A Fast and Accurate Algorithm for Matching Images Using Hilbert Scanning Distance with Threshold Elimination Function
A Fast and Accurate Algorithm for Matching Images Using Hilbert Scanning Distance with Threshold Elimination Function
复制标题
具有阈值消除功能的希尔伯特扫描距离快速准确的图像匹配算法
DOI：
10.1093/ietisy/e89-d.1.290
复制
发表时间：
2006
期刊：
影响因子：
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通讯作者：
Haijiang Tang
中科院分区：
文献类型：
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作者：
Li Tian;S. Kamata;K. Tsuneyoshi;Haijiang Tang
找到“模型”点集和“图像”点集之间的最佳变换是点模式匹配的主要目的。相似性度量起着关键作用，用于确定两个对象之间的相似程度。尽管一些著名的豪斯多夫距离测量对于这项任务效果很好，但它们的计算量非常大，并且受到噪声点的影响。在本文中，我们提出了一种使用希尔伯特曲线的新颖相似性度量，称为希尔伯特扫描距离（HSD）来解决该问题。该方法在一维（1-D）序列而不是二维（2-D）空间中计算距离度量，这大大降低了计算复杂度。通过应用阈值消除功能，可以消除由噪声和位置误差（例如特征或边缘提取时出现的误差）引起的大距离值。所提出的算法已应用于边缘图与噪声的匹配任务。实验结果表明，HSD可以在较低的计算复杂度下为图像匹配提供足够的信息。我们相信这为点模式识别的研究奠定了新的方向。
To find the best transformation between a "model" point set and an "image" point set is the main purpose of point pattern matching. The similarity measure plays a pivotal role and is used to determine the degree of resemblance between two objects. Although some well-known Hausdorff distance measures work well for this task, they are very computationally expensive and suffer from the noise points. In this paper, we propose a novel similarity measure using the Hilbert curve named Hilbert scanning distance (HSD) to resolve the problems. This method computes the distance measure in the one-dimensional (1-D) sequence instead of in the two-dimensional (2-D) space, which greatly reduces the computational complexity. By applying a threshold elimination function, large distance values caused by noise and position errors (e.g. those that occur with feature or edge extraction) are removed. The proposed algorithm has been applied to the task of matching edge maps with noise. The experimental results show that HSD can provide sufficient information for image matching within low computational complexity. We believe this sets a new direction for the research of point pattern recognition.