Geometric Feature Extraction Research Articles

Geometric feature extraction in manufacturing based on a knowledge graph

In times of global crises, the resilience of production chains is becoming increasingly important. If a supply chain is interrupted, a cost-effective solution must be established quickly. In the context of Industry 4.0, the concept of smart manufacturing offers a solution for fast and automated decision-making in production planning. The core idea of smart manufacturing is the digitalization of the product life cycle and the linking of individual phases of this cycle. Computer Aided Process Planning (CAPP) plays an important role as the connecting element between design and manufacturing. An important prerequisite for CAPP is the automated analysis of 3D models of components. The aim of this work is the development of an automatic feature recognition (AFR) -method to recognize geometric manufacturing features and their properties from 3D-models and then store them in a knowledge base. In that way, the result of the design can be automatically analysed and compared with manufacturing information afterwards in order to achieve an automated process planning. Geometric and topological information of a 3D model (STEP-AP242 format) generated by CAD systems is extracted by a Python-script developed and stored in an ontology-based knowledge base. The extracted product data is analysed using a Python-script to identify manufacturing features. To provide a comprehensive extensibility of the model, geometric features are defined according to a layered and hierarchical structure.

A novel approach for prognosis of lithium-ion battery based on geometrical features and data-driven model

Lithium-ion (Li-ion) batteries are widely used in such devices as today’s electrical vehicles, consumer electronics, and unmanned aerial vehicles, and will play a key role in the future. Unexpected Li-ion battery abnormities may result in serious inconvenience and enormous replacement costs. Thus, the diagnostic and prognostic methods play important roles in battery replacement scheduling, maintenance strategy development, and battery failure precaution, However, many published methods are unsuitable for both battery capacity and end-of-discharge. In this paper, a hybrid ensemble approach, integrating k-fold cross validation (k-CV) and genetic algorithm with back-propagation neural network (GA-BPNN), is proposed for capacity and end-of-discharge of Li-ion battery prognostics combined with geometrical features. Geometrical features extracted from charge-discharge cycles of Li-ion batteries are set as the inputs of the neural network. K-fold cross validation is introduced to determine the number of BPNN hidden layer neurons, genetic algorithm is used to initialize and optimize the connection weights and thresholds of BPNN. By the critical geometrical feature extraction and the ensemble BPNN model with k-fold cross validation and genetic algorithm, accurate battery capacity and end-of discharge are accomplished, making the proposed model can potentially be used for real-time estimate for the conditions given in this article. The performance of the proposed approach is demonstrated by using actual Li-ion battery data, which is supplied by the NASA Ames Research Center database.

Neural 3D face rendering conditioned on 2D appearance via GAN disentanglement method

Previewing the shaded output of 3D models has been a long-standing requirement in the field. Typically, this is achieved by applying common materials; however, this approach is often labor-intensive and can yield only rough results in the trial stage. Conventional 2D style transfer methods are unsuitable for 3D-to-2D cross-domain conversion, and they cannot accurately reflect the mesh’s geometry. Inspired by StyleGAN2’s related research, we propose a method for rendering 2D images of 3D face meshes directly controlled by a single 2D reference image, using GAN disentanglement. Our approach involves an input of a 3D mesh and a reference image, where encoders extract geometric features from the mesh and appearance features from the reference image. These features control the StyleGAN2 generator to obtain a generated image that preserves the 3D mesh’s geometry and the reference image’s appearance. Our experiments demonstrate that this method performs well in generating images while maintaining geometric consistency.

Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection

Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic aperture radar (SAR) renders the identification of false alarms using detectors challenging. Polarimetric SAR has the potential to improve ship detection performance owing to its distinctive polarization characteristics. The present study proposes a dualistic cascade convolutional neural network (DCCNN) algorithm driven by polarization characteristics for ship detection with fully PolSAR data. First, the new characterizations of fully PolSAR data—Optimized SPAN (OSPAN) and 6-D polarization vector (P6), were mined and defined based on the polarization coherence matrix to introduce more intact information of targets. Then, a backbone feature extraction network with parallel dualistic cascade architecture, basic geometric feature extraction network (BGFENet), and polarization feature enhancement network (PFENet) was specifically constructed, which provided the comprehensive feature representation of targets via feature fusion. Finally, the classification and regression tasks were accomplished in the fully convolutional detection subnetwork relying on extracted multi-scale fusion feature maps, while focusing on target location regression, leading to a decrease in the cost of detection efficiency caused by redundant PFENet. In addition, the corresponding training strategy according to the special architecture of DCCNN was designed to overcome the problem of labeled fully PolSAR data insufficiency and migrate refined geometric knowledge of targets in the single-polarization SAR amplitude image. Experimental results on the established fully polarized SAR dataset show that DCCNN outperforms the competitive CNN-based target detection methods by at least 6.79% in terms of average precision. Moreover, experimental results on the typical large scenes show that DCCNN is at least 0.88% higher than the well-known conventional methods in terms of F1 score.

Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance

Despite the availability of 3D digital models, 2D floor plans remain extensively used for quality inspection and maintenance as they offer firsthand information. While laser scanners enable efficient capture and reconstruction of real-world scenes, challenges arise in accurately extracting building geometry from laser scanning data due to the loss of geometric features. This paper describes a method for accurately reconstructing 2D geometric drawings of built facilities using laser scanning data. These techniques involve transforming the dimension of 3D data into 2D and displaying the registered data as pixels to extract solid lines that represent wall structures. By employing dimensionality transformation and pixelation techniques, the method supports reliable quality inspection and maintenance processes, overcoming the challenges of extracting precise geometry from laser scanning data. This paper contributes to the automated extraction of geometric features from point clouds and inspires the future development of fully automated 2D CAD and 3D BIM in alignment with Scan-to-BIM.

Numerical Method for Geometrical Feature Extraction and Identification of Patient-Specific Aorta Models in Pediatric Congenital Heart Disease

An algorithm providing information on the key geometric features of an aorta extracted from multi-slice computed tomography images is proposed. Using the numerical method, the aorta’s geometric characteristics, such as vessel cross-sectional areas and diameters, as well as distances between arteries, can be determined. This step is crucial for training the meta-model necessary to construct an expert system with a significantly reduced volume of data and for identifying key relationships between diagnoses and geometric and hydrodynamic features. This methodology is expected to be part of an innovative decision-making software product for clinical implementation. Based on clinical and anamnestic data as well as calculations, the software will provide the shunt parameters (in particular, its diameter) and installation position to ensure regular blood flow.

Lightweight integration of 3D features to improve 2D image segmentation

Scene understanding has made tremendous progress over the past few years, as data acquisition systems are now providing an increasing amount of data of various modalities (point cloud, depth, RGB...). However, this improvement comes at a large cost on computation resources and data annotation requirements. To analyze geometric information and images jointly, many approaches rely on both a 2D loss and 3D loss, requiring not only 2D per pixel-labels but also 3D per-point labels. However, obtaining a 3D groundtruth is challenging, time-consuming and error-prone. In this paper, we show that image segmentation can benefit from 3D geometric information without requiring a 3D groundtruth, by training the geometric feature extraction and the 2D segmentation network jointly, in an end-to-end fashion, using only the 2D segmentation loss. Our method starts by extracting a map of 3D features directly from a provided point cloud by using a lightweight 3D neural network. The 3D feature map, merged with the RGB image, is then used as an input to a classical image segmentation network. Our method can be applied to many 2D segmentation networks, improving significantly their performance with only a marginal network weight increase and light input dataset requirements, since no 3D groundtruth is required.

Labelled Indoor Point Cloud Dataset for BIM Related Applications

BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps.

