2019/02

ACTA AUTOMATICA SINICA

自動化學報

無

10.16383/j.aas.2018.c170521

http://www.aas.net.cn/cn/article/doi/10.16383/j.aas.2018.c170521

航拍圖像拼接 ；KAZE算法；FREAK算法；Grid-KNN算法

Aerial image mosaic；KAZE；FREAK；Grid-KNN

The aerial image is subject to many effects including light, rotation changes, changes in dimensions and so on. The real-time performance of the KAZE algorithm is not desirable and the K-nearest neighbor (KNN) match algorithm takes a long time. Therefore, we propose a mosaic algorithm for UAV aerial image based on the improved KAZE. Firstly, we use an accelerated KAZE algorithm to extract feature points of the image, and use the binary feature descriptor fast retina keypoint (FREAK) to describe the feature points. Then, we adopt the Grid-KNN algorithm for rough match of these points, and use the random sample consensus algorithm for exact match and calculating the geometric transform model. Finally, we use the weighted average algorithm for image fusion. Experimental results show that compared with the KAZE algorithm and the KNN algorithm, the proposed algorithm has better performance on changes of illumination, rotation and scale, as well as processing speed. It is a stable, accurate and stitching algorithm.

為了更好地解決航拍圖像易受光照、旋轉變化、尺度變化等影響，KAZE算法實時性較差以及基於K近鄰的特徵匹配算法耗時較長等問題，該文提出了一種基於改進KAZE的無人機航拍圖像拼接算法.該方法首先利用加速的KAZE算法提取圖像的特徵點，採用二進制特徵描述子FREAK（Fast retina keypoint）進行特徵點描述，然後使用Grid-KNN算法進行特徵點粗匹配，利用隨機一致性算法對匹配的特徵點進一步提純併計算幾何變換模型，最後採用加權平均算法對圖像進行融合.實驗結果表明，該文所提算法使圖像在光照變化、旋轉變化及尺度變化下具有較好的性能，且處理速度較KAZE算法與K近鄰特徵匹配算法有較大提升，是一種穩定、精確度高、拼接效果良好的無人機航拍圖像拼接方法.

中文