摘要
Ｈｏｕｇｈ 变换是目前文献中应用最广泛的特征提取方法．然而，Ｈｏｕｇｈ 变换空间开销大的缺陷严重地限制了它的进一步应用．空间开销大的缺陷不仅在标准Ｈｏｕｇｈ 变换中存在，而且在近年来新提出的随机Ｈｏｕｇｈ 变换、概率Ｈｏｕｇｈ 变换以及动态Ｈｏｕｇｈ 变换中同样存在．这一缺陷在３０ 多年来的Ｈｏｕｇｈ 变换研究过程中始终没有得到很好的解决．该文提出的参数空间分解法旨在从根本上克服Ｈｏｕｇｈ 变换空间开销大的缺陷．参数空间分解法的基本原理是用多个二维数组来实现一个高维参数空间，从而大大降低了空间开销．大量实验证明参数空间分解法是一种有效的Ｈｏｕｇｈ 变换实现方法．
The Hough transform has been widely used in technique for geometric primitive extraction. However, one of the main problems of the Hough transform is its high cost of space, which greatly circumscribed its further applications. Not only does the standard Hough transform have such defect, but also the newly proposed Hough techniques such as randomized Hough transform, probabilistic Hough transform and dynamic Hough transform all suffer from the high space cost which has not been wonderfully solved. This paper proposes the parameter space decomposition approach to alleviate the high memory requirement. The basic principle of the parameter space decomposition approach is to use several 2 D arrays to implement a high dimension parameter space, which can drastically reduce the space burden. In fact the parameter decomposition approach can be considered as a trade off between a large space reduction and a slight false extraction rate. Numerous experiments show that the parameter space decomposition approach is an effective way of randomized Hough transform implementation.
出处
《计算机学报》
EI
CSCD
北大核心
1999年第9期911-917,共7页
Chinese Journal of Computers
关键词
HOUGH变换
参数空间分解
计算机视觉
图像处理
Randomized Hough transform, tabu search algorithm, geometric primitive extraction, parameter space decomposition approach.
