less efficient due to global sampling, so we proposed an algorithm based on local sampling and tracking to satisfy the real-time requirement of feature lines extraction for complex models. The proposed algorithm identifies features by multiple metrics including dihedral angle, angle defect and normal voting tensor. The credibility of feature detection can be improved by taking advantage of these metrics. The coverage of global features is achieved through local sampling and features tracking basing on initial features. During the tracking process, different types of features are identified dynamically and adaptively. The final step is to track weak feature contours from corners to ensure the integrity of the contours. We validated our algorithm through instances from the field of CAD and digital medicine. The experimental results show that our algorithm is feasible and high efficient.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2016年第10期1644-1653,共10页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(51275094
61300106)
广东省自然科学基金(2015A030310112)
广东省高性能计算重点实验室开放课题(201501)
广东工业大学青年基金(14ZK0022)
关键词
特征线提取
局部采样
二面角
顶点缺角
法向张量投票
extraction of feature lines
local sampling
dihedral angle
angle defect
normal tensor voting
