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面向全景拼接的图像配准技术研究及应用 被引量:8

Research and application of image registration technology in panoramic stitching
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摘要 针对SIFT算法在生成特征向量和进行特征匹配过程中存在的计算量较大、容易产生误匹配等不足,提出一种优化的SIFT配准算法。优化算法首先引入拉普拉斯算子对图像边缘进行锐化处理,结合图像单元信息投影熵原理提取分块图像特征;再依据投影熵矢量欧氏距离最小揣度进行特征匹配;最后利用改进的随机抽样一致性算法删除误匹配。改进算法应用于全景图像拼接中。实验表明,与原始SIFT配准算法相比,优化算法能够有效提高算法效率,减少错误匹配,取得了较好的匹配效果。 Aiming at solving the insufficiencies of the SIFT registration algorithm such as large calcu- lation, error matching when generating feature vectors and doing feature matching, we propose an optimized SIFT registration algorithm. The optimized registration algorithm firstly sharpens the edge of images by bringing in the Laplacian operator and extracts the characteristics of block images via the projection entropy of image unit information. Then feature points are matched according to the minimum Euclidean distance of entropy vector projection. Finally, the improved random sample consensus algorithm is adopted to eliminate error matching. We apply the optimized algorithm in panoramic mosaic and experimental results show that, the optimized registration algorithm exceeds the SIFT registration algorithm, which not only effectively improves the efficiency, but also reduces error matching, achieving good matching effect.
出处 《计算机工程与科学》 CSCD 北大核心 2016年第2期317-324,共8页 Computer Engineering & Science
基金 江西省教育厅科技项目(GJJ14430) 江西省教育厅重点项目(赣教技字[12770]号)
关键词 全景拼接 图像配准 SIFT算法 单元信息投影熵 panoramic mosaic image registration SIFT algorithm projection entropy of unit information
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