Fast Target Recognition Based on Improved ORB Feature

Xie, Ying; Wang, Quan; Chang, Yuanxiong; Zhang, Xueyuan

doi:10.3390/app12020786

Cited by 14 publications

(14 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ORB(Oriented fast and rotated brief, ORB) is a fast and pixel-based feature detection algorithm that finds extensive applications in diverse fields, including target recognition [1], [2], simultaneous localization and mapping [3]- [5], and remote sensing images [6]. Harris [7] Harris C, Stephens M. 1994 Shi-Tmasi [8] Shi J.…”

Section: Introductionmentioning

confidence: 99%

An Improved ORB Feature Extraction Algorithm Based on Enhanced Image and Truncated Adaptive Threshold

Dai

2023

IEEE Access

View full text Add to dashboard Cite

The ORB (Oriented fast and rotated brief, ORB) algorithm is limited by its inability to extract feature points or extract only a small number of feature points when a fixed threshold is used in complex lighting conditions. To address this issue, this study proposes a method that combines image enhancement with truncated adaptive threshold to improve the ORB feature extraction algorithm. Firstly, the original image is converted to grayscale. Secondly, the image is enhanced by applying Gaussian filtering for noise reduction, truncated adaptive gamma brightness adjustment, and unsharp masking operation. Then, the enhanced image is segmented into subregions of a specified size, and an improved truncated OTUS method is employed to calculate the adaptive threshold for each subregion. Finally, ORB feature points are extracted using the adaptive threshold. Experimental results show that the improved ORB algorithm can significantly increase the number of feature points in complex lighting conditions, with good accuracy, real-time performance, and robustness.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved ORB Feature Extraction Algorithm Based on Enhanced Image and Truncated Adaptive Threshold

Dai

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Finally, the matching with the highest similarity is selected as the matching result. The commonly used template matching algorithm is matchTemplate; the other is the feature point matching algorithm [3] . The principle is to extract and describe the key points in the image, and then complete the image matching by matching the key points.…”

Section: Design Of Automatic Test Results Judgment Scheme Based On Im...mentioning

confidence: 99%

Research on automatic test system based on image matching algorithm

Zhao

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

View full text Add to dashboard Cite

The traditional test of the system using graphical interface determines the test results by manual. With the higher accuracy requirements of the test software system in current industrial domain, the traditional manual testing has the problems of high costs, low accuracy and efficiency. Then an automatic test system based on image matching algorithm is proposed to simulate manual automatic test result. Firstly, the architecture of automatic test system is researched, which reduces the test cost. Then, an automatically selected method by template matching algorithm and ORB algorithm for the complexity of the target images, which improves the matching rate and accuracy. Finally, an image similarity calculation algorithm based on perceptual hash algorithm is built, which improves the test accuracy and efficiency twice. The experimental results show that the accuracy achieved to 97%, which meets the accuracy requirements for the industrial field.

show abstract

“…ORB is an order of magnitude faster than SURF, and over two orders faster than SIFT. It has been widely used in many fields in recent years, such as remote sensing image matching, target positioning and tracking (Zhang et al, 2014;Xie et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Bubble Trajectory Tracking Based on ORB Algorithm

et al. 2023

View full text Add to dashboard Cite

The system of gas-liquid two-phase bubbly flows is widely found in many industrial fields, such as nuclear energy, chemical, petroleum, and refrigeration. Bubbly two-phase flows measuring including detection and tracking affects the specific engineering problem solving to a great extent. The particle tracking velocity (PTV) algorithm is generally used for the tracking of the particles in the flow field. However, it does not take the shape change of particles into account in the process of flow. In this paper, a kind of bubble feature matching method based on ORB algorithm is proposed, and the edge detection method of findContours in OpenCV is used to extract the bubble contour in the image. The proposed algorithm implements the trajectory tracking of the bubbles with shape change when moving up in liquid. The feasibility of bubble trajectory tracking is shown by displaying of different bubble tracks in the plan, 3D plots and contour changing plots.

show abstract

Fast Target Recognition Based on Improved ORB Feature

Cited by 14 publications

References 36 publications

An Improved ORB Feature Extraction Algorithm Based on Enhanced Image and Truncated Adaptive Threshold

An Improved ORB Feature Extraction Algorithm Based on Enhanced Image and Truncated Adaptive Threshold

Research on automatic test system based on image matching algorithm

Bubble Trajectory Tracking Based on ORB Algorithm

Contact Info

Product

Resources

About