A Convolutional Neural Network for Parameter Estimation of the Bi-GTD Model

In this paper, a novel parameter estimation method based on a convolutional neural network (CNN) is proposed to extract geometrical features of radar objects. The CNN’s design is inspired by the inversion process of a physically relevant model, called the geometrical theory of diffraction (GTD) model, whose bistatic form can be used to describe the bistatic scattering response from the target in the netted radar system. This model-inspired inversion method can automatically compensate for phase errors between multiple signal channels and obtain better parameter estimation performance than traditional methods, such as the orthogonal matching pursuit (OMP), the estimation of signal parameters via rotational invariance techniques (ESPRIT) and the multiple signal classification (MUSIC). The experimental results not only verify the validity of the proposed intelligent inversion method but also demonstrate the interpretability and generalization ability of the CNN, whose architecture is designed based on mathematical derivation.

An Improved Machine Learning Algorithm for Silver Nanoparticle Images: A Study on Computational Nano-Materials

Objectives: To study the determination and classification of Scanning Electron Microscopy (SEM) images of silver nanoparticles using digital image processing techniques and classifying the images using various machine learning classifiers namely; SVM, K-NN, and PNN classifiers. Methods: Segmentation techniques namely; Fuzzy C-Means (FCM) and K-Means were applied to extract geometric features from SEM images of silver nanoparticles. The size of nanoparticles was determined and classified based on various nano applications. The categorization of silver nanoparticles was done based on the geometrical feature value, i.e. area, and was further classified into three categories such as; 0-100 nm, 101-200 nm, and 201-500 nm using both the segmentation techniques. Further, the proposed method extracts texture features such as; kurtosis, skewness, and entropy and used SVM, K-NN, and PNN classifiers to classify the silver nanoparticle SEM images. The proposed results are analyzed and interpreted by chemical experts to observe the effectiveness of the proposed method. Findings: To improve the classification accuracy of Silver nanoparticle images, textural features such as; kurtosis, skewness, and entropy were extracted and SVM, K-NN, and PNN classifiers are used to classify the silver nanoparticle images. The performance of the proposed algorithm is evaluated based on performance evaluation measures such as; specificity, sensitivity, true predictive value, and false positive value. Higher TPV, specificity, and sensitivity values show promising results. It is observed that the SVM classifier has 75.00%, K-NN has 62.50%, and PNN yielded an accuracy of 93.00%. Novelty: Traditional synthesis and characterization techniques are inefficient and time-consuming and require more space and infrastructure to analyze the size of the nanoparticle. The current study circumvents these limitations. The silver nanoparticle images used in the present study are synthesized using rudanti fruit seeds, and the process is named as green synthesis process or biological method. This method is widely used because it does not use anytoxic chemicals, is inexpensive, andis environmentally friendly. In the previous work, only geometrical features namely; Area (pore size), and range-wise pore size were calculated depending on the application-oriented. The present study employs two phases; the training phase (knowledge base) and the testing phase(classification). The training phase includes segmentation, feature extraction, and categorization depending on the applications. The testing phase extracts the textural features such as; kurtosis, skewness, and entropy, and classifies the silver nanoparticles by using SVM, K-NN, and PNN classifiers. Keywords: Silver nanoparticles; Image segmentation; FCM; KMeans; SVM; KNN; PNN

