High accuracy position method based on computer vision and error analysis

2003 ◽  
Author(s):  
Shihao Chen ◽  
Zhongke Shi
Keyword(s):  
Computer Vision ◽  
Error Analysis ◽  
High Accuracy

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

scholarly journals Extraction and Research of Crop Feature Points Based on Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2553 ◽  
Author(s):  
Jingwen Cui ◽  
Jianping Zhang ◽  
Guiling Sun ◽  
Bowen Zheng
Keyword(s):  
Computer Vision ◽  
Point Cloud ◽  
High Accuracy ◽  
Depth Camera ◽  
Positioning Error ◽  
Feature Points ◽  
Matching Method ◽  
Depth Images ◽  
Computer Vision Technology ◽  
Rgb Images

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

Modified wavelet method for solving fractional variational problems

2020 ◽  
pp. 107754632093202
Author(s):  
Haniye Dehestani ◽  
Yadollah Ordokhani ◽  
Mohsen Razzaghi
Keyword(s):  
Error Analysis ◽  
Operational Matrix ◽  
Approximate Solutions ◽  
Variational Problems ◽  
High Accuracy ◽  
Construction Method ◽  
Computational Method ◽  
Wavelet Method ◽  
Fractional Variational Problems ◽  
Operational Matrix Of Integration

In this article, a newly modified Bessel wavelet method for solving fractional variational problems is considered. The modified operational matrix of integration based on Bessel wavelet functions is proposed for solving the problems. In the process of computing this matrix, we have tried to provide a high-accuracy operational matrix. We also introduce the pseudo-operational matrix of derivative and the dual operational matrix with the coefficient. Also, we investigate the error analysis of the computational method. In the examples section, the behavior of the approximate solutions with respect to various parameters involved in the construction method is tested to illustrate the efficiency and accuracy of the proposed method.

Systematic error analysis and compensation for high accuracy star centroid estimation of star tracker

2010 ◽  
Vol 53 (11) ◽  
pp. 3145-3152 ◽  
Author(s):  
Hui Jia ◽  
JianKun Yang ◽  
XiuJian Li ◽  
JunCai Yang ◽  
MengFei Yang ◽  
...  
Keyword(s):  
Error Analysis ◽  
Systematic Error ◽  
High Accuracy ◽  
Star Tracker ◽  
Centroid Estimation

scholarly journals DEVELOPMENT OF AN ALL-PURPOSE FREE PHOTOGRAMMETRIC TOOL

2016 ◽  
Vol XLI-B6 ◽  
pp. 31-38 ◽  
Author(s):  
D. González-Aguilera ◽  
L. López-Fernández ◽  
P. Rodriguez-Gonzalvez ◽  
D. Guerrero ◽  
D. Hernandez-Lopez ◽  
...  
Keyword(s):  
Computer Vision ◽  
Point Clouds ◽  
Educational Needs ◽  
High Accuracy ◽  
System Evaluation ◽  
Improve Quality ◽  
3D Point Clouds ◽  
Uav Images ◽  
Different Levels ◽  
Educational Component

Photogrammetry is currently facing some challenges and changes mainly related to automation, ubiquitous processing and variety of applications. Within an ISPRS Scientific Initiative a team of researchers from USAL, UCLM, FBK and UNIBO have developed an open photogrammetric tool, called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS allows to obtain dense and metric 3D point clouds from terrestrial and UAV images. It encloses robust photogrammetric and computer vision algorithms with the following aims: (i) increase automation, allowing to get dense 3D point clouds through a friendly and easy-to-use interface; (ii) increase flexibility, working with any type of images, scenarios and cameras; (iii) improve quality, guaranteeing high accuracy and resolution; (iv) preserve photogrammetric reliability and repeatability. Last but not least, GRAPHOS has also an educational component reinforced with some didactical explanations about algorithms and their performance. The developments were carried out at different levels: GUI realization, image pre-processing, photogrammetric processing with weight parameters, dataset creation and system evaluation. <br><br> The paper will present in detail the developments of GRAPHOS with all its photogrammetric components and the evaluation analyses based on various image datasets. GRAPHOS is distributed for free for research and educational needs.

Optical System Error Analysis of High Accuracy Star Trackers

2013 ◽  
Vol 33 (3) ◽  
pp. 0323003
Author(s):  
孙婷 Sun Ting ◽  
邢飞 Xing Fei ◽  
尤政 You Zheng
Keyword(s):  
Error Analysis ◽  
Optical System ◽  
High Accuracy ◽  
System Error
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