Contour Estimation Research Articles

Contour-based object forecasting for autonomous driving

A novel algorithm, called contour-based object forecasting (COF), to simultaneously perform contour-based segmentation and depth estimation of objects in future frames in autonomous driving systems is proposed in this paper. The proposed algorithm consists of encoding, future forecasting, decoding, and 3D rendering stages. First, we extract the features of observed frames, including past and current frames. Second, from these causal features, we predict the features of future frames using the future forecast module. Third, we decode the predicted features into contour and depth estimates. We obtain object depth maps aligned with segmentation masks via the depth completion using the predicted contours. Finally, from the prediction results, we render the forecasted objects in a 3D space. Experimental results demonstrate that the proposed algorithm reliably forecasts the contours and depths of objects in future frames and that the 3D rendering results intuitively visualize the future locations of the objects.

Maneuvering extended target tracking method based on transformer network

This research introduces a new approach to address the challenges related to inaccuracies in shape estimation and motion state tracking during the tracking of maneuvering extended targets. The method combines a novel neural network model with a cubature Kalman filter. Initially, the target’s shape variation is represented using a second-order autoregressive model, and Bayesian filtering is employed for target contour estimation in static environments. Subsequently, the kinematic state tracking of maneuvering targets is enhanced by modifying a neural network model based on Transformer architecture. Through the effective integration of these techniques, precise tracking of maneuvering extended targets is achieved, facilitating accurate estimation of irregular contour information while tracking the targets’ motion state in real-time. The efficacy of the proposed approach is further confirmed through various simulation scenarios, underscoring its suitability for tracking maneuvering extended targets.

Statistical Analysis of Features for Detecting Leukemia

In this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools for medical diagnostics research. By visualizing and analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase in aberrant white blood cells (WBCs). Recognizing acute leukemia tumor cells in blood smear images (BSI) is a challenging assignment. Image segmentation is regarded as the most significant step in the automated identification of this disease. The innovative concavity-based segmentation algorithm is employed in this study to segment WBC in sub-images from the ALLIDB2 database. The concave endpoints and elliptical features are used in the segmentation step of convex-shaped cell images. The procedure involves the extraction of contour evidence, which detects the visible section of each object, and contour estimation, which corresponds to the final object’s contours. Following the identification of the cells and their internal structure by concavity-based segmentation, the cells are categorized based on their morphological and statistical features. The method was evaluated using a public dataset meant to test classification and segmentation approaches. The statistical tool SPSS is used to independently check the significance of derived features. For classification, significant features are passed into machine learning techniques such as support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), and Nave Bayes (NB). With an AUC of 98.9% and a total accuracy of 95%, the neural network model performed better. We advocate using the neural network model to identify acute leukemia cells based on its accuracy.

Athletes' Action Recognition and Posture Estimation Algorithm Based on Image Processing Technology

A posture estimation algorithm based on image processing technology involves analyzing images or video frames to determine the position and orientation of human bodies or specific body parts. This process typically begins with detecting key points or landmarks on the body, such as joints or anatomical features. This paper presents a novel approach for athletes' action recognition and posture estimation using image processing technology, specifically focusing on the Contour Mapping Multivariant Recognition and Estimation (CMMRE) algorithm. The primary objective is to develop a robust system capable of accurately identifying human actions and estimating postures from visual data, with potential applications in sports analysis, surveillance, and human-computer interaction. The CMMRE algorithm employs advanced techniques for contour mapping, feature extraction, and prediction, leveraging