Motion Planning of Manipulator by Points-Guided Sampling Network

This paper proposes a network called points-guided sampling net (PGSN) to guide the sampling process in sampling-based motion planner by utilizing the geometric information of obstacles. The geometric information is extracted from the point cloud of obstacles. By analyzing the properties of the point cloud, we propose a VAE feature extraction net that incorporates the variational autoencoder (VAE) framework with unique architectures designed for point clouds. Furthermore, we design a multi-modal sampling net to model the probability distribution of the states based on training trajectories taken from different environments. Based on PGSN, we propose a sampling-based motion planning algorithm called the point-guided rapidly-exploring random tree (PG-RRT). Three experiments are conducted to verify the proposed PGSN: Exp I shows the proposed VAE feature extraction net can successfully extract geometric features from the inputted point cloud; Exp II verifies the multi-modal sampling net successfully chooses corresponding mode with respect to extracted features; Exp III demonstrates the efficacy of our PG-RRT algorithm by showing PG-RRT outperforms other algorithms. Moreover, we provide theoretical analysis and insights towards understanding our model. Note to Practitioners—Obstacles cause lots of the sampling space invalid, thus the traditional sampling-based motion planning (SBMP) algorithm is usually unable to generate a trajectory within a reasonable short period of time. To improve the success rate and efficiency of SBMP, this paper proposes a novel deep neural network called points-guided sampling net (PGSN). PGSN is designed to exploit: (1) environmental point clouds and (2) training trajectories from multiple environments with different obstacles. In the first step, the point clouds include important geometric information. To utilize this information, we adopt a variational autoencoder approach which combines an encoder and a decoder together to extract geometric features more accurately from point clouds. In the second step, trajectories from multiple environments have a multi-modal property which can be represented by a truncated multivariate Gaussian mixture model. We propose a multi-modal sampling net to learn optimal parameters of this model from the training trajectories, and to select corresponding mode based on the extracted features. Experiments demonstrate that the proposed algorithm is feasible and can achieve higher success rate than the state-of-the-art methods. Our method uses a single frame of point cloud to improve efficiency, therefore multiple point clouds from different perspective maybe needed when objects occlude with each other.

Contour Primitive of Interest Extraction Network Based on Dual-Metric One-Shot Learning for Vision Measurement

Although many existing vision measurement systems have achieved high performances, they are object-specific and have limitations in flexibility. Toward intelligent vision measurement that can be conveniently reused for novel objects, this article focuses on the image geometric feature extraction with one-shot learning ability. We propose a contour primitive of interest (CPI) extraction network with dual metric (CPieNet-DM), which can obtain a designated CPI in a query image of a novel object under the guidance of only one annotated support image. First, the dual-metric learning mechanism is proposed, which not only utilizes inter-image similarity as guidance but also leverages the intra-image coherency of CPI pixels to facilitate the inference. Second, a neural network is designed to infer the CPI map based on the dual metric, which also predicts the CPI's geometric parameters. Moreover, the dual context aggregator is plugged in to provide the awareness of both images’ contexts. Third, the network training is jointly supervised by the multiple tasks of dual-metric learning, geometric parameters regression, and CPI extraction. The online hard example mining is utilized to improve the training outcome. The effectiveness of the proposed methods is validated with a series of experiments.

Recognition and Pose Estimation Method for Stacked Sheet Metal Parts

To address issues such as detection failure and the difficulty in locating gripping points caused by the stacked placement of irregular parts in the automated sheet metal production process, a highly robust method for the recognition and pose estimation of parts is proposed. First, a decoding framework for parts of a two-dimensional code is established. The morphological closed operation and topology of contours are used to locate the two-dimensional code, and the type of the part is decoded according to the structure of the two-dimensional code extracted by the projection method. Second, the recognition model of the occluded part type is constructed. The edge information of parts is extracted. The contour convex hull is used to split the part contours, and the similarity of segmented contours is calculated based on the Fourier transform. Finally, the occluded parts are located. The corner points of the metal parts are extracted by the adjacency factor of the differential chain code sequence and the contour radius of curvature. The transformation matrix between the part and the standard template is calculated using similar contour segments and contour corner points. A stereo vision system is built to detect and localize the irregular sheet metal parts for experiments, including detection and information extraction experiments of the two-dimensional laser-generated code and detection and positioning experiments of parts under different occlusion rates. The experimental results show that the decoding framework can accurately decode the two-dimensional code made by a laser under low-contrast conditions, the average recognition rate can reach 93% at multiple occlusion rates, the geometric feature extraction algorithm is more accurate than common algorithms and no pseudo-corner points, the localization error is less than 0.8 mm, and the pose angle error is less than 0.6°. The methods proposed in this paper have high accuracy and robustness.

Geometric prior guided hybrid deep neural network for facial beauty analysis

AbstractFacial beauty analysis is an important topic in human society. It may be used as a guidance for face beautification applications such as cosmetic surgery. Deep neural networks (DNNs) have recently been adopted for facial beauty analysis and have achieved remarkable performance. However, most existing DNN‐based models regard facial beauty analysis as a normal classification task. They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis. To be specific, landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision. Inspired by this, we introduce a novel dual‐branch network for facial beauty analysis: one branch takes the Swin Transformer as the backbone to model the full face and global patterns, and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts. Additionally, the designed multi‐scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches. In model optimisation, we propose a hybrid loss function, where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features. Experiments performed on the SCUT‐FBP5500 dataset and the SCUT‐FBP dataset demonstrate that our model outperforms the state‐of‐the‐art convolutional neural networks models, which proves the effectiveness of the proposed geometric regularisation and dual‐branch structure with the hybrid network. To the best of our knowledge, this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Image classification using gaussian curvature based local tetra pattern descriptor with enhanced feature selection technique

A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques.

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

Learning efficient facial landmark model for human attractiveness analysis

Existing geometric features on facial attractiveness analysis only focus on the ratios and distances, which is incomplete to represent all the information of a face. In this paper, we introduce a new category of feature, i.e., the angle features, to describe the angle of different organs such as the chin and eyes, which help boost the analysis performance in experiment. In addition, existing facial beauty analysis papers usually apply existing landmark models and extract their own different geometric feature sets on the landmarks. On the one hand, the geometric features are quite chaotic between different papers. On the other hand, most of the landmarks in the existing landmark model are useless for geometric feature extraction which wastes a lot of computational resources. To tackle these issues, we suggest to define a common geometric feature set and learn a special landmark model for attractiveness analysis. Specially, we collect all the available geometric features from the previous jobs and introduce a genetic feature selection algorithm to select the most effective geometric features. Furthermore, we introduce a special landmark model which exactly covers all the extracted geometric features. The experiments show that our method with the introduced angle features and the common feature set can outperform state-of-art facial beauty estimation methods with geometric features.

Rapid seismic risk assessment of bridges using UAV aerial photogrammetry

This paper proposes a framework for rapid seismic risk assessment of bridges using aerial photogrammetric surveys conducted by Unmanned Aerial Vehicles (UAVs). First, the data acquisition process for the photogrammetric 3D reconstruction of an asset and the subsequent procedure for computer-vision-based automatic extraction of visible geometric features are presented. Then, the extracted features are combined in the structural models to perform a seismic risk assessment in terms of capacity-to-demand ratios. Uncertainties are related to the material and geometric hidden variables. Monte Carlo simulation is performed, assuming uniform (uninformed) distributions for the input variables affected by epistemic uncertainty. The ranges of such distributions are estimated based on engineering judgment and knowledge of construction practices. The analysis provides an estimate of the epistemic uncertainties of the capacity-to-demand ratios, with limited available information obtained from UAV aerial photogrammetry. The feasibility and applicability of the proposed framework are demonstrated through a case study of a simply supported bridge in the Italian highway network. The 3D reconstruction process is validated by comparing the aerial survey with a laser scanner survey. The seismic risk of the selected bridge is assessed using the geometric information obtained from the UAV aerial photogrammetric survey. Finally, the paper discusses the applicability of the proposed methodology for bridge management purposes in an automated framework and at network scale.

Appearance design defect detection of rigid plastic products based on geometric feature extraction

Appearance design defect detection of rigid plastic products based on geometric feature extraction

Appearance design defect detection of rigid plastic products based on geometric feature extraction

Appearance design defect detection of rigid plastic products based on geometric feature extraction