Department of Electronic and Electrical Engineering, University of Surrey, Guildford GU2 5XH, England.
IEEE Trans Pattern Anal Mach Intell. 1987 May;9(5):690-8. doi: 10.1109/tpami.1987.4767964.
We introduce the Adaptive Hough Transform, AHT, as an efficient way of implementing the Hough Transform, HT, method for the detection of 2-D shapes. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. The method is substantially superior to the standard HT implementation in both storage and computational requirements. In this correspondence we illustrate the ideas of the AHT by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces. We show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.
我们引入自适应霍夫变换（AHT）作为一种高效的二维形状检测方法，用于实现霍夫变换（HT）。AHT 使用小的累加器数组和灵活的迭代“从粗到精”累加和搜索策略的思想来识别霍夫参数空间中的显著峰值。该方法在存储和计算需求方面都明显优于标准 HT 实现。在本通信中，我们通过在二维参数空间中搜索证据簇来解决图像中线性和圆形线段的识别问题，说明了 AHT 的思想。我们表明，该方法对额外噪声的添加具有鲁棒性，并且可以用于分析包含多个形状的复杂图像。
