Manual Measurement Methods Research Articles

Effects of laser scanner quality and tractor speed to characterise apple tree canopies

Precision orchard management requires non-destructive sensor technologies to identify key sources of variation in apple yield and quality so operations such as fruit thinning, root-pruning as well as harvesting can be better targeted and optimised. We compared the data quality of two light detection and ranging (LiDAR) scanners to assess their ability to estimate the volume of ‘Braeburn’ apple trees. Scanner one was a high-quality tractor-mounted LiDAR (SICK, LDMRS 400001, Düsseldorf, Germany) and the second system consisted of two lower-cost SICK LiDAR sensors fitted on a commercial orchard sprayer. To compare the two scanner systems, two tree row sections with different growth vigour were created by targeted root pruning to reduce annual shoot growth. LiDAR scans and reference assessments were made at the end of the growing season. In addition, LiDAR data were obtained at two tractor speeds: 1.5 and 7.0 km/h (standard spraying speed). Results show key canopy features like the beginning/end of rows or large gaps can be detected with the lower-cost sensors. However, tractor speed had a major effect on data quality. At 1.5 km/h, alpha-shaped tree volumes of the high-quality LiDAR show a high correlation (R2 = 0.84) with the reference assessments, whereas the data quality of the lower-cost sensors was not high enough to represent the tree volume with the alpha-shape algorithm (R2 = 0.09). Moreover, this work raises the question of how precise a sensor-based tree volume needs to be determined to describe heterogeneity in an orchard. Specifically, how to best match sensor data to manual methods of canopy measurement and allow sensor data to be used for multiple applications.

Research on High-Throughput Crop Root Phenotype 3D Reconstruction Using X-ray CT in 5G Era

Currently, the three-dimensional detection of plant root structure is one of the core issues in studies on plant root phenotype. Manual measurement methods are not only cumbersome but also have poor reliability and damage the root. Among many solutions, X-ray computed tomography (X-ray CT) can help us observe the plant root structure in a three-dimensional and non-destructive form under the condition of underground soil in situ. Therefore, this paper proposes a high-throughput method and process for plant three-dimensional root phenotype and reconstruction based on X-ray CT technology. Firstly, this paper proposes a high-throughput transmission for the root phenotyping and utilizing the imaging technique to extract the root characteristics; then, the study adopts a moving cube algorithm to reconstruct the 3D (three-dimensional) root. Finally, this research simulates the proposed algorithm, and the simulation results show that the presented method in this paper works well.

Noncontact High-Precision Aperture Detection Based on Distortion Compensation and Adaptive Mean Blur Algorithm

High-precision apertures are of great significance for the manufacture and assembly of precision instruments. There are currently two common types of methods for aperture measurement: manual measurement methods and computer vision-based methods. The computer vision-based methods work by using industrial cameras and morphological operators and image interpolation algorithms, and can dramatically reduce human errors. However, these methods still suffer from two problems. First, common morphological operations would damage the edge information of the image and fail to capture the accurate edge of the hole. Second, although the magnification algorithm based on image interpolation has an obvious edge positioning effect, it tends to make the edge jagged and blurred. To reduce the influence of image distortion on the error of subsequent image processing, we perform distortion compensation on the image, and use the grid intersection and pixel information in the vicinity for fitting and analysis, and calculate the lens distortion coefficient to correct the image distortion. Then, to obtain more accurate edge information, we improve the Canny operator by using the mean blur with an adaptive threshold that preserves the edge in place of the Gaussian blur and adding a new convolution kernel to smooth the image to provide a more accurate profile for subsequent aperture measurements. With these efforts, we propose a non-contact high-precision aperture measurement method based on a distortion compensation and adaptive mean blur algorithm. The proposed method has a measurement error no greater than ±0.03 mm, and achieves a 100% pass rate. The method solves the problem that the edge contour of the hole cannot be accurately extracted due to edge blurring, and allows for high-precision detection of apertures in industrial fields. The method is expected to be used with a number of precision instruments for aperture size measurement.

JS-WHL-ISH-LANCET-2: WHAT CONSTITUTES AN ACCEPTABLE TEST OF CLINICAL VALIDITY?

