Accelerated KAZE (AKAZE) is a multi-scale 2D feature detection and description algorithm in nonlinear scale spaces proposed recently. This paper presents an image stitching algorithm which uses a feature detection and description algorithm; AKAZE and an image blending algorithm; weighted average blending. The whole process is divided into the following steps: First of all, detect feature points in the image and then get feature descriptors of detected points using AKAZE. Next, obtain corresponding matching pairs by using K-NN (K nearest neighbors) algorithm and remove the false matched points by MSAC (M-estimator SAmple Consensus) algorithm. MSAC is a variant of the RANSAC (Random Sample Consensus) algorithm and more accurate than RANSAC. Thereafter, calculate the homography matrix from correct matches. At last, blend the images by using weighted average blending algorithm. Comparison of proposed AKAZE-based algorithm with SIFT-, SURF- and ORB-based algorithms is also presented. According to the experiments, the proposed AKAZE-based image stitching algorithm minimizes stitching seam and generates a perfect stitched image, and also this algorithm is faster than previous algorithms.

中文翻译：

---

使用 AKAZE 功能进行图像拼接

Accelerated KAZE（AKAZE）是最近提出的一种非线性尺度空间中的多尺度二维特征检测和描述算法。本文提出了一种使用特征检测和描述算法的图像拼接算法；AKAZE 和图像混合算法；加权平均混合。整个过程分为以下几个步骤：首先检测图像中的特征点，然后使用AKAZE得到检测点的特征描述子。接下来，使用K-NN（K最近邻）算法获得对应的匹配对，并通过MSAC（M-estimator SAmple Consensus）算法去除虚假匹配点。MSAC 是 RANSAC（随机样本共识）算法的变体，比 RANSAC 更准确。此后，根据正确的匹配计算单应矩阵。最后，使用加权平均混合算法混合图像。还提出了基于 AKAZE 的算法与基于 SIFT、SURF 和 ORB 的算法的比较。根据实验，所提出的基于AKAZE的图像拼接算法最大限度地减少了拼接缝并生成了完美的拼接图像，并且该算法比以前的算法更快。