scholarly journals Liquid Level Detection in Standard Capacity Measures with Machine Vision

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2676
Author(s):  
Gregor Bobovnik ◽  
Tim Mušič ◽  
Jože Kutin
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Liquid Level ◽  
Measuring System ◽  
Legal Metrology ◽  
System A ◽  
Measurement Results ◽  
Capacity Measure ◽  
Measuring Scale ◽  
Liquid Level Detection

Capacity measures are commonly used volume standards for testing measuring systems for liquids other than water. Manual readings from the measuring scale can often be difficult due to the location of the capacity measure or to the nature of the measured liquid. This article focuses on the automation of this procedure by using a single camera machine vision system. A camera positioned perpendicular to the transparent neck captures the image of the liquid meniscus and the measuring scale. The volume reading is determined with the user-defined software in the LabVIEW programming environment, which carries out the image preprocessing, detection of the scale marks and the liquid level, correction of lens distortion and parallax effects and final unit conversions. The realized measuring system for liquid level detection in standard capacity measures is tested and validated by comparing the automated measurement results with those taken by the operators. The results confirm the appropriateness of the presented measuring system for the field of legal metrology.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals Development of the Embedded Vision System: A Survey

2009 ◽  
Vol 42 (7) ◽  
pp. 216-221 ◽  
Author(s):  
Wei Zou ◽  
Junzhi Yu ◽  
De Xu
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Vision Systems ◽  
Embedded Vision ◽  
System A ◽  
Processing Ability ◽  
The Future ◽  
Embedded Vision System ◽  
And Performance ◽  
Embedded Vision Systems

Recently embedded technology has been widely applied to machine vision and embedded vision systems are more and more popular. This paper reviews the advances on embedded vision systems, and then compares and analyzes their frameworks in processing ability, cost and performance. A discussion is provided for some unsolved problems for embedded vision systems. Finally, the future of embedded vision system is outlined.

scholarly journals Liquid Level Detection of Syringe Pump and Syringe Based on Machine Vision

2021 ◽  
Vol 1971 (1) ◽  
pp. 012002
Author(s):  
Yunxi Liu ◽  
Miao Guo ◽  
Jingmin Gao ◽  
Yue Li
Keyword(s):  
Machine Vision ◽  
Liquid Level ◽  
Syringe Pump ◽  
Liquid Level Detection

scholarly journals The Evaluation of Expanded Uncertainty of DC Voltages in the Presence of Electromagnetic Interferences using the LabVIEW Environment

2021 ◽  
Vol 21 (5) ◽  
pp. 136-141
Author(s):  
Przemysław Otomański ◽  
Eligiusz Pawłowski ◽  
Anna Szlachta
Keyword(s):  
Direct Measurement ◽  
Measuring System ◽  
Measuring Instrument ◽  
Uncertainty Of Measurement ◽  
Expanded Uncertainty ◽  
System A ◽  
Data Acquisition Board ◽  
Measurement Results ◽  
Virtual Measuring Instrument ◽  
User Friendly

Abstract The paper presents a possible application of integrated LabVIEW environment to the final evaluation of measurement results in direct measurement. The possibilities of presenting and visualizing the uncertainty of measurement results in a convenient and user-friendly form are also discussed. The topics discussed in the paper were presented using a developed application in LabVIEW. The paper discusses the topic of measurement of direct voltages in the presence of strong electromagnetic interferences. These problems are frequently omitted or hardly emphasized. It presents a suitable measuring system, a virtual measuring instrument created in the LabVIEW environment, and the results of tests carried out for an example NI PCI-6221 data acquisition board. The described approach can be applied also in other measurement situations.