For the accurate clinical measurement of blood pressure (BP), hypertension guidelines recommend using an automated BP measuring device that has passed accuracy testing in accordance with an internationally-accepted validation protocol. Such a protocol is designed to rigorously determine the accuracy of an automated BP measuring device compared with a reference manual BP measurement method in a general, or special, population of people. The results of validation studies should be reported in full and made publicly available by such means as publication in the peer-reviewed literature. Since the 1980's, many validation protocols have been developed by Hypertension societies and organisations concerned with the safe and effective use of technology to improve health. An international initiative to harmonize different protocols and develop a single ‘universal’ accepted standard for BP device validation was recently completed by experts from the US Association for the Advancement of Medical Instrumentation, the European Society of Hypertension and the International Organization for Standardization. This standard (ISO 81060–2:2018) is now the recommended international validation protocol designed to replace all other previous standards and protocols. International hypertension organisation recommend convergence towards the global regulatory requirement for mandatory, independent clinical validation of automated BP measuring devices according to the universal standard. This will ensure that the accuracy of automated BP measuring devices is confirmed before being cleared for sale by regulatory authorities, and is an urgent international need.

Soil Deformation after Water Drop Impact-A Review of the Measurement Methods.

Water erosion is an unfavorable phenomenon causing soil degradation. One of the factors causing water erosion is heavy or prolonged rainfall, the first effect of which is the deformation of the soil surface and the formation of microcraters. This paper presents an overview of research methods allowing the study of microcraters as well as the process of their formation. A tabular summary of work on the measurements of various quantities describing the craters is presented. The said quantities are divided into three groups: (i) static quantities, (ii) dynamic quantities, and (iii) dimensionless parameters. The most important measurement methods used to study crater properties, such as (i) basic manual measurement methods, (ii) photography, (iii) high-speed imaging, (iv) profilometers, (v) 3D surface modelling, and (vi) computed tomography (CT) and its possibilities and limitations are discussed. The main challenges and prospects of research on soil surface deformation are also presented.

Sistem Pemantauan Pertumbuhan Anggrek Berdasarkan Pengolahan Citra Digital

Growth monitoring and plant protectionis the major aspect of horticulture because its productivity depends on the health of the plants. Manual direct measurement methods tend to be destructive towards observed plants. In this research, a smart non-contact growth monitoring system was implemented on a chamber with orchid plants asthe objectsobserved. The images of the orchids were taken and became the input of the system to be processed to estimate the height of the plants.The contour of the orchid plant as the object was obtained and the height was calculated based on the highest and the lowest contour.The result shows that the developed system is proven to be capable of measuring orchid’s height in real-time with accuracy more than 95,7%. Thus, this system will effectively help farmers to improve the quality and the quantity of the plant’s productivity.

Intra- and Inter-rater Reliability of a Magnetic Resonance Imaging-Based Volumetric Analysis of the Abductor Hallucis Muscle

Background: The muscle volume considerably changes with aging, pathologies, mechanical loading and exercise, and immobilization. It is recognized as an important parameter that can be measured by various methods to evaluate the effectiveness of interventions focusing on muscle strengthening and function. However, before the application of any measurement method, their reliability needs to be investigated and established. Objectives: This study aimed to evaluate the inter- and intra-rater reliability of the manual measurement method of the abductor hallucis muscle volume in feet with hallux valgus deformities using magnetic resonance imaging (MRI). Patients and Methods: The MRI images of the feet of 15 samples with a hallux valgus deformity were selected in this study. The cross-sectional areas of the abductor hallucis muscle were measured in the cuts along the entire length of the foot and multiplied by slice thickness. Two trained raters performed the measurements. The second rater repeated the measurements after five days to eliminate the memory effect. The intra-rater reliability and inter-rater reliability were assessed based on the intraclass correlation coefficient [ICC (2, 1)] to evaluate the extent of agreement between the raters at a 95% confidence interval. Results: The between- and within-rater ICCs were 0.92 (0.79 - 0.97) and 0.99 (0.97 - 0.99), respectively. The standard error of measurements was also small in both inter-rater (6.2%) and intra-rater (2.1%) reliability analyses. Conclusion: The manually outlined slice-by-slice volume measurement of the abductor hallucis muscle based on MRI images showed excellent inter- and intra-rater reliability. The excellent intra-rater reliability, besides the lower standard error percentage of measurements, indicates the superiority of measurements by a single person. However, further studies with a larger sample size are recommended.

