scholarly journals A Fast Detection Method of Network Crime Based on User Portrait

2021 ◽  
Vol 3 (1) ◽  
pp. 17-28
Author(s):  
Yabin Xu ◽  
Meishu Zhang ◽  
Xiaowei Xu
Keyword(s):  
Detection Method ◽  
Fast Detection

Fast Non-Destructive Detection of Wool Content in Blended Fabrics Based on Near-Infrared Spectroscopy Technology

2015 ◽  
Vol 671 ◽  
pp. 356-362 ◽  
Author(s):  
Zhi Feng Chen ◽  
Yuan Quan Hong ◽  
Chang Jiang Wan ◽  
Lian Ying Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Detection Method ◽  
Quantitative Model ◽  
National Standard ◽  
Fast Detection ◽  
Significant Difference ◽  
Validation Set ◽  
Non Destructive

A fast non-destructive method of detection of wool content in blended fabrics was studied based on Near Infrared spectroscopy technology in order to avoid the time-consuming, tedious work and the destruction of samples in the traditional inspection. 621 wool/nylon, wool/polyester and wool/nylon/polyester blended fabrics were taken as research objects. To get the wool content, we established the wool near-infrared quantitative model by partial least squares (PLS) method after analyzing the color and composition of the samples. For verifying the validity and practicability of the model, 100 samples were chosen as an independent validation set. The variance analysis shows that there is no significant difference between Near Infrared fast detection method and national standard method (GB/T2910-2009),which indicates that this method is expected to be a means of fast non-destructive detection and will have extensive application future in the field of wool content detection.

scholarly journals Identification of mycolic acid forms using surface-enhanced Raman scattering as a fast detection method for tuberculosis

2018 ◽  
Vol Volume 13 ◽  
pp. 6029-6038 ◽  
Author(s):  
Jayakumar Perumal ◽  
US Dinish ◽  
Anne Bendt ◽  
Agne Kazakeviciute ◽  
Chit Yaw Fu ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Detection Method ◽  
Surface Enhanced Raman Scattering ◽  
Mycolic Acid ◽  
Fast Detection ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Enhanced Raman Scattering

scholarly journals Fast detection method of transmission line defects and faults based on airborne laser LiDAR

2021 ◽  
Vol 2005 (1) ◽  
pp. 012240
Author(s):  
Shi Lei ◽  
Yang Heng ◽  
Xu Lianggang ◽  
Yang Yuan ◽  
Wang Di ◽  
...  
Keyword(s):  
Transmission Line ◽  
Detection Method ◽  
Fast Detection ◽  
Airborne Laser ◽  
Line Defects

scholarly journals A fast X-corner detection method based on block-search strategy

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983414
Author(s):  
Cai Meng ◽  
Qi Wang ◽  
Lingjie Wu ◽  
Shaoya Guan ◽  
Yao Wu ◽  
...  
Keyword(s):  
Search Strategy ◽  
Detection Method ◽  
Corner Detection ◽  
Image Size ◽  
Image Block ◽  
Fast Detection ◽  
Sampling Window ◽  
Sampling Windows ◽  
Searching Method ◽  
Detection Speed

In order to detect X-corner (or X-point) features more accurately and apace, this article presents a novel and fast detection method based on block-by-block search strategy. Unlike general pixel-by-pixel searching method, the sampling window is first moved along the image block-by-block to find the X-corner candidates rapidly keeping in view the four-step and min-step-distance constraints. During the motion, some overlap is kept between the adjacent sampling windows in order to ensure that all X-corners could have a chance to reside inside, avoiding the possibility of that some X-corners may locate on the edge. Moreover, labeling technology is adopted to prevent duplicate candidates. After the collection of X-corner candidates, the neighborhood variance and centrosymmetry constraints are used to exclude outliers, and the intersection lines is calculated as the sub-pixel position of true X-corner. The experimental results using synthetic and real images show that the presented method approximately takes just about 13 ms to detect 52 X-corners in an image size of 1024 × 768 on a computer having Intel Core i3 CPU at 3.6 GHz and 4GB RAM. The proposed method has faster detection speed compared with the latest methods such as ChESS, SC, and Micron Tracker system while possessing the same or higher detection precision.

Fast detection of moving objects based on sequential images processing

2020 ◽  
Vol 39 (4) ◽  
pp. 5037-5044
Author(s):  
Peng-Cheng Wei ◽  
Fangcheng He ◽  
Jing Li
Keyword(s):  
Moving Objects ◽  
Feature Matching ◽  
Detection Method ◽  
Detection Methods ◽  
Dynamic Scenes ◽  
Practical Applications ◽  
Fast Detection ◽  
Wide Range ◽  
Images Processing ◽  
Sequential Images

Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the SIFT algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance.

A Fast Detection Method For Polynomial Fitting Lane With Self-attention Module Added

2021 ◽  
Author(s):  
Xi Li ◽  
Zhen Huang ◽  
Xiongfeng Sun ◽  
Tianliang Liu
Keyword(s):  
Detection Method ◽  
Polynomial Fitting ◽  
Fast Detection
Sign in / Sign up

Export Citation Format

Share Document