Set Of Geometric Features Research Articles

Corrigendum: Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy

[This corrects the article DOI: 10.3389/fonc.2021.611437.].

Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data

Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some prespecified set of geometric features of each part. We develop a framework for identifying nonparametric variation patterns that uses dissimilarity representation of the data and dissimilarity-based manifold learning, which helps discover a low-dimensional implicit manifold parameterization of the variation. Visualizing how the parts change as the manifold parameters are varied helps build an understanding of the physical characteristic of the variation. We also discuss using the nominal surface of parts when it is accessible to improve the computational expense and visualization aspects of the framework. Our approaches clearly reveal the nature of the variation patterns in a real cylindrical-part machining example and a simulated square head bolt example.

Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy.

PurposeTo develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy.MethodsFrom a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined.ResultsFeature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods – AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%).ConclusionThe presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.

Facial geometric feature extraction based emotional expression classification using machine learning algorithms.

Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.

Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model

Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser-based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human-dominated defect classification and evaluation work. This study presents a 3D model-based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi-class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model-based automatic track surface defect inspection.

Machine learning-based identification of radiofrequency electromagnetic radiation (RF-EMR) effect on brain morphology: a preliminary study.

The brain of a human and other organisms is affected by the electromagnetic field (EMF) radiations, emanating from the cell phones and mobile towers. Prolonged exposure to EMF radiations may cause neurological changes in the brain, which in turn may bring chemical as well as morphological changes in the brain. Conventionally, the identification of EMF radiation effect on the brain is performed using cellular-level analysis. In the present work, an automatic image processing-based approach is used where geometric features extracted from the segmented brain region has been analyzed for identifying the effect of EMF radiation on the morphology of a brain, using drosophila as a specimen. Genetic algorithm-based evolutionary feature selection algorithm has been used to select an optimal set of geometrical features, which, when fed to the machine learning classifiers, result in their optimal performance. The best classification accuracy has been obtained with the neural network with an optimally selected subset of geometrical features. A statistical test has also been performed to prove that the increase in the performance of classifier post-feature selection is statistically significant. This machine learning-based study indicates that there exists discrimination between the microscopic brain images of the EMF-exposed drosophila and non-exposed drosophila. Graphical abstract Proposed Methodology for identification of radiofrequency electromagnetic radiation (RF-EMR) effect on the morphology of brain of Drosophila.

Sustainable manufacturing of prototype automotive gear components within a multi-axis machining platform

This research discusses the development and validation of a sustainable process chain for the efficient manufacturing of prototype gears for automotive applications. To reach the objectives, a selective laser hardening set-up has been integrated into a multi-axis machining platform. This allows to machine and harden gears in a single production center. In this research, 3 automotive drivetrain gears, representing a set of geometrical features (gear flanks,…) and materials, were manufactured and validated. Using the integrated laser hardening set-up, uniform hardness profiles were achieved on the different features while all measured quality parameters were within required tolerances.

Real-time hand posture recognition using hand geometric features and Fisher Vector

Hand posture recognition (HPR), one of the most effective and intuitive human computer interfaces, has been extensively studied and widely adopted in various multimedia applications. Shape descriptors extracted from a hand contour or silhouette have been proved effective in representing a hand posture. However, it is difficult for these shape-based methods to achieve good balance between accuracy and efficiency. To this end, in this paper, we propose a novel hand shape descriptor based on a set of geometric features (SoGF) and Fisher Vector (FV), for effective and efficient HPR. Three types of geometric features, including distances, angles and curvatures, are extracted from a hand silhouette to form a discriminative local descriptor, and FV is adopted to encode the set of local descriptors for compact hand shape representation. To recognize hand postures, we construct a classifier using a multi-class Support Vector Machine (SVM) with FVs as input. The experimental results on four public HPR datasets show that the proposed method can achieve the mean accuracy of the state-of-the-art methods in real time.

Hybrid algorithms of the person identification by face image

In this paper, the problem of constructing hybrid algorithms for identifying a person (IAP) by the image is considered, which is the main one in the development of biometric systems based on them. To solve this problem, we propose a model of algorithms, consisting of the following stages: selection of the area of the face in the image; searching pupils on the face image (FI); determining the location of the mouth and nose; forming a set of geometric features; allocation of subsets of strongly coupled features; the definition of representative features in each subset of strongly related features; interim assessment for the FI class for each set of representative features; estimate for the FI class for all intermediate estimates. A distinctive feature of the proposed hybrid algorithm (HA) is the integration of the results of various methods for determining anthropometric points at the level of calculating the final score for each class of facial images. The main purpose of this paper is to develop an algorithm for integrating various methods of identifying features when IAP on a FI.