An investigation of woven fabric density measurement using image analysis based on RTV-SFT

To overcome the shortcomings of time-consuming manual measurement methods of fabric density, a novel density measurement method of woven fabrics using image analysis technology via the relative total variation model based on the view of structure extraction from texture (RTV-SFT) is developed. Firstly, a fabric image acquisition system of multi-zoom portable digital microscope is constructed to obtain color dyed fabric images. Secondly, creatively from the perspective of analyzing periodic structure information of image texture information, a new structure extraction from texture algorithm, namely RTV-SFT is proposed to process fabric images to obtain images without non-periodic texture information aiming to achieve accurate yarn segmentation. Finally, the woven fabric density is calculated by effectively adopting the gray level projection method with the improved Gaussian filter algorithm. In the experiment, compared with manual method, the accuracy of the proposed density measurement method is above 93.3%.

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements.

With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications.

Towards Test Bench for Aspiration Catheters in Realistic Evaluation Scenarios

Abstract Aspiration catheters play an important role in mechanical thrombectomy in interventional neuroradiology as they provide support for stent retrievers or are increasingly used separately for contact aspiration. To evaluate the performance of different aspiration catheters in terms of suction force, most current studies rely on manual measurement methods or the suction force is not measured close to the aspiration catheter tip. Moreover, there is currently no standardized method that records measurements digitally and with high temporal resolution. To overcome these challenges, we propose a new test bench that simulates a dedicated aspiration scenario and takes measurements close to the aspiration catheter tip. To meet the requirements, a simplified vascular phantom, a pressure sensor and a simplified clot model were implemented in the prototype. To demonstrate the applicability of the prototype, two commonly used aspiration catheters were evaluated. The obtained measurements show that it is possible to perform standardized comparisons between aspiration catheters from different manufacturers as well as different aspiration pumps and aspiration tubings and their combinations. The developed prototype overcomes the still widely used manual and semimanual and thus error prone measurement methods for the evaluation of vacuum based thrombectomy systems.

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.

Automatic Measurement Method of Slump Flow of Cement Paste Based on Dynamic Image Processing Technology

The slump flow and flow time T200 are the main parameters to evaluate the rheological properties of cement paste. Generally, the manual measurement method is used and it is difficult to get T200 accurately. In this paper, an automatic measurement method of slump flow based on dynamic image processing technology is studied. Firstly, the cement paste flowability test video is divided into frames every 100 ms; secondly, based on the principle of perspective transformation, an automatic point selection method is proposed to correct the frame image to solve the problem of shooting angle; thirdly, the watershed algorithm based on distance transformation is used to segment the boundary of the cement paste area, and the slump flow is calculated according to the polar coordinates of the boundary; lastly, the relationship curve between slump flow and time is automatically output, and the slump flow at any time is obtained. The method is verified by nine groups of cement paste flowability tests. The results show that this method can fastly and accurately obtain the slump flow and flow time T200. Compared with the traditional manual measurement method, the measurement error of slump flow is less than 2%, and the measurement error of flow time T200 is less than 8%. The method enables real-time dynamic measurement of the slump flow.

Smartphone application-based measurements of stem-base width and plant height in rice seedling

The measurements of stem-base width and plant height in rice seedlings are significantly associated with the assessment of in-field seedling growth quality, the determination of optimal seedling transplanting time, and the decision-making of later fertilization use. At present, the measurements of such two morphological traits heavily rely on the inefficient manual measurement method. Herein, we develop a smartphone application to accomplish these tedious and repetitive measurement tasks. Using the developed smartphone application, the reference object and rice seedling were taken into an entire image and then were performed image pre-processing, thereby segmenting the foreground from the background. The reference object image and rice seedling image in the foreground were respectively separated via a comparison in heights of the foreground contours. The reference object image was used for obtaining a scale factor, while the rice seedling image was used for measuring actual stem-base width and plant height by employing the obtained scale factor. The results of automatic measurements resulted from different conditions were compared with those of manual measurements, accomplishing the assessment of measurement accuracy. Collectively, the smartphone application achieved R2 values of 0.9832 and 0.9796 in measuring stem-base width and plant height respectively, and corresponding RMSE values were 0.0307 mm and 1.2311 mm respectively, when tests were performed in various conditions including three camera resolutions, three photographing heights, and three sunlight conditions. The mean runtime for processing an individual image was about 1796.164 ms when a total of 350 rice seedlings were measured under various conditions. This work not only demonstrates that the smartphone application allows low-cost, fast, and accurate measurements of stem-base width and plant height in rice seedling, which can be used as a reasonable alternative to the traditional manual measurement method; but also offers a valuable reference for farmers or researchers in terms of evaluating in-field seedling growth quality, determining optimal seedling transplanting time, as well as guiding follow-up in-field fertilizer expenditures; and further provides methodological support for relevant researches in other crops or domains.

