Improved ORB Based Image Registration Acceleration Algorithm in Visual-Inertial Navigation System

Zhao, Yanyun; Xiong, Zhi; Duan, Shengqing; Zhou, Shuailin; Cui, Yuchen

doi:10.1109/cac51589.2020.9326928

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The points that remain after this procedure are considered the optimal points. The formula for calculating the sum of differences is detailed in equation (7).…”

Section: )mentioning

confidence: 99%

“…The ORB algorithm, particularly effective in various applications like object positioning, facial recognition, and robot navigation, combines "FAST" (Features from Accelerated Segment Test) for key feature detection and "BRIEF" (Binary Robust Independent Elementary Features) for feature description [7]. It leverages image intensity-based detection for rapid key feature identification [8] and produces compact and affine-resistant binary vector feature descriptions [9].…”

Section: Introductionmentioning

confidence: 99%

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Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Borman,

Harjoko,

2024

IJACSA

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Feature points obtained using traditional ORB methods often exhibit redundancy, uneven distribution, and lack scale invariance. This study enhances the traditional ORB algorithm by presenting an optimal technique for extracting feature points, thereby overcoming these challenges. Initially, the image is partitioned into several areas. The determination of the quantity of feature points to be extracted from each region takes into account both the overall number of feature points and the number of divisions that the image undergoes. This method tackles concerns related to the overlap and redundancy of feature points in the extraction process. To counteract the nonscale invariance issue in feature points obtained via the ORB method, a Gaussian pyramid is employed, and feature points are extracted at each level. Experimental findings demonstrate that our method successfully extracts feature points with greater uniformity and rationality, while preserving image matching accuracy. Specifically, our technique outperforms the traditional ORB algorithm by approximately 4% and the SURF algorithm by 2% in terms of matching performance. Additionally, the processing time of our proposed algorithm is three times faster than that of the SURF algorithm and twelve times faster than the SIFT algorithm.

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“…The points that remain after this procedure are considered the optimal points. The formula for calculating the sum of differences is detailed in equation (7).…”

Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Borman,

Harjoko,

2024

IJACSA

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show abstract

“…Due to the good performance of the ORB characteristics [10][11][12][13][14][15], many scholars at home and abroad have made different improvements to the ORB algorithm [16][17][18][19]. Hong et al [20] matched the ORB feature point matching algorithm and eight parameters and combined the rotation model, improving the detection speed of the feature point; and Bing et al [21] improved the rotation of the ORB algorithm in the feature point matching algorithm of the ORB, which enhanced the matching accuracy; but for special scenes, the study of the ORB algorithm is rare under the conditions of poor illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

Fast Target Recognition Based on Improved ORB Feature

et al. 2022

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A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements.

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“…In the aspect of foreign object detection, the traditional method for the frame difference can merely detect the contour information of the foreground target, the extraction of the target is incomplete, and the optical flow method is slow and the real-time performance is not strong. The ORB [4] (Oriented FAST and Rotated BRIEF)feature matching algorithm can quickly detect feature points in the picture, with a minor amount of calculation and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on foreign object detection method in track area based on Mask-RCNN

Chen²,

Zou³

et al. 2022

J. Phys.: Conf. Ser.

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As the train speeds up, the damage caused by the collisions between trains and foreign objects are becoming more and more severe. Therefore, it is of great significance to monitor the intrusion of foreign objects in the track environment. In this paper, transfer learning is introduced into Mask-RCNN deep learning model. And the data set of rail image is used to train the model, which improves the effect of rail segmentation. The trained model is used to segment the rail in the picture, and the rail vulnerable area is divided based on the segmentation results. The sliding window ORB feature matching algorithm is used to calculate the similarity of vulnerable area. The detection of foreign objects in the area where is easy to invade is realized, and the detection reliability is improved. Experiments represent that this method has high accuracy, strong practicability, good robustness and universality.

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Improved ORB Based Image Registration Acceleration Algorithm in Visual-Inertial Navigation System

Cited by 4 publications

References 8 publications

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Improved ORB Algorithm Through Feature Point Optimization and Gaussian Pyramid

Fast Target Recognition Based on Improved ORB Feature

Research on foreign object detection method in track area based on Mask-RCNN

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