Object recognition using fast adaptive Hough transform

Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing Pub Date : 1989-06-01 DOI:10.1109/PACRIM.1989.48313

D. Haule, A. Malowany

引用次数: 8

Abstract

A fast adaptive Hough transform (FAHT) approach is developed for detecting shapes which can be characterized by two parameters. This class of shapes includes both linear and circular image features. The method is based on identifying linear and circular segments in images by searching for clusters of evidence in two-dimensional parameter spaces. The FAHT differs from HT in the degree of freedom allowed in the placement and choice of shape of the window which defines the range of parameters under study at each resolution. This method is superior to that of HT implementation in both storage and computational requirements. The ideas of the FAHT are illustrated by tackling the problem of identifying linear segments in images by searching for clusters of evidence in two-dimensional parameter spaces. It is shown that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.<>

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基于快速自适应霍夫变换的目标识别

提出了一种快速自适应霍夫变换(FAHT)方法，用于检测可由两个参数表征的形状。这类形状包括线性和圆形图像特征。该方法通过在二维参数空间中搜索证据簇来识别图像中的线性段和圆形段。FAHT与HT的不同之处在于，在窗口的放置和形状选择方面允许的自由度，在每个分辨率下，窗口的形状定义了所研究的参数范围。该方法在存储和计算需求上都优于HT实现。FAHT的思想是通过在二维参数空间中搜索证据簇来解决识别图像中的线性段的问题来说明的。结果表明，该方法对外来噪声具有较强的鲁棒性，可用于分析包含多种形状的复杂图像。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

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