3D computer vision system based on spatial phase mapping

2000 ◽  
Author(s):  
Xiang Peng ◽  
Weiqiang Shi ◽  
Zonghua Zhang ◽  
Xiaotang Hu
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Spatial Phase ◽  
3D Computer Vision ◽  
Phase Mapping

Robot performance measurement and calibration using a 3D computer vision system

Robotica ◽  
1995 ◽  
Vol 13 (4) ◽  
pp. 327-337 ◽  
Author(s):  
B. Preising ◽  
T. C. Hisa
Keyword(s):  
Computer Vision ◽  
Robot Control ◽  
Vision System ◽  
Computer Vision System ◽  
Robot Calibration ◽  
End Effector ◽  
Tolerance Level ◽  
Cartesian Space ◽  
3D Computer Vision ◽  
Robot Performance

SummaryPresent day robot systems are manufactured to perform within industry accepted tolerances. However, to use such systems for tasks requiring high precision, various methods of robot calibration are generally required. These procedures can improve the accuracy of a robot within a small volume of the robot's workspace. The objective of this paper is to demonstrate the use of a single camera 3D computer vision system as a position sensor in order to perform robot calibration. A vision feedback scheme, termed Vision-guided Robot Control (VRC), is described which can improve the accuracy of a robot in an on-line iterative manner. This system demonstrates the advantage that can be achieved by a Cartesian space robot control scheme when end effector position/orientation are actually sensed instead ofcalculated from the kinematic equations. The degree of accuracy is determined by setting a tolerance level for each of the six robot Cartesian space coordinates. In general, a small tolerance level requires a large number of iterations in order to position the end effector, and a large tolerance level requires fewer iterations. The viability of using a vision system for robot calibration is demonstrated by experimentally showing that the accuracy of a robot can be drastically improved. In addition, the vision system can also be used to determine the repeatability and accuracy of a robot in a simple, efficient, and quick manner. Experimental work with an IBM Electric Drive Robot (EDR) and the proposed vision system produced a 97 and a 145 fold improvement in the position and orientation accuracy of the robot, respectively.

3D Computer-vision system for automatically estimating heifer height and body mass

2018 ◽  
Vol 173 ◽  
pp. 4-10 ◽  
Author(s):  
Oron Nir ◽  
Yisrael Parmet ◽  
Daniel Werner ◽  
Gaby Adin ◽  
Ilan Halachmi
Keyword(s):  
Computer Vision ◽  
Body Mass ◽  
Vision System ◽  
Computer Vision System ◽  
3D Computer Vision

scholarly journals Unexpected Regularity in Swimming Behavior of Clausocalanus furcatus Revealed by a Telecentric 3D Computer Vision System

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e67640 ◽  
Author(s):  
Giuseppe Bianco ◽  
Vincenzo Botte ◽  
Laurent Dubroca ◽  
Maurizio Ribera d’Alcalà ◽  
Maria Grazia Mazzocchi
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Swimming Behavior ◽  
Computer Vision System ◽  
3D Computer Vision ◽  
Clausocalanus Furcatus

Colour assessment of milk and milk products using computer vision system and colorimeter

2021 ◽  
pp. 105084
Author(s):  
Bojana Milovanovic ◽  
Ilija Djekic ◽  
Jelena Miocinovic ◽  
Bartosz G. Solowiej ◽  
Jose M. Lorenzo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Milk Products

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

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