scholarly journals Nighttime Pedestrian Ranging Algorithm Based on Monocular Vision

2016 ◽  
Vol 16 (5) ◽  
pp. 156-167
Author(s):  
Hongxia Wang ◽  
Xue-Xue Kang ◽  
Yang Yu
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
High Precision ◽  
Monocular Vision ◽  
Night Vision ◽  
Experimental Results ◽  
Night Time

Abstract Since the traditional computer vision ranging algorithm is imperfect in pertinence and precision, night time monocular vision pedestrian ranging method is proposed for vehicular infrared night vision goggles. Firstly, the method calibrated the internal and external parameters of infrared night-vision goggles, then, it corrected distortion of collected Vehicular Infrared Night Vision Image, and finally it ranged objective pedestrians by using night time monocular vision pedestrian ranging algorithm. The experimental results show that this method has the characteristics of pertinence, high precision and good real-time, and has good practicability.

scholarly journals Pose Estimation of Excavator Manipulator Based on Monocular Vision Marker System

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4478
Author(s):  
Jiangying Zhao ◽  
Yongbiao Hu ◽  
Mingrui Tian
Keyword(s):  
Computer Vision ◽  
Error Analysis ◽  
Real Time ◽  
Construction Industry ◽  
Pose Estimation ◽  
Measurement System ◽  
Monocular Vision ◽  
Construction Sites ◽  
Orientation Error ◽  
Marker System

Excavation is one of the broadest activities in the construction industry, often affected by safety and productivity. To address these problems, it is necessary for construction sites to automatically monitor the poses of excavator manipulators in real time. Based on computer vision (CV) technology, an approach, through a monocular camera and marker, was proposed to estimate the pose parameters (including orientation and position) of the excavator manipulator. To simulate the pose estimation process, a measurement system was established with a common camera and marker. Through comprehensive experiments and error analysis, this approach showed that the maximum detectable depth of the system is greater than 11 m, the orientation error is less than 8.5°, and the position error is less than 22 mm. A prototype of the system that proved the feasibility of the proposed method was tested. Furthermore, this study provides an alternative CV technology for monitoring construction machines.

Study on the Process of 3D Gesture Recognition Technology Based on Computer Vision

2014 ◽  
Vol 643 ◽  
pp. 201-207
Author(s):  
Guang Li Zhuang ◽  
Jia Lin Tang ◽  
Shu Fen Chen ◽  
Xi Ying Li ◽  
Bin Hua Su
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Real Time ◽  
Gesture Recognition ◽  
Experimental Results ◽  
Video Sequences ◽  
History Of Development ◽  
Recognition Result ◽  
History Of

This paper presents a 3D gesture recognition technology based on machine vision as the center. Based on a large number of experiments, this paper sums up and introduces the existing gesture recognition technology, the key research contents of gesture recognition, as well as the history of development of gesture recognition technology. Then, the paper does research in the main technology of gesture recognition .The experimental results show that the method can realize 3D gesture recognition in video sequences with real-time and stability, even more; it can get better recognition result.

Accurate Calibration Method for a Computer Vision System

2014 ◽  
Vol 981 ◽  
pp. 356-359
Author(s):  
Ji Zhou Wei ◽  
Shu Chun Yu ◽  
Hong Bing Wu ◽  
Ya Wei Zhang ◽  
Yong Meng Feng
Keyword(s):  
Computer Vision ◽  
High Precision ◽  
Vision System ◽  
Total Error ◽  
Calibration Method ◽  
Experimental Results ◽  
Calibration Data ◽  
Computer Vision System ◽  
Building Model ◽  
Camera Distortion

A high-precision calibration method was proposed. This method is divided four steps: extracting calibration data, building model, calculating inside and outside parameters, and correcting camera distortion. Experimental results show that calibration is very accurate and the total error is not more than 0.06 pixels.

A Cutting Line Positioning and Defects Detection System for Polyurethane Plate Based on Computer Vision

2012 ◽  
Vol 562-564 ◽  
pp. 119-122 ◽  
Author(s):  
Ji Li Lu ◽  
Ming Xing Lin
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Detection System ◽  
Vision System ◽  
Experimental Results ◽  
Original Image ◽  
Computer Vision System ◽  
Gray Image ◽  
Cutting Line ◽  
Main Defect

In this paper, we present a design of a real-time computer vision system for polyurethane plate cutting line positioning and defects detection. The main defect of polyurethane plate is uneven texture which doesn’t meet the product requirements. We translate the original image to gray image and find the points with strongest gray as the cutting line, extract feathers and detect defects. The experimental results show that it is easy and effective to position cutting points and find defects of polyurethane plate, which can meet the requirements of production and has great practical value.

Method on 3D Dense Point Cloud Recovery of Geographical Scene

2013 ◽  
Vol 748 ◽  
pp. 619-623 ◽  
Author(s):  
Yan Liang ◽  
Ye Hua Sheng ◽  
Ka Zhang
Keyword(s):  
Computer Vision ◽  
Virtual Reality ◽  
High Precision ◽  
Point Cloud ◽  
Low Cost ◽  
Experimental Results ◽  
Affine Invariant ◽  
Invariant Features ◽  
Dense Point

The object of this research is to reconstruct 3D dense point cloud of geographical scene. With the technology and method of computer vision , first affine invariant features are extracted and matched, then cameras parameters and 3D dense point cloud are recovered and united under geographical reference. The experimental results show that this method with low cost and high precision of centimeters can satisfy the requirements of measurement, modeling and virtual reality.

scholarly journals Fast High-Precision Electrical Impedance Tomography System for Real-Time Perfusion Imaging

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61570-61580 ◽  
Author(s):  
Weichen Li ◽  
Junying Xia ◽  
Ge Zhang ◽  
Hang Ma ◽  
Benyuan Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Electrical Impedance Tomography ◽  
High Precision ◽  
Perfusion Imaging ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Tomography System

scholarly journals How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 741
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
High Precision ◽  
Recommender System ◽  
Visual Information ◽  
Experimental Results ◽  
Supplementary Information ◽  
Digital Platform ◽  
Different Types ◽  
Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

scholarly journals High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Sign in / Sign up

Export Citation Format

Share Document