结合几何不变量和模糊聚类进行目标识别

E. Walker
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引用次数: 7

摘要

物体识别是识别图像中物体的类型和位置的过程。早期的研究表明,在局部特征对应中使用模糊兼容性,在二维物体的姿态估计中使用模糊聚类是可取的。本文通过应用几何不变量,特别是四个共线点的交叉比,将该方法扩展到三维物体的图像。识别过程分为三个子任务:局部特征对应、目标识别和姿态确定。描述了每个子任务的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining geometric invariants with fuzzy clustering for object recognition
Object recognition is the process of identifying the types and locations of objects in the image. Earlier work has shown the desirability of using fuzzy compatibility for local feature correspondence and fuzzy clustering for pose estimation of two dimensional objects. The paper extends the methodology to images of three dimensional objects by applying geometric invariants, specifically the cross ratio of four collinear points. The recognition process is divided into three subtasks: local feature correspondence, object identification, and pose determination. Algorithms are described for each subtask.
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