基于Mahalanobis距离和加权KNN图的图像匹配算法

2015 2nd International Conference on Information Science and Control Engineering Pub Date : 2015-04-24 DOI:10.1109/ICISCE.2015.34

Du Bo, Zhangguan-liang, Cuixiao-long

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引用次数: 10

摘要

提出了一种基于马氏距离的点模式匹配算法，并通过实验对其效果进行了分析和验证。其次，对图变换匹配算法和加权图变换匹配算法进行了深入研究。为了克服Mahalanobis距离和WGTM的局限性，提出了一种基于Mahalanobis距离的加权图变换点模式匹配算法。在中值距离和角距离约束下，将Mahalanobis距离评价的相似度嵌入到WGTM算法中。然后通过迭代剔除离群点得到点对。合成数据和实际数据的实验结果表明，该算法是有效的、鲁棒的。

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An Algorithm of Image Matching Based on Mahalanobis Distance and Weighted KNN Graph

A point pattern matching algorithm based on Mahalanobis distance is proposed, which effect is analyzed and confirmed by experiments. Secondly, the Graph Transformation Matching algorithm and Weighted Graph Transformation Matching algorithm are studied deeply. To overcome the limitation of Mahalanobis distance and WGTM, a novel and robust point pattern matching algorithm based on Weighted Graph Transformation using Mahalanobis distance is proposed. The similarity evaluated by Mahalanobis distance is embedded into WGTM algorithm under the constraint of median distance and angular distance. Then point pairs were obtained through iteratively eliminating the outliers. Experimental results on synthetic data and real-world data demonstrate that the proposed algorithm is effective and robust.

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来源期刊

2015 2nd International Conference on Information Science and Control Engineering

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