Abstract
Abstract. In the process of image stitching, the ORB (Oriented FAST and Rotated BRIEF) algorithm lacks the characteristics of scale invariance and high mismatch rate. A principal component invariant feature transform (PCA-ORB, Principal Component Analysis- Oriented) is proposed. FAST and Rotated BRIEF) image stitching method. Firstly, the ORB algorithm is used to optimize the feature points to obtain the feature points with uniform distribution. Secondly, the principal component analysis (PCA) method can reduce the dimension of the traditional ORB feature descriptor and reduce the complexity of the feature point descriptor data. Thirdly, KNN (K-Nearest Neighbor) is used, and the k-nearest neighbor algorithm performs roughly matching on the feature points after dimensionality reduction. Then the random matching consistency algorithm (RANSAC, Random Sample Consensus) is used to remove the mismatched points. Finally, the fading and fading fusion algorithm is used to fuse the images. In 8 sets of simulation experiments, the image stitching speed is improved relative to the PCA-SIFT algorithm. The experimental results show that the proposed algorithm improves the image stitching speed under the premise of ensuring the quality of stitching, and can play a role in fast, real-time and large-scale applications, which are conducive to image fusion.
Highlights
The feature point detection algorithm is based on the FAST (Features from Accelerated Segment Test) algorithm and the BRIEF (Binary Robust Independent Elementary Features) algorithm
3.3 Image Fusion algorithm, we should pay attention to the following two key elements: one is to carry out the fusion operation only for the overlapping area and try to keep the original image information unchanged; the other is to maintain the smooth transition of the fusion boundary, which can effectively avoid the spelling generated during the splicing (Yang et al, 2002)
In order to verify the feasibility of the proposed algorithm, the PCA-ORB algorithm and the PCA-SIFT algorithm were used to perform the splicing experiment on the 8 groups of images to be stitched
Summary
The ORB feature point detection algorithm was proposed by Rublee in ICCV in 2011. The feature point detection algorithm is based on the FAST (Features from Accelerated Segment Test) algorithm and the BRIEF (Binary Robust Independent Elementary Features) algorithm. A new algorithm, the algorithm has proved to be a good feature point detection algorithm in the actual application process
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