Catching Data from Displayers by Machine Vision

2012 ◽  
Vol 566 ◽  
pp. 124-129 ◽  
Author(s):  
Li Feng Yao ◽  
Jian Fei Ouyang
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Health Assessment ◽  
Measuring System ◽  
Health Measurement ◽  
Scale Invariant ◽  
Individual Values ◽  
Segmented Images ◽  
Sift Features ◽  
Scale Invariant Feature

With the emergence of eHealth, the importance of keeping digital personal health statistics is quickly rising in demand. Many current health assessment devices output values to the user without a method of digitally saving the data. This paper presents a method to directly translate the numeric displays of the devices into digital records using machine vision. A wireless-based machine vision system is designed to image the display and a tracking algorithm based on SIFT (Scale Invariant Feature Transform) is developed to recognize the numerals from the captured images. First, a local camera captures an image of the display and transfers it wirelessly to a remote computer, which generates the gray-scale and binary figures of the images for further processing. Next, the computer applies the watershed segmentation algorithm to divide the image into regions of individual values. Finally, the SIFT features of the segmented images are picked up in sequence and matched with the SIFT features of the ten standard digits from 0 to 9 one by one to recognize the digital numbers of the device’s display. The proposed approach can obtain the data directly from the display quickly and accurately with high environmental tolerance. The numeric recognition converts with over 99.2% accuracy, and processes an image in less than one second. The proposed method has been applied in the E-health Station, a physiological parameters measuring system that integrates a variety of commercial instruments, such as OMRON digital thermometer, oximeter, sphygmomanometer, glucometer, and fat monitor, to give a more complete physiological health measurement.

scholarly journals Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1027
Author(s):  
Md Sultan Mahmud ◽  
Qamar U. Zaman ◽  
Travis J. Esau ◽  
Young K. Chang ◽  
G. W. Price ◽  
...  
Keyword(s):  
Image Processing ◽  
Powdery Mildew ◽  
Machine Vision ◽  
Detection System ◽  
Vision System ◽  
Machine Vision System ◽  
Field Site ◽  
System A ◽  
Processing Program ◽  
Powdery Mildew Disease

Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (>8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

scholarly journals Derivation of sonograph quality parameters by the use of Point Spread Function analysis

2007 ◽  
pp. S69-S76
Author(s):  
L Doležal ◽  
J Mazura ◽  
J Tesařík ◽  
H Kolářová ◽  
D Korpas ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Function Analysis ◽  
Side Lobe ◽  
Quality Parameters ◽  
Measuring System ◽  
System A ◽  
Point Spread ◽  
Measurement Results ◽  
Spread Function ◽  
Computer Controlled

A measuring system evaluating a Point Spread Function generated in an ultrasonographic image by scanning a spherical target was developed. The target is moved in measuring bath filled by water over scanned volume via 3D computer controlled positioning system. A video signal obtained is converted to digital form and analyzed by original software to derive various objective parameters of the imager as follows: Focal areas in both the azimuth and the elevation directions, Ultrasound scanning lines visualisation, Manufacturer preloaded TGC, Width of the scanning plane, Side lobe levels and Amplification uniformity in the azimuth direction. The method was verified by testing 18 different equipments in 282 measurements. Samples of particular measurement results in form of graphical outputs are included. Medical and physiological impacts of this approach are discussed.

Chrysanthemum Cutting Sticking Robot System

1999 ◽  
Vol 11 (3) ◽  
pp. 220-224 ◽  
Author(s):  
Naoshi Kondo ◽  
◽  
Mitsuji Monta
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Water Tank ◽  
Machine Vision System ◽  
Robot System ◽  
System A ◽  
Long Time ◽  
Position And Orientation

Cutting sticking operation is essential on a chrysanthemum production to enhance its productivity. Since it is said that several hundred chrysanthemum seedlings are produced in a year in Japan, it takes a long time and much labor to do the sticking operation and automation of the monotonous operation is desired. A robotic cutting sticking system mainly consisted of four sections; a cutting providing system, a machine vision system, a leaf removing device, and a sticking device. First, a bundle of cuttings was put into a water tank. The cuttings were spread out on the water by vibration of the water tank. The cuttings were picked by a manipulator based on information of cutting positions from a TV camera and sent them one by one to next stage. Secondly, another TV camera detected the position and orientation of the transported cutting and indicated a grasping point in the cutting stem for another manipulator moving. Thirdly, the manipulator moved the cutting to a sticking device through a leaf removing device to cut lower leaves, and then 10 cuttings were stuck into a tray at a time.

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