Image Stitching using AKAZE Features

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.