Shank length and circumference measurement algorithm of breeder chickens based on extraction of regional key points

The phenotypic parameters of breeder chickens are the external expression of their genetic genes. Accurate and efficient acquisition of chicken phenotypic parameters is a key technology in poultry breeding. However, the traditional manual measurement method is time-consuming and laborious, and the measurement error is large, which seriously affects the effect and efficiency of poultry breeding. Therefore, in this paper, Xinghua chicken as the research object, combining computer vision technology and deep learning algorithm, proposed the shank length and circumference measurement algorithm (SLCM) of breeder chickens. The algorithm firstly detected and extracted the region of chicken claw and shank bottom base by YOLOV5 algorithm. And then extracted the key points of shank length and circumference measurement based on the pixel distribution features of row direction and column direction in this region, and finally calculated the values of shank length and circumference. The test results of SLCM algorithm show that compared with the traditional manual measurement method, the Pearson correlation coefficient of shank length and circumference measured by algorithm is 0.8722 and 0.8869. Besides, this algorithm not only has smaller standard deviation (standard deviation of shank length is 1.35 mm, and shank circumference is 0.25 mm) and coefficient of variation (the mean coefficient of variation of shank length is 0.02, maximum is 0.06; and the mean coefficient of variation of shank circumference is 0.01, maximum is 0.03), but also has higher measurement efficiency. This research provided reference and basis for the accurate measurement of phenotypic parameters of breeder chickens and had important significance for poultry breeding.

Comparison of Accuracy of Coronary Artery Calcium Score between Artificial Intelligence and Manual MethodComparison of Accuracy of Coronary Artery Calcium Score between Artificial Intelligence and Manual Method

Agaston Score used as indicator of quantative coronary artery calcification is calculated manually by Human being. The accuracy of the values measured using artificial intelligence programs and the manual measurement method, which is an existing conventional method, was compared. The comparison of the average value of the results of 136 people using the initial version showed a statistically significant difference, and the results of 156 people using the upgraded version showed no statistically significant difference. In the case of initial version of the program, the accuracy of the division of the anatomical structure was poor, resulting in a difference in the result values. After that, in the case of the group using the improved version, the problem was improved, resulting in no statistically significant difference. It was possible to confirm accuracy and the possibility of development of deep learning based AI.

THE USE OF CAPTURED IMAGES BY REMOTELY PILOTED AIRCRAFT (RPA) IN MEASURING THE STACKED LOG VOLUME IN A STOCKYARD

Quantifying wood in industrial yards quickly, accurately and at low costs is of great importance for stock management and decision making. In this work, the volume of wood logs stacked in an industrial yard was quantified using aerial images with high spatial resolution obtained by remotely piloted aircraft (RPA). The volumetric measurements were tested using the Structure from Motion with Multi-View Stereo (SfM-MVS) photogrammetry technique and compared with values obtained through the conventional manual measurement method. The aerial images were taken at three different flight heights of 60, 80 and 100 m installed at 10 ground control points using a GNSS RTK receptor. The aerial images were processed by digital photogrammetry software to generate digital surface models (DSM), which made it possible to obtain the volumetric measurements of the stacked wood. Thus, aerial images acquired at 60, 80 and 100 m altitude presented variations of 17.50%, 14.93% and 8.31%, respectively. After the statistical tests applied to the results of volumetric estimates, the SfM-MVS photogrammetry did not show significant differences for the conventional measurement method, indicating that the use of aerial images acquired by RPA can be a viable option to estimate the stacked wood volume in an industrial yard.

