参数空间分解法

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

PARAMETER SPACE DECOMPOSITION APPROACH

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.