Research on optimization of image fast feature point matching algorithm

Manyi Wu

doi:10.1186/s13640-018-0354-y

Abstract

The author studied the feature point extraction and matching based on BRISK and ORB algorithms, experimented with the advantages of both algorithms, and ascertained optimal pyramid layer and inter-layer scale parameters used in features extraction and matching for the same scale image and different scale images with BRISK and ORB algorithm, and analyzed the effectiveness of different parameters combinations on the accuracies of feature extraction and matching and proposed method to determine parameters based on the results. In addition, comparing with the traditional algorithm, using the optimal algorithm with the parameters combining Gaussian denoising, graying, and image sharpening, the ratio of feature points for detection improved 3%; the number of effective matching points increased by nearly 2%. Meanwhile, an algorithm experiment on UAV image mosaic was carried out. The transition of mosaic image color was more natural, and there was no clear mosaic joint with the stitching effect, which indicated that the optimized parameters and the extracted feature point pairs can be used for matrix operations and the algorithm is suitable for UAV image mosaic processing.

Highlights

Image feature point extraction and matching is a very important technical link in image processing
In the process of changing the scale parameter of the pyramid image from 1.1 to 2.0, when the pyramid layer is 4, the image of different scales, the average matching accuracy of the Binary robust invariant scalable keypoints (BRISK) algorithm is less than 10%
In the case of feature point matching on the same scale image, when the number of pyramid layers is unchanged (i = 4) and the scale parameters between pyramid images are gradually increasing, the following conclusions are drawn: first, the algorithm consumes a slight decrease in time consumption; and second, the accuracy of feature detection is gradually increasing and it tends to be stable from 37 to 54%

Summary

Introduction

Image feature point extraction and matching is a very important technical link in image processing. Image stitching, 3D (three dimension) modeling and other technical implementations rely on image feature point extraction and matching. After years of in-depth research and application practice, the algorithm for feature point extraction and description is constantly improving and perfecting. It has a wide range of applications in image matching [1, 2], image retrieval [3, 4], image recognition [5,6,7], video data tracking [8], image stitching [9], image classification [10], and many other aspects.

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Conclusion