On Geometric Hashing and the Generalized Hough Transform
On Geometric Hashing and the Generalized Hough Transform
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DOI：
10.1109/21.310509
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发表时间：
1994-09
期刊：
影响因子：
--
通讯作者：
Y. C. Hecker;R. Bolle
中科院分区：
文献类型：
--
作者：
Y. C. Hecker;R. Bolle
广义Hough变换和几何散列是目前基于模型的目标识别的两种范例。这两种方案同时查找场景中对象的实例，并确定这些实例的位置和方向。该方法以类似的方式对目标的模型进行编码，并通过对目标模型的图像特征进行投票来实现目标识别。对于这两种方案，对象识别时间在很大程度上独立于在对象模型数据库中编码的对象的数量。本文对这两种方案进行了透视，并考察了它们的异同。作者还研究了目标表示技术，并讨论了如何将目标表示用于目标识别和位置估计。>；
The generalized Hough transform and geometric hashing are two contemporary paradigms for model-based object recognition. Both schemes simultaneously find instances of objects in a scene and determine the location and orientation of these instances. The methods encode the models for the objects in a similar fashion and object recognition is achieved by image features "voting" for object models. For both schemes, the object recognition time is largely independent of the number of objects that are encoded in the object-model database. This paper puts the two schemes in perspective and examines differences and similarities. The authors also study object representation techniques and discuss how object representations are used for object recognition and position estimation. >