An Intelligent Arabic Sign Language Recognition System Using a Pair of LMCs With GMM Based Classification

In this paper, we propose a dual leap motion controllers (LMC)-based Arabic sign language recognition system. More specifically, we introduce the concept of using both front and side LMCs to cater for the challenges of finger occlusions and missing data. For feature extraction, an optimum set of geometric features is selected from both controllers, while for classification, we used both a Bayesian approach with a Gaussian mixture model (GMM) and a simple linear discriminant analysis (LDA) approach. To combine the information from the two LMCs, we introduce an evidence-based fusion approach; namely, the Dempster–Shafer (DS) theory of evidence. Data were collected from two native adult signers, for 100 isolated Arabic dynamic signs. Ten observations were collected for each of the signs. The proposed framework uses an intelligent strategy to handle the case of missing data from one or both controllers. A recognition accuracy of about 92% was achieved. The proposed system outperforms state-of-the-art glove-based systems and single-sensor-based techniques.

TLS POINT CLOUD REGISTRATION FOR DETECTING CHANGE IN INDIVIDUAL ROCKS OF A MOUNTAIN RIVER BED

Abstract. Remote sensing techniques are an important tool in fluvial transport monitoring, since they allow for effective evaluation of the volume of transported material. Nevertheless, there is no methodology for automatic calculation of movement parameters of individual rocks. These parameters can be determined by point cloud registration. Hence, the goal of this study is to develop a robust algorithm for terrestrial laser scanning point cloud registration. The registration is based on Iterative Closest Point algorithm, which requires well established initial parameters of transformation. Thus, we propose to calculate the initial parameters based on key points representing the maximum of Gaussian curvature. For each key point the set of geometrical features is calculated. The key points are then matched between two point clouds as a nearest neighbor in feature domain. Different combinations of neighborhood sizes, feature subsets, metrics and number of nearest neighbors were tested to obtain the highest ratio between properly and improperly matched key points. Finally, RANSAC algorithm was used to calculate the initial transformation parameters between the point clouds and the ICP algorithm was used for calculation of final transformation parameters. The investigations carried out on sample point clouds representing rocks enabled the adjustment of parameters of the algorithm and showed that the Gaussian curvature can be used as a 3-dimentional key point detector for such objects. The proposed algorithm enabled to register point clouds with the mean distance between point clouds equal to 3 mm.

A Simulation Model for Regenerated Objects with Multiparameter Evaluation of Technical Condition Reliability Estimation

Abstract The paper presents the developed simulation model allowing one to estimate the reliability of objects whose technical condition is described by a set of geometrical features. Their values change during operation because of the wearing processes of the part under consideration. The operational availability (or unavailability) of a technical object is determined by the evaluation conducted using a set of criteria referring to its analysed features. In addition, the proposed model enables one to take into account the regeneration of such objects.

Machine-learning-based estimation and rendering of scattering in virtual reality.

In this work, a technique to render the acoustic effect of scattering from finite objects in virtual reality is proposed, which aims to provide a perceptually plausible response for the listener, rather than a physically accurate response. The effect is implemented using parametric filter structures and the parameters for the filters are estimated using artificial neural networks. The networks may be trained with modeled or measured data. The input data consist of a set of geometric features describing a large quantity of source-object-receiver configurations, and the target data consist of the filter parameters computed using measured or modeled data. A proof-of-concept implementation is presented, where the geometric descriptions and computationally modeled responses of three-dimensional plate objects are used for training. In a dynamic test scenario, with a single source and plate, the approach is shown to provide a similar spectrogram when compared with a reference case, although some spectral differences remain present. Nevertheless, it is shown with a perceptual test that the technique produces only a slightly lower degree of plausibility than the state-of-the-art acoustic scattering model that accounts for diffraction, and also that the proposed technique yields a prominently higher degree of plausibility than a model that omits diffraction.

Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection

Background and ObjectivePremature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves). MethodsInitially, a preprocessing stage based on wavelet denoising electrocardiogram signal scaling is applied. Then, the signal is segmented taking into account the ventricular depolarization timing and a new set of geometrical features are extracted. In order to validate this approach, simulations encompassing eight different classifiers are presented. To select the best classifiers, a new methodology is proposed based on the Analytic Hierarchy Process. ResultsThe best results, achieved with an Artificial Immune System, were 98.4%, 91.1% and 98.7% for accuracy, sensitivity and specificity, respectively. When artificial examples were generated to balance the dataset, the recognition performance increased to 99.0%, 98.5% and 99.5%, employing the Support Vector Machine classifier. ConclusionsThe proposed approach is compared with some of latest references and results indicate its effectiveness as a method for recognizing premature ventricular contraction. Besides, the overall system presents low computation load.

A Graph Representation Composed of Geometrical Components for Household Furniture Detection by Autonomous Mobile Robots

This study proposes a framework to detect and recognize household furniture using autonomous mobile robots. The proposed methodology is based on the analysis and integration of geometric features extracted over 3D point clouds. A relational graph is constructed using those features to model and recognize each piece of furniture. A set of sub-graphs corresponding to different partial views allows matching the robot’s perception with partial furniture models. A reduced set of geometric features is employed: horizontal and vertical planes and the legs of the furniture. These features are characterized through their properties, such as: height, planarity and area. A fast and linear method for the detection of some geometric features is proposed, which is based on histograms of 3D points acquired from an RGB-D camera onboard the robot. Similarity measures for geometric features and graphs are proposed, as well. Our proposal has been validated in home-like environments with two different mobile robotic platforms; and partially on some 3D samples of a database.

