Multipurification of matching pairs based on ORB feature and PCB alignment case study

Specified craft production is a major characteristic of all complex civilizations, and archaeologists have frequently utilized craft specialization as a distinguishing element in identifying and researching the evolution of political complexity. This study aims to better realize the design of modern craft products, using a combination of digital image processing technique and visual sensing system. The impact of digital technology combined with a visual sensing system on product design is investigated and an improved oriented fast and rotated brief (ORB) feature matching algorithm is designed for target image feature extraction. To verify the performance of the algorithm, different experiments are designed for the accomplishment of purification effect, scale invariance, rotation invariance, and computation time. Results show that the corresponding performance accuracies after continuous purification using the angle constraint method are 99.6%, 99.3%, and 99.7%, respectively, which are significantly higher than the accuracy of a single action. In addition, the absolute value of the error of the rotation angle measured by the improved ORB feature matching algorithm is 0.14° on average. It shows that the improved ORB feature matching algorithm has high matching accuracy. The proposed algorithm improves the accuracy of target image feature extraction and has important significance in image recognition.

show abstract

“…is method can organize a lot of calculations into the neighborhood of the test sample set. is greatly improves computational efficiency [22,23].…”

Section: Rough Matching Using K Nearestmentioning

confidence: 99%

Modern Craft Product Design Using Digital Technology Combined with Visual Sensing System in Complex Environment

Zhao

Chen

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Some of the features also contain additional information, such as their orientation and size. Currently, oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB), 11 scale-invariant feature transform (SIFT), 12 and speeded-up robust features 13 are the primary feature extraction techniques. In the feature description step, descriptors are obtained by describing the information of the pixels around the key points; the descriptors are designed based on the principle that features with similar appearance should have similar descriptors.…”

Section: Introductionmentioning

confidence: 99%

Image matching algorithm based on improved AKAZE and Gaussian mixture model

et al. 2023

View full text Add to dashboard Cite

.Simultaneous localization and mapping (SLAM) technology has gained popularity as a result of technological advancements, and image matching—the labor-intensive and crucial component of SLAM technology—has emerged as a key area of research. Hence, the purpose of this study is to develop an image matching method based on improved accelerated-KAZE (AKAZE) and Gaussian mixed model (GMM) segmentation to guarantee the successful completion of subsequent vision challenges. (i) For feature extraction and feature description, first, the AKAZE algorithm was used to extract the key points of the gray-scale image, and second, the binary robust independent elementary features (BRIEF) algorithm was used to obtain the descriptors. Introduction of gray-scale prime method and convolution kernel function to enhance the robustness of features in the BRIEF calculation process. (ii) For feature matching, the gray-scale image is pseudocolored, and then all the pixels in its R channel are segmented by GMM, retaining the features in the same segmented region after brute-force matching. The experimental results demonstrate that, in comparison to oriented FAST and rotated BRIEF and AKAZE, the proposed algorithm in this work provides higher matching accuracy for images of different data sets. That is, the matching approach can be used more effectively for subsequent vision tasks.

show abstract

Multi-Stage Refinement Feature Matching Using Adaptive ORB Features for Robotic Vision Navigation

Sun

et al. 2022

IEEE Sensors J.

View full text Add to dashboard Cite

Multipurification of matching pairs based on ORB feature and PCB alignment case study

Cited by 5 publications

References 38 publications

Modern Craft Product Design Using Digital Technology Combined with Visual Sensing System in Complex Environment

Modern Craft Product Design Using Digital Technology Combined with Visual Sensing System in Complex Environment

Image matching algorithm based on improved AKAZE and Gaussian mixture model

Multi-Stage Refinement Feature Matching Using Adaptive ORB Features for Robotic Vision Navigation

Contact Info

Product

Resources

About