This paper proposes an adaptive threshold image matching strategy based on the SURF algorithm and the improved brisk algorithm. The process of algorithm can be divided into four parts. First, histogram equalization is...
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ISBN:
(纸本)9781728113128
This paper proposes an adaptive threshold image matching strategy based on the SURF algorithm and the improved brisk algorithm. The process of algorithm can be divided into four parts. First, histogram equalization is applied to enhanced the image. Then the SURF threshold is determined by calculating the complexity of the image. After that, the SURF detection is used to obtain the image feature points. Then the feature points are processed by brisk descriptor. Finally, the Lowe's algorithm is adopted to obtain necessary matching points. The results of experiment verified that, compared with SURF algorithm in speed, the improved algorithm has computational advantages, and the matching accuracy is obviously enhanced compared to the brisk algorithm.
The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the brisk and SIFT algorith...
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The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the brisk and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on brisk corner points. By combining the SIFT scale space and the brisk algorithm, a new scale space construction method is proposed. The brisk algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that brisk and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.
In this paper the idea of an autonomic drone landing system which bases on different markers detection, is presented. The issue of safe autonomic drone landing is one of the major aspects connected with drone missions...
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ISBN:
(数字)9781510613553
ISBN:
(纸本)9781510613553;9781510613546
In this paper the idea of an autonomic drone landing system which bases on different markers detection, is presented. The issue of safe autonomic drone landing is one of the major aspects connected with drone missions. The idea of the proposed system is to detect the landing place, marked with an image called marker, using one of the image recognition algorithms, and heading during the landing procedure to this place. Choosing the proper marker, which allows the greatest quality of the recognition system, is the main problem faced in this paper. Seven markers are tested and compared. The achieved results are described and discussed.
In the process of construction safety monitoring, the construction image is not corrected, resulting in low monitoring accuracy and high false alarm rate. Therefore, a construction safety monitoring system based on UA...
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ISBN:
(纸本)9783031288661;9783031288678
In the process of construction safety monitoring, the construction image is not corrected, resulting in low monitoring accuracy and high false alarm rate. Therefore, a construction safety monitoring system based on UAV image is designed. The hardware framework of the system is designed by the camera mounted UAV module and stm32f103c8t6 as the system control core through wireless network communication technology. Based on the hardware design, the UAVimage is corrected, and the image features are extracted by the brisk algorithm according to the preprocessing results. The kernel function SVM is introduced to classify theUAVimages and realize the safety monitoring of engineering construction. The system test results show that the monitoring accuracy of the designed monitoring system is higher than 90%, and the false alarm rate is low. It can monitor the construction safety behavior in real time, and is more flexible and convenient in actual use.
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