The Evaluation of Color Spaces for Large Woody Debris Detection in Rivers Using XGBoost Algorithm

Large woody debris (LWD) strongly influences river systems, especially in forested and mountainous catchments. In Taiwan, LWD are mainly from typhoons and extreme torrential events. To effectively manage the LWD, it is necessary to conduct regular surveys on river systems. Simple, low cost, and accurate tools are therefore necessary. The proposed methodology applies image processing and machine learning (XGBoost classifier) to quantify LWD distribution, location, and volume in river channels. XGBoost algorithm was selected due to its scalability and faster execution speeds. Nishueibei River, located in Taitung County, was used as the area of investigation. Unmanned aerial vehicles (UAVs) were used to capture the terrain and LWD. Structure from Motion (SfM) was used to build high-resolution orthophotos and digital elevation models (DEM), after which machine learning and different color spaces were used to recognize LWD. Finally, the volume of LWD in the river was estimated. The findings show that RGB color space as LWD recognition factor suffers serious collinearity problems, and it is easy to lose some LWD information; thus, it is not suitable for LWD recognition. On the contrary, the combination of different factors in different color spaces enhances the results, and most of the factors are related to the YCbCr color space. The CbCr factor in the YCbCr color space was best for identifying LWD. LWD volume was then estimated from the identified LWD using manual, field, and automatic measurements. The results indicate that the manual measurement method was the best (R2 = 0.88) to identify field LWD volume. Moreover, automatic measurement (R2 = 0.72) can also obtain LWD volume to save time and workforce.

Three-Dimensional Atrial Wall Thickness Measurement Algorithm From a Segmented Atrial Wall Using a Partial Differential Equation

Despite advancements in high-precision segmentation technology for computed tomographic angiography (CTA)-based cardiac wall segmentation, the accurate detection of the endocardial (Endo) and epicardial (Epi) boundaries remains a prerequisite for automated measurements of the cardiac wall thickness (WT). We proposed a novel algorithm for automated three-dimensional (3D) atrial WT (AWT) measurements, including an automatic Endo-Epi boundary detection. We detected the boundaries that were topologically indistinguishable due to an open geometry at the anatomical boundaries using the combined Convex hull and Poisson solver methods. The Laplace equation for the WT measurement was solved by a partial differential equation combining the two detected boundaries of the myocardial wall. We verified the robustness of our algorithm in mask images of the atrial wall that were separated from the CTA images of 20 patients and a phantom model. The accuracy of the automatically detected Endo-Epi boundaries was acceptable as compared to that manually extracted from the phantom model (Dice coefficient = 0.979). The 3D AWTs calculated by the novel automated method from the CTA images obtained from 20 patients with atrial fibrillation had <10% error as compared to the conventional manual AWT measurement method for comparing the regional WT in all locations except the left antral area. The proposed algorithm’s AWT detection time was 27.15±6.99 s per patient, which was 1/40 that of the conventional method. Consequently, our results showed that the proposed automatic 3D AWT measurement algorithm had the potential to significantly improve the efficiency of calculating the AWT while maintaining the existing level of accuracy.

Automatic Measurement of Elongation at Break of Cable Sheath Based on Binocular Vision

<p indent=0mm>In order to solve the problem of accuracy and automation of manual measurement for elongation at break of cable sheath, an automatic measurement based on binocular vision is proposed. Firstly, after the sample tensile video is collected, location method based on information entropy is used on the first frame for the center coordinates of two circle markers, if markers are correctly identified, the center coordinate is used as the marked point, otherwise the point closest to the center of circle in SURF feature points with good stereo matching results is selected as the marked point. Secondly, the marked points tracking based on L-K optical flow is used in the sampled video of sheath stretching process, the determination of the rough frame position of sheath breaking is based on the calculated three-dimensional coordinates and acceleration, then two sampling intervals are backtracked for frame by frame detection. Finally, the breaking frame is located precisely then elongation at break is calculated. The experimental results in the field of quality inspection of cable sheath show that the measured root mean square error of elongation at break is 7.13% which is lower than that of manual measurement method (21.84%), compared with the true values measured by 240 fps high-speed camera, and the processing speed of the method reaches 107.8 fps. Propose method can obviously improve the detection accuracy, speed and automation in practical application.

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.