Clustering methods for the optimization of atomic cluster structure.

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

An algorithm to track laboratory zebrafish shoals

In this paper, a semi-automatic multi-object tracking method to track a group of unmarked zebrafish is proposed. This method can handle partial occlusion cases, maintaining the correct identity of each individual. For every object, we extracted a set of geometric features to be used in the two main stages of the algorithm. The first stage selected the best candidate, based both on the blobs identified in the image and the estimate generated by a Kalman Filter instance. In the second stage, if the same candidate-blob is selected by two or more instances, a blob-partitioning algorithm takes place in order to split this blob and reestablish the instances' identities. If the algorithm cannot determine the identity of a blob, a manual intervention is required.This procedure was compared against a manual labeled ground truth on four video sequences with different numbers of fish and spatial resolution. The performance of the proposed method is then compared against two well-known zebrafish tracking methods found in the literature: one that treats occlusion scenarios and one that only track fish that are not in occlusion. Based on the data set used, the proposed method outperforms the first method in correctly separating fish in occlusion, increasing its efficiency by at least 8.15% of the cases. As for the second, the proposed method's overall performance outperformed the second in some of the tested videos, especially those with lower image quality, because the second method requires high-spatial resolution images, which is not a requirement for the proposed method. Yet, the proposed method was able to separate fish involved in occlusion and correctly assign its identity in up to 87.85% of the cases, without accounting for user intervention.

Classification of Nonferrous Metals Using Magnetic Induction Spectroscopy

Recycling automotive, electronic, and other end-of-life waste liberates large quantities of metals, which can be returned to the supply chain. Sorting the nonferrous metals, however, is not straightforward. Common methods range from laborious hand-sorting to expensive and environmentally deleterious wet processes. The goal is to move toward dry processes, such as induction sensors and vision systems, which can identify and sort nonferrous scrap efficiently and economically. In this paper, we present a new classification method using magnetic induction spectroscopy (MIS) to sort three high-value metals that make up the majority of the nonferrous fraction—copper, aluminum, and brass. Two approaches are investigated: the first uses MIS with a set of geometric features returned by a vision system, where metal fragments are matched to known test pieces from a training set. The second approach uses MIS only . A surprisingly effective classifier can be constructed by combining the MIS frequency components in a manner determined by how eddy currents circulate in the metal fragment. An average precision and recall (purity and recovery rate) of around 92% was shown. This has significant industrial relevance, as the MIS-only classifier is simple, scalable, and straightforward to implement on existing commercial sorting lines.

Parking Trajectory Planning Using Multiresolution State Roadmaps

This paper presents a trajectory planning method for automated parking that makes use of multiresolution roadmaps. The proposed roadmap generation algorithm takes as an input manually designed geometric information of a parking lot consisting of parking space layout, obstacle configuration, and a set of geometric features called guidelines. It constructs a roadmap by dividing the guidelines in multiple resolutions until it achieves enough coverage over the set of safe trajectories satisfying vehicle kinematics, curvature limit, and collision-avoidance. Using this roadmap, a complex parking trajectory composed of both forward and reverse motions can be computed with small online computation cost. The proposed method is evaluated in both numerical simulations and real experiments.

Hemodynamic parameters and vessel tortuosity: an investigation with a mesenterial vascular network

Purpose: The effect of hemodynamic parameters on vessel tortuosity remains un-clear. Here we investigate the correlation of tortuosity with a set of hemodynamicparameters in a mesenterial vascular network.Methods: A mesenterial vascular network of 389 vessels (131 arteries, 132 veins, and 126 capillaries) was imaged. Eleven hemodynamic parameters were measured (pressure, wall shear stress, diameter, blood velocity and flow, viscosity, haematocrit, partial oxygen saturation, oxygen saturation, wall thickness, and local vessel density). Tortuosity was assessed quantitatively with a validated algorithm and correlation computed with subsets of hemodynamic parameters selected by a lasso regressor.Results: Results suggest that tortuosity is related to pressure, wall shear stress, diameter, blood velocity, viscosity, partial but not full oxygen saturation, and wall thickness for the arteries; diameter, blood flow, hematocrit, and density for the veins; and viscosity (but not hematocrit), partial and full oxygen saturation, and density for the capillaries. The combination of hemodynamic parameters correlating best with tortuosity is the set of all parameters except density (r = 0.64, p < 0.01), using as tortuosity definition the set of tortuosity features (geometric measures) correlating best with a single hemodynamic factor for the arteries.Conclusion: This pilot suggests two general conclusions. First, the quantitative definition of tortuosity (i.e., the set of geometric features adopted) should be tuned to the specific data and problem considered. Second, tortuosity is caused by a combination of hemodynamic factors, not a single one.