Volume 18, Issue 2 (10-2021)
JSDP 2021, 18(2): 147-162 |
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Hossein-Nejad Z, Nasri M. Natural Image Mosaicing based on Redundant Keypoint Elimination Method in SIFT algorithm and Adaptive RANSAC method. JSDP 2021; 18 (2) :147-162
URL: http://jsdp.rcisp.ac.ir/article-1-1008-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1008-en.html
Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University
Abstract: (1997 Views)
Image mosaicing refers to stitching two or more images which have overlapping regions to a larger and more comprehensive image. The image mosaicing process is widely used in scene stabilization, change detection, video compression, and image compression. Image mosaicing methods can be divided into two categories, direct methods and feature-based methods, which feature-based methods are more accurate. Scale Invariant Feature transform (SIFT (is one of the most common feature-based methods in the image mosaicing. However, one of the big defects of SIFT algorithm is the large number of duplicate key points and being time-consuming due to the high dimensions of classical SIFT descriptor. In this paper, to solve these problems, a new four-step approach for image mosaicing is proposed. At first, Redundant Keypoint Elimination-SIFT (RKEM-SIFT) algorithm which has been proposed in [1] is used to identify keypoints of reference and sensed images and to improve the mosaicing process. 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 improved RANdom SAmple Consensus (RANSAC) algorithm is used 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 suggested method of blending is to combine the images based on 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. The proposed method is implemented on standard image databases, created image databases, and has been compared with SURF- fast bidirectional matching, SURF-LM and SIFT-RANSAC methods. The results of the experiments show the superiority of the proposed method according to the criteria of mean square error and accuracy, which compared to the best compared method (SURF-fast bidirectional matching) reduces 6.7% maximum error, 30.09% root mean square error and 37.68% caused the median error.
Type of Study: Research |
Subject:
Paper
Received: 2019/05/2 | Accepted: 2020/06/1 | Published: 2021/10/8 | ePublished: 2021/10/8
Received: 2019/05/2 | Accepted: 2020/06/1 | Published: 2021/10/8 | ePublished: 2021/10/8
References
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