Natural Image Mosaicing based on Redundant Keypoint Elimination Method in SIFT algorithm and Adaptive RANSAC method

Image mosaicing refers to stitching two or more images which have overlapping regions to a larger and more comprehensive image. Scale Invariant Feature transform (SIFT) is one of the most commonly used detectors previously used in image mosaicing. The defects of SIFT algorithm are the large number of redundant keypoints and high execution time due to the high dimensions of classical SIFT descriptor, that reduces the efficiency of this algorithm. In this paper, to solve these problems a new four-step approach for image mosaicing is proposed.  At first, the keypoints of both reference and sensed images are extracted based on Redundant Keypoint Elimination-SIFT (RKEM-SIFT) algorithm to improve the mosaicing process. Then, to increase the speed of the algorithm, the 64-D SIFT descriptor for keypoints description is used. Afterwards, the proposed RANdom SAmple Consensus (RANSAC) algorithm is used for removing mismatches. Finally, a new method for image blending is proposed. The details of the proposed steps are as follows. RKEM-SIFT algorithm has been proposed in [1] to eliminate redundant points based on redundancy index. In this paper, RKEM algorithm is used to extract keypoints to improve the accuracy of image mosaicing. In the second stage, for each keypoint of the image, 64-D SIFT descriptor is computed. In this descriptor, unlike the 128-D SIFT descriptor, a smaller window is used which improves the accuracy of matching and reduces the running time. In the third stage, the proposed adaptive RANSAC algorithm is suggested to determine the adaptive threshold in the RANSAC algorithm to remove the mismatches and to improve the image mosaicing. Determining the appropriate threshold value in RANSAC is so important, because if an appropriate value is not chosen for this algorithm, the mismatches are not removed, and eventually there will be a serious impact on the outcome of the image mosaicing process. In this method, the threshold value is based on the median value of distances between matching points and their transformed model. Image blending in the mosaicing process is the final step which blends the pixels intensity in the overlapped region to avoid seams. The proposed method of blending is to combine the images based on the Gaussian weighting function, which the mean of this function is considered as the average of the data in the overlapped region of two images. The proposed blending method reduces artifacts in the image for better performance of the mosaicing process. Another advantage of this proposed method is the possibility to combine more than two images that are suitable for creating panoramic images. The simulation results of the proposed image mosaicing technique, which includes the RKEM-SIFT algorithm as feature detector, 64-D SIFT descriptor, proposed adaptive RANSAC algorithm and proposed image blending algorithm on different image databases show the superiority of the proposed method according to RMSE criteria, precision and running time.