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Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood vessel like IVC, which makes large respirophasic movements and shows a complicated three-dimensional geometry. Using bi-dimensional (2D) B-mode views either in a long or short axis has improved the characterization of IVC dynamics compared to measurements along a single direction (M-mode). However, specific movements of the IVC can also challenge the information provided by these 2D sections. Thus, these two orthogonal views, provided by an US system in the X-plane, are integrated here using an innovative method. It is tested on simulated videos of the IVC by performing complicated movements, which are compensated by the new method, overcoming the biased measurements provided by 2D scans. The method is then applied on example experimental data. [ABSTRACT FROM AUTHOR]

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant's performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants' consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts. [ABSTRACT FROM AUTHOR]

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The present study aimed to examine children's and adults' performance and strategies in volume estimations. Three tasks were designed and presented to 40 adults and 40 6th grade children who were asked to estimate: (a) the number of unit measures (bricks/rice spoons) that may be put in a container, (b) the quantity of bricks/rice, between two different quantities of bricks/rice, that could fit in a container, and (c) the container, between two containers, that may hold a quantity given. Participants were – randomly and in equal numbers from each age group – assigned to four groups differing in their opportunity for trial condition and the presence of the half reference point in the containers. Although the majority of participants showed quite low performance rates in estimating volume, their estimations were more successful when the trial condition was available to them as well as when the trials referred to small containers and discrete quantities. The mental counting strategy was mainly used by both age groups. Adults, however, often used the algorithm and processed precise volume measurement. These results are further discussed in relation to educational implications as well as to the perspective of early supporting children's abilities for estimations at the primary school. [ABSTRACT FROM AUTHOR]

Purpose: Utility of prostate-specific antigen density (PSAd) for risk-stratification to avoid unnecessary biopsy remains unclear due to the lack of standardization of prostate volume estimation. We evaluated the impact of ellipsoidal formula using multiparametric magnetic resonance (MRI) and semi-automated segmentation using tridimensional ultrasound (3D-US) on prostate volume and PSAd estimations as well as the distribution of patients in a risk-adapted table of clinically significant prostate cancer (csPCa). Methods: In a prospectively maintained database of 4841 patients who underwent MRI-targeted and systematic biopsies, 971 met inclusions criteria. Correlation of volume estimation was assessed by Kendall's correlation coefficient and graphically represented by scatter and Bland–Altman plots. Distribution of csPCa was presented using the Schoots risk-adapted table based on PSAd and PI-RADS score. The model was evaluated using discrimination, calibration plots and decision curve analysis (DCA). Results: Median prostate volume estimation using 3D-US was higher compared to MRI (49cc[IQR 37–68] vs 47cc[IQR 35–66], p < 0.001). Significant correlation between imaging modalities was observed (τ = 0.73[CI 0.7–0.75], p < 0.001). Bland–Altman plot emphasizes the differences in prostate volume estimation. Using the Schoots risk-adapted table, a high risk of csPCa was observed in PI-RADS 2 combined with high PSAd, and in all PI-RADS 4–5. The risk of csPCa was proportional to the PSAd for PI-RADS 3 patients. Good accuracy (AUC of 0.69 and 0.68 using 3D-US and MRI, respectively), adequate calibration and a higher net benefit when using 3D-US for probability thresholds above 25% on DCA. Conclusions: Prostate volume estimation with semi-automated segmentation using 3D-US should be preferred to the ellipsoidal formula (MRI) when evaluating PSAd and the risk of csPCa. [ABSTRACT FROM AUTHOR]

Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of indoor storage facilities.The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, wide range of temperature variation, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. Despite their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.

ABSTRACT: Variability in the size of slaughtered chickens remains a longstanding challenge in the standardization of the poultry industry. To address this issue, we present a novel approach that uses volume as a grading metric for chicken carcasses. This innovative method, unexplored in existing studies, employs real-time data capture of moving chicken carcasses on a production line using Kinect v2 depth imaging and 3-D reconstruction technologies. The captured depth images are processed into point clouds followed by 3-D reconstruction. Volume is calculated from the reconstructed models using the surface integration method, and additional 2-D and 3-D features are extracted as input parameters for machine learning models. Multiple regression models were evaluated, with the bagged tree model demonstrating superior performance, achieving an R² value of 0.9988, RMSE of 5.335, and ARE of 2.125%. Furthermore, our method showed remarkable efficiency with an average processing time of less than 1.6 seconds per carcass. These results indicate that our novel approach fills a critical gap in existing automated grading methodologies by offering both accuracy and efficiency. This validates the applicability of depth imaging, 3-D reconstruction, and machine learning for estimating chicken carcass volume with high precision, thereby enabling a more comprehensive, efficient, and reliable chicken carcass grading system.

In this paper, we propose an amount estimation method for food intake based on both color and depth images. Two pairs of color and depth images are captured pre- and post-meals. The pre- and post-meal color images are employed to detect food types and food existence regions using Mask R-CNN. The post-meal color image is spatially transformed to match the food region locations between the pre- and post-meal color images. The same transformation is also performed on the post-meal depth image. The pixel values of the post-meal depth image are compensated to reflect 3D position changes caused by the image transformation. In both the pre- and post-meal depth images, a space volume for each food region is calculated by dividing the space between the food surfaces and the camera into multiple tetrahedra. The food intake amounts are estimated as the difference in space volumes calculated from the pre- and post-meal depth images. From the simulation results, we verify that the proposed method estimates the food intake amount with an error of up to 2.2%. [ABSTRACT FROM AUTHOR]

In this article, authors attempt to estimate the volume of rock pieces using the laser scanning technique supported by measurements from the RGB camera. Laboratory tests were performed, based on which the error in estimating the size of the fragments was determined based on the available perspectives. With a known estimation error, the volume of the ore chunks was estimated based on data from a single perspective. The presented methodology makes it possible to determine a unital numerical measure to compare Hydraulic Breaker Hammers (HBH) and Lekatech Electric Hammer (LEH) by estimating the energy consumption determined through electric current measurements. [ABSTRACT FROM AUTHOR]

In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a Convolutional neural network (CGVP-CNN) model that receives an input of a 3-side image and then extracts the needed features to estimate an approximation for the volume. The classification was comparatively performed via ten different classifiers, namely, K-Nearest Neighbors (KNN), Linear Support Vector Machines (Linear SVM), Radial Basis Function (RBF) SVM, Gaussian Process, Decision Tree, Random Forest, Multi-Layer Perceptron (MLP), Adaptive Boosting (AdaBosst), Naive Bayes, and Quadratic Discriminant Analysis (QDA). After several experiments on testing and training data, results yield a classification accuracy of 100%, 92%, 95%, 96%, 100%, 100%, 100%, 96%, 81%, and 92%, respectively. The test reveals the best timing with KNN, which maintained an accuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiency of the model, so by applying a cross-validation experiment, the model generalization was measured. The used dataset was isolated based on the volume values to ensure the model generalization not only on new images of the same volume but with a volume outside the trained range. Then, the predicted volume values were passed to the classifiers group, where classification reported accuracy was found to be (100%, 100%, 100%, 98%, 88%, 87%, 100%, 87%, 97%, 100%), respectively. Although obtaining a classification with high accuracy is the main motive, this work has a remarkable reduction in the data preprocessing time compared to related works. The CGVP-CNN model managed to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classification accuracy using the estimated volume value. [ABSTRACT FROM AUTHOR]

[Objectives]Because of the limit of camera field, the walnut point cloud suffers from missing points, which detrimentally affects the accuracy of walnut phenotype analysis. To enhance the accuracy and expedite the efficiency of walnut grading and classification, this paper proposed a walnut external parameter estimation approach with three-dimensional reconstruction. [Methods]We introduced a coarse-grained realistic structure with the point cloud generation network(CGRSNet)to form a point cloud completion network with an encoding-decoding mechanism. Firstly, a new sampling algorithm was implemented to extract feature information of the original point cloud in different dimensions and thereby increased the feature extraction effectiveness of walnut phenotype. Afterward, the autoencoder was utilized to reconstruct the point cloud in multiple dimensions, and a complete walnut shape was eventually obtained. In the end, the parameters of the model were applied to estimate the volume of walnut. The walnut geometry model generated by multiple parameters was digitized into the point cloud data to feed CGRSNet and get a pre-trained walnut geometry model through shape completion. A fine-tuning process was then conducted with the real walnut point cloud data. [Results]The chamfer distance of the completion network for walnut data set was 0.196 cm. According to the linear regression analysis, the RMSE and R² score of the volume estimation were 2.47cm and 0.94, respectively, which were 0.88 cm and 0.01 higher than those of PF-Net, respectively. [Conclusions]The walnut external parameter estimation algorithm based on CGRSNet proposed in this paper presents satisfactory practicability and accuracy. [ABSTRACT FROM AUTHOR]

Recently, there has been a recognized need to use waste lime landfills for land reclamation projects. Waste lime earthwork is more challenging than general earthwork operations due to potential health and safety hazards and significant changes in earthwork volume. There has been substantial research undertaken on the role of advanced construction technologies in general earthwork; however, their potential in hazardous waste lime environments for earthwork management is yet to be explored. This research proposes a methodology that integrates drone and BIM construction technology for effective and efficient hazardous waste lime earthwork management for construction projects. Drones are used for surveying, providing as-built information, and BIM to model, process, and evaluate the progress of waste lime earthwork. Volume change analyses are conducted using BIM to calculate dehydration time due to the ripple effect of waste lime for cost-efficient earthwork haulage. The case study provides evidence that the proposed method using advanced construction technologies improves productivity, safety, cost control, and better project management for earthwork related to hazardous waste materials. [ABSTRACT FROM AUTHOR]

Selecting appropriate reservoir volume is a necessary premise to ensure the normal operation of the hydraulic system. In order to provide a reference for the reasonable calculation of the self-pressurized hydraulic reservoir volume of civil aircraft,based on the general design requirements of SAE AS5586 aircraft hydraulic system reservoir,the calculation methods for variable volume produced by actuator,hydraulic accumulator and physical properties of oil are introduced and analyzed in this paper. A set of step by step reservoir volume calculation methods are proposed to get the design value of the reservoir. Taking an aircraft hydraulic reservoir volume calculation as an example,the calculation method of each variable volume in the system is deduced. The results show that the reservoir of the hydraulic system of this type of aircraft can meet the requirements in each flight phase.

One of the most important obstacles for taking advantage of forest resources in the Italian Alps is represented by the high level of private properties fragmentation and by their small size. Thus, there is an urgent need for tools to support single or multi-forest owners to gain reliable and updated information on their forest stands so that the proper silvicultural activities following all the existing regulations can be adopted. The present research was aimed at promoting a shared management of small private forest properties in the mountainous area of Friuli Venezia Giulia (NE Italy) through the implementation of a new WebGIS tool to support forest decisions and management at different spatial scales. This new tool was developed updating and merging together different available information sources (e.g., tree species composition, the presence of protected areas, forest roads, etc.) with ad-hoc elaborated layers (e.g., standing volume, annual increment of volume, forest accessibility, etc.), also elaborating a cost analysis related to the different possible harvesting methods. The tool allows queries at the level of either a single or a group of cadastral parcels to obtain data in a format, which can be used for filling in the planning document requested by the regional authorities.

In Canada's Northwest Territories (NT), industrial activities conducted during the winter, such as ice road construction and exploratory drilling, require the use of water from ice-covered water bodies. Withdrawal in excess of 10% of available under-ice volume can threaten fish habitat or other users. The Land and Water Boards (LWBs) of the Mackenzie Valley require water licences for water withdrawal beyond regulated thresholds. Applicants must provide information including identification and location of proposed water sources, timing and proposed volume of water and winter water withdrawal must be limited to

Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based and smart-container-based fluid intake assessment approaches using inertial sensors. The comparison of these two approaches should be analyzed according to gesture recognition and volume estimation. In addition, the influence of the fill level and sip size information on the performance is explored in this study. The accuracy of gesture recognition with postprocessing is 92.89% and 91.8% for the wrist-worn-based approach and smart-container-based approach, respectively. For volume estimation, sip-size-dependent models can achieve better performance than general SVR models for both wrist-worn-based and smart-container-based approaches. The improvement of MAPE, MAD, and RMSE can reach over 50% except MAPE for small sip sizes. The results demonstrate that the sip size information and recognition performance are important for fluid intake assessment approaches. [ABSTRACT FROM AUTHOR]

In Canada's Northwest Territories (NT), industrial activities conducted during the winter, such as ice road construction and exploratory drilling, require the use of water from ice-covered water bodies. Withdrawal in excess of 10% of available under-ice volume can threaten fish habitat or other users. The Land and Water Boards (LWBs) of the Mackenzie Valley require water licences for water withdrawal beyond regulated thresholds. Applicants must provide information including identification and location of proposed water sources, timing and proposed volume of water and winter water withdrawal must be limited to ,10% of available volume to protect fish habitat under the ice. Many applicants are at early project stages and the necessary information on bathymetry and volumes of water is not readily available or requires expertise and effort that may not be feasible at the early stages of smaller projects. This paper describes the alternative method for determining available winter water volumes from lakes to support small-scale projects. A simple formula of 'allowable volume (m3) - surface area (m2) * 0.1 m' was developed and tested to provide a conservative estimate of under-ice volumes from easily available data which is protective in spite of uncertainties inherent in limited data. [ABSTRACT FROM AUTHOR]

Abstract Fast and accurate measurement of the volume of earthmoving materials is of great significance for the real-time evaluation of loader operation efficiency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fill factor estimation, and it has significant theoretical research and engineering application value.

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant’s performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants’ consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts.

Volume is an important attribute used in many forest management decisions. Data from 83 fixed-area plots located in central New Brunswick, Canada, are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer’s (1967) volume equation. When density was included in the models (Volume=f(Diameter,Height,Density)) choice of diameter measure was more important than choice of height measure. When density was not included (Volume=f(Diameter,Height)), the opposite was true. For models with density included, moment-based estimators of stand diameter and height performed better than all other measures. For models without density, largest tree estimators of stand diameter and height performed better than other measures. The overall best equation used quadratic mean diameter, Lorey’s height, and density (root mean square error ​= ​5.26 ​m3⋅ha−1; 1.9 % relative error). The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha (root mean square error ​= ​32.08 ​m3⋅ha−1; 11.8 % relative error). The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test–retest 3T and 7T sessions were used to extensively assess the test–retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

Fast and accurate measurement of the volume of earthmoving materials is of great significance for the real-time evaluation of loader operation efficiency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader's shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fill factor estimation, and it has significant theoretical research and engineering application value. [ABSTRACT FROM AUTHOR]

The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies. [ABSTRACT FROM AUTHOR]

A healthy diet can help to prevent or manage many important conditions and diseases, particularly obesity, malnutrition, and diabetes. Recent advancements in artificial intelligence and smartphone technologies have enabled applications to conduct automatic nutritional assessment from meal images, providing a convenient, efficient, and accurate method for continuous diet evaluation. We now extend the goFOODTM automatic system to perform food segmentation, recognition, volume, as well as calorie and macro-nutrient estimation from single images that are captured by a smartphone. In order to assess our system's performance, we conducted a feasibility study with 50 participants from Switzerland. We recorded their meals for one day and then dietitians carried out a 24 h recall. We retrospectively analysed the collected images to assess the nutritional content of the meals. By comparing our results with the dietitians' estimations, we demonstrated that the newly introduced system has comparable energy and macronutrient estimation performance with the previous method; however, it only requires a single image instead of two. The system can be applied in a real-life scenarios, and it can be easily used to assess dietary intake. This system could help individuals gain a better understanding of their dietary consumption. Additionally, it could serve as a valuable resource for dietitians, and could contribute to nutritional research. [ABSTRACT FROM AUTHOR]

Earthmoving is a significant activity in most heavy structural designing projects. Earthmoving volume is typically assessed by counting the number of stacked trucks and weighing them on a scale; however, these strategies are error-prone and costly. To address the challenge, this study investigated a deep learning approach for estimating earth volume from photo images of loaded trucks. First, a basic classification model with one convolutional layer has been developed to estimate earth volume by classifying the images into different levels. Next, we applied transfer learning to a pre-trained deep convolutional neural network in order to improve classification performance. For evaluation of the approach, the models have been trained and tested by using images of miniature trucks loaded with different amounts of earth, ranging between 0 and 1000 ml up to six classes at 200 ml intervals. The experimental results showed that the pre-trained network with transfer learning achieved more than 90% accuracy in most cases. The results indicate that the proposed approach has the potential in estimating earth volume in trucks in real-time with minimal intervention by taking images.

In this paper, we propose an amount estimation method for food intake based on both color and depth images. Two pairs of color and depth images are captured pre- and post-meals. The pre- and post-meal color images are employed to detect food types and food existence regions using Mask R-CNN. The post-meal color image is spatially transformed to match the food region locations between the pre- and post-meal color images. The same transformation is also performed on the post-meal depth image. The pixel values of the post-meal depth image are compensated to reflect 3D position changes caused by the image transformation. In both the pre- and post-meal depth images, a space volume for each food region is calculated by dividing the space between the food surfaces and the camera into multiple tetrahedra. The food intake amounts are estimated as the difference in space volumes calculated from the pre- and post-meal depth images. From the simulation results, we verify that the proposed method estimates the food intake amount with an error of up to 2.2%.

The volume estimation of a rigid object from a single view object image is the important need in numerous automated vision based systems. The volume estimation on multiple view images are simple to estimate. But volume estimation on a single view object image is a difficult process and has significant importance in volume estimation. This work presents effective object volume estimation in both regular and irregular single view object images. Initially, the single view input images are pre-processed with Mean-median filtering. Afterwards, edge features are extracted by utilizing the Gaussian edge based laplacian operator and key points are extracted using the Scale invariant feature transform (SIFT) feature. The extracted features are considered for the shape analysis of the objects. Subsequently, VGG-ResNet framework is utilized for depth analysis based on the extracted features. The point clouds generation for the volume estimation is attained through the extracted features. Finally, the volume estimation on single view object is effectively attained through the hybrid 3 dimensional U-Net and graph neural network (Hybrid 3DU-GNet). This framework provides the 3D geometric creation for the accurate volume estimation. This provides the significant improvement on volume estimation. The presented methodology effectively estimates the volume on both regular and irregular single view object images. The presented approach is implemented in the working platform of MATLAB. The experimental results of the presented work is analysed with the different existing approaches and proved the significant improvement in performance metrics. The performance metrics are Accuracy (98.59%), precision (98.21%), recall (97.09%), computational time (3.2 seconds), R-squared (98.2%), (Mean absolute percentage error) MAPE (6.1%), and (Root mean squared error) RMSE (0.93). [ABSTRACT FROM AUTHOR]

Electrical bioimpedance (EBI) is a non-invasive technique for monitoring bladder volume. The EBI approaches global impedance (GI), impedance ratio method (IRM), and voltage change ratio (VCR) have been successfully applied to detect changes in the volume of objects with constant but unknown conductivity. In this work, simulated and in-vitro experiments are used to compare the performance of these methods with respect to a new approach proposed by some authors and called modified VCR (MVCR). Experimental results indicated that GI, VCR, and MVCR indices increase when the volume of the object under study increases. On the other hand, no direct or inverse relationship between IRM and volume was observed. The coefficient of variation, defined as the interquartile range divided by the median of a set of measurements, was lower for MVCR, which indicates that the proposed approach is less sensitive to measurement noise than the other three methods. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. Results: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm3 (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm3 m3 in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e., RMSE: 7975.6 mm3, the smallest among the other variable parameters). Conclusions: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs. [ABSTRACT FROM AUTHOR]

The present study attempts to combine direct current and induced polarization methods for 3D geological modelling and preliminary volume estimation of gold mineralization targets in the Simi locality, Adamawa, Cameroon. To begin, resistivity and chargeability data were collected along seven profiles oriented in N150E using the electrical tomography technique. A geological investigation was also carried out and allowed in addition to the geological formations to highlight the existence of gold mineralization in the study area. The interpretation of the inversion results was done by taking into consideration the geological field data and the existing resistivity charts. The 3D distribution of the interpreted sections allowed us to make a horizontal correlation between the different sections and to elaborate the 3D geological model of the study area by using the deterministic inverse distance method. The estimated model indicates a volume of 621,130.625 m3 of the possible gold complex. The gold complex was then divided into two mineralization domains based on chargeability ranges (mV/V): A first domain is associated with a major mineralization zone (39–60 mV/V) with a volume of 10,842 m3, and a second domain is associated with secondary mineralization zone (18–39 mV/V) with a volume of 610,288 m3. This model allows to compartmentalize the gold complex and can be used as a base model for the gold investigation in the study area. [ABSTRACT FROM AUTHOR]

Among physical attributes, dimensions and weight of agricultural products are important parameters that are useful for designing grading/packaging systems. Size and weight measurements usually require extensive time and labor. Moreover, it is often difficult to accurately do the measurements for the products having irregular shapes, such as sweet potatoes. Even though there are many previous works on the topic, they either lack accuracy or require multiple captures and computing power to reconstruct the three-dimensional representation of the products. This paper proposes the measurement of sweet potato features, including the body length, width, and thickness, by using a simple vision system that requires only a single camera to capture a top-view image of the products. After the background segmentation step, the product is virtually sliced into many equal slices along the longitudinal axis. The volume of the product is calculated as the summation of the volumes of these individual slices. Since there is a high correlation between the products’ weight and volume, the weight of the product can be calculated. The experimental results obtained show that the proposed approach is accomplished with highly competitive results, which have an accuracy of up to 96% (with an R2 of 0.98) for the volume estimation task and an accuracy of up to 95% (with an R2 of 0.96) for the weight estimation task. Due to its simplicity, this model can be used to design and develop the sizing/weighing/packaging systems.

Earthmoving is a significant activity in most heavy structural designing projects. Earthmoving volume is typically assessed by counting the number of stacked trucks and weighing them on a scale; however, these strategies are error-prone and costly. To address the challenge, this study investigated a deep learning approach for estimating earth volume from photo images of loaded trucks. First, a basic classification model with one convolutional layer has been developed to estimate earth volume by classifying the images into different levels. Next, we applied transfer learning to a pre-trained deep convolutional neural network in order to improve classification performance. For evaluation of the approach, the models have been trained and tested by using images of miniature trucks loaded with different amounts of earth, ranging between 0 and 1000 ml up to six classes at 200 ml intervals. The experimental results showed that the pre-trained network with transfer learning achieved more than 90% accuracy in most cases. The results indicate that the proposed approach has the potential in estimating earth volume in trucks in real-time with minimal intervention by taking images. [ABSTRACT FROM AUTHOR]

The segmentation and estimation of thyroid volume in 3D ultrasound images have attracted the research community's attention because of their great importance in clinical diagnosis. Usually, thyroid volume estimation is based on the segmentation of 3D ultrasound images, which is difficult due to various disorders, including non-homogeneous texture distribution within the thyroid region, artifacts, speckles, and the nature of the thyroid shape. This paper presents an approach to segmenting all individual slices and then reconstructing them into a 3D object to overcome these difficulties. The process involves four techniques. The VOI initialization encompasses the probable thyroid gland; it greatly affects the segmentation results. Multi-gene genetic programming determines the appropriate textural features. The block-matching technique estimates the thyroid gland's change in size and location from slice to slice. Finally, the ITKSNAP software reconstructs the 3D volume. The proposed method is compared with state-of-the-art methods to prove its effectiveness in medical image analysis. Sixteen 3D images from an ultrasound thyroid image dataset were used for the experiments. The analysis of the results based on performance evaluation metrics shows that the proposed method is more efficient than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Estimating the volume of standing trees is a fundamental concern in forestry and is typically accomplished using one or more measurements of stem diameter along with formulae that assume geometric primitives. In contrast, technologies such as terrestrial Light Detection And Ranging (LiDAR) can record very detailed spatial information on the actual surface of an object, such as a tree bole. We present a method using LiDAR that provides accurate volume estimates of tree stems, as well as 2D rasters that display details of stem surfaces, which we call the "unwrapping method." This method combines the concepts of cylinder fitting, voxelization, and digital elevation models. The method is illustrated and tested using a sample of standing trees, whereby we are able to generate accurate volume estimates from the point cloud, as well as accurate visualization of the scanned stem sections. When compared to volume estimates derived from Huber's, Smalian's, and Newton's formulae, the differences are consistent with previous studies comparing formula-derived volume estimates and water-displacement-derived volume estimates, suggesting the unwrapping method has comparable accuracy to water displacement. [ABSTRACT FROM AUTHOR]

One essential problem in aircraft conceptual design is initial sizing in which the aircraft primary parameters such as weight, size, and thrust are estimated for given design requirements. The airbreathing hypersonic aircraft is a type of novel aircraft and has significant differences from conventional aircraft in terms of its flight speed and propulsion system. Traditional initial sizing methods are not suitable for this type of novel aircraft. This paper presents an improved initial sizing method for the conceptual design of airbreathing hypersonic aircraft. An illustrative airbreathing hypersonic aircraft is used to describe the detailed procedure of the method. The weight and size of the aircraft are estimated through the simultaneous solution of the weight equation and the volume equation. Constraint analysis is applied to determine the solution space of the thrust-to-weight ratio and the wing loading. A thrust trade is conducted to find the minimum takeoff gross weight of the aircraft. The impacts of technology parameters on the weight, size, and thrust are investigated by sensitivity analyses. The presented method is based on rational derivation. It can be expected that the initial sizing results from the method are reasonable and satisfactory for conceptual design of the airbreathing hypersonic aircraft. [ABSTRACT FROM AUTHOR]

Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based and smart-container-based fluid intake assessment approaches using inertial sensors. The comparison of these two approaches should be analyzed according to gesture recognition and volume estimation. In addition, the influence of the fill level and sip size information on the performance is explored in this study. The accuracy of gesture recognition with postprocessing is 92.89% and 91.8% for the wrist-worn-based approach and smart-container-based approach, respectively. For volume estimation, sip-size-dependent models can achieve better performance than general SVR models for both wrist-worn-based and smart-container-based approaches. The improvement of MAPE, MAD, and RMSE can reach over 50% except MAPE for small sip sizes. The results demonstrate that the sip size information and recognition performance are important for fluid intake assessment approaches.

We present a novel approach for extracting metric volume information of fruits and vegetables from short monocular video sequences and associated inertial data recorded with a hand-held smartphone. Estimated segmentation masks from a pre-trained object detector are fused with the predicted change in relative pose obtained from the inertial data to predict the class and volume of the objects of interest. Our approach works with simple RGB video frames and inertial data which are readily available from modern smartphones. It does not require reference objects of known size in the video frames. Using a balanced validation dataset, we achieve a classification accuracy of 95% and a mean absolute percentage error for the volume prediction of 16% on untrained objects, which is comparable to state-of-the-art results requiring more elaborated data recording setups. A very accurate estimation of the model uncertainty is achieved through ensembling and the use of Gaussian negative log-likelihood loss. The dataset used in our experiments including ground-truth volume information is available at https://sst.aau.at/cns/datasets.

Rockfall processes are now commonly studied through monitoring campaigns using repeat lidar scanning. Accordingly, several recent studies have evaluated how the temporal resolution of data collection and various data-processing decisions can influence the apparent rockfall volumes estimated using typical rockfall database creation workflows. However, there is a lack of studies that consider how data quality and associated data-processing decisions influence rockfall volume estimation. In this work, we perform a series of tests based on an existing reference rockfall database from the Front Range of Colorado, USA, to isolate the influences of data resolution (point spacing), individual point precision, and the filter threshold applied to change results, on the volume estimates obtained for rockfalls. While the effects of individual point precision were found to be limited for typical levels of gaussian noise (standard deviation per coordinate direction ≤ 0.02 m), data resolution and change filter threshold were found to have systematic impacts on volume estimates, with the volume estimates for the smallest rockfalls decreasing substantially with increases in point spacing and change filter threshold. Because these factors disproportionately impact volume estimates for smaller rockfalls, when these factors change, the slope of the apparent power law that describes the relative frequency-volume distribution of rockfalls changes. Evidence is presented that suggests that this phenomenon can explain discrepancies between power law slopes presented in the literature based on studies focused on different scales of rockfall activity. Overall, this study demonstrates the impacts of raw data attributes on rockfall volume estimation and presents an additional effect that tends to bias rockfall frequency–magnitude power law relationships towards underestimation of the relative prevalence of small rockfalls. [ABSTRACT FROM AUTHOR]

Volume estimation of specific objects via close-range remote sensing is a complex task requiring expensive hardware and/or significant computational burden, often discouraging users potentially interested in the technology. This paper presents an innovative system for cost-effective near real-time volume estimation based on a custom platform equipped with depth and tracking cameras. Its performance has been tested in different application-oriented scenarios and compared against measurements and state-of-the-art photogrammetry. The comparison showed that the developed architecture is able to provide estimates fully comparable with the benchmark, resulting in a quick, reliable and cost-effective solution to the problem of volumetric estimates within the functioning range of the exploited sensors. [ABSTRACT FROM AUTHOR]

The volume of a biological structure may be estimated from photographs using its apparent cross-sectional area (ACA), or by measuring its dimensions from orthogonal viewpoints and treating it as an ellipsoid. With ACA half as many photographs are required to achieve results comparable to the ellipsoid method (EM); potentially simplifying data collection by reducing specimen handling time. ACA also facilitates the collection of additional morphological data for analyzing the shape of specimens using geometric morphometrics. To characterize ACA's performance relative to EM, we carried out in-silico validations of ACA versus EM using virtual objects of known volume; first using a diversified set of 21 common objects, and subsequently, a set of 9 echinoderms scanned from wild-type specimens. The volume of each object was estimated twice, using pairs of images with orthogonal viewpoints. The relative and absolute performance (percent error) of both methods were evaluated. ACA performed comparably to EM across a broad range of object shapes and sizes. Both methods were sensitive to objects with low solidity and performed equally well for approximate spheres with high solidity. ACA tended to work as well or better than EM for objects with low circularity. Notably, in most cases, averaged ACA values also produced satisfactory or superior results relative to EM. Our results suggest that ACA represents a viable alternative to EM for estimating the volume of biological structures, especially those which deviate substantially from an ellipsoid form. Awareness of ACA's applications and limitations, coupled with its ease of implementation, may encourage the inclusion of morphological metrics in experimental designs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has altered all aspects of human life since its breakout in March 2020 in the USA and around the world. There has been a tremendous increase in the use of plastic products as most of the PPE (masks, gloves, and other medical equipment) are made from plastic. Therefore, the generation of plastic waste was expected to increase significantly, which was also reported by many news agencies and organizations. This study determines the increase in plastic waste in municipal solid waste (MSW) and investigates its effect on landfill volume consumption during the COVID-19 pandemic. MSW samples were collected from the working face of Irving Hunter Ferrell landfill from May 2020 to December 2020. During every attempted sample collection, eight bags of MSW samples, each weighing 20–25 lbs., were collected. The MSW samples collected from the landfill were characterized and later the volume was estimated to evaluate the potential effects on landfill airspace. Based on the experimental investigations, it was found that plastic waste generation increased significantly during the pandemic (increasing from an 18.5% pre-pandemic level to 30% during the pandemic). Volumetric estimation suggests that the increased amount of plastic waste occupies 20% more volume in landfills. Quantification and estimation of the volume of the increased amount of plastic waste can be useful in predicting the impact of the pandemic on the lifetime of landfills. [ABSTRACT FROM AUTHOR]

We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf’s law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. For continuous data, we recommend using nonlinear least square regression (NLS) on the top-volume queries, where the bound on the volume is obtained from the well-known Clauset, Shalizi and Newman (CSN) estimation of power-law parameters. For binned data, we propose using a Chi-square minimization approach restricted to the top-volume queries, where the bound is obtained by the binned version of the CSN method. Estimations are then derived for the total number of queries and for the total volume of the population, including statistical error bounds. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed. [ABSTRACT FROM AUTHOR]

Quick clay is found across Scandinavia and is especially prominent in south-eastern and central Norway. Quick clay is prone to failure and can cause landslides with high velocities and large run-outs. The 1978 Rissa landslide is one of the best-known quick clay landslides to have occurred in the last century, both due to its size and the fact that it was captured on film. In this article, we utilise Structure from Motion Multi-View Stereo (SfM-MVS) photogrammetry to process historical aerial photography from 1964 to 1978 and derive the first geodetic volume of the Rissa landslide. We found that the landslide covered a total onshore area of 0.36 km2 and had a geodetic volume of 2.53 ± 0.52 × 106 m3 with up to 20 m of surface elevation changes. Our estimate differs profusely from previous estimates by 43–56% which can partly be accounted for our analysis not being able to measure the portion of the landslide that occurred underwater, nor account for the material deposited within the landslide area. Given the accuracy and precision of our analyses, we believe that the total volume of the Rissa landslide may have been less than originally reported. The use of modern image processing techniques such as SfM-MVS for processing historical aerial photography is recommended for understanding landscape changes related to landslides, volcanoes, glaciers, or river erosion over large spatial and temporal scales.

The Cavalieri estimator is used for volume measurement of brain and brain regions. Derived from this estimator is the Area Fraction Fractionator (AFF), used for efficient area and number estimations of small 2D elements, such as axons in cross-sectioned nerves. However, to our knowledge, the AFF has not been combined with serial sectioning analysis to measure the volume of small-size nervous structures. Using the nigrostriatal dopaminergic system as an illustrative case, we describe a protocol based on Cavalieri's principle and AFF to estimate the volume of its somatic, nuclear, dendritic, axonal and axon terminal cellular compartments in the adult mouse. The protocol consists of (1) systematic random sampling of sites within and across sections in regions of interest (substantia nigra, the nigrostriatal tract, caudate-putamen), (2) confocal image acquisition of sites, (3) marking of cellular domains using Cavalieri's 2D point-counting grids, and 4) determination of compartments' total volume using the estimated area of each compartment, and between-sections distance. The volume of the nigrostriatal system per hemisphere is ∼0.38 mm3, with ∼5 % corresponding to perikarya and cell nuclei, ∼10 % to neuropil/dendrites, and ∼85 % to axons and varicosities. In contrast to other methods to measure volume of discrete objects, such as the optical nucleator or 3D reconstructions, it stands out for its versatility and ease of use. The use of a simple quantitative, unbiased approach to assess the global state of a system may allow quantification of compartment-specific changes that may accompany neurodegenerative processes. [Display omitted] • Using unbiased stereology, the mouse nigrostriatal system volume was measured. • The new protocol combines the Cavalieri estimator with area fraction sampling. • The volume of this dopaminergic system is ∼0.37 mm3 per hemisphere. • It is ∼3 % perikarya, ∼2 % nuclei, ∼10 % dendrites, ∼49 % axons and ∼36 % terminals. • This novel protocol can be applied to other neurochemically-specific brain systems. [ABSTRACT FROM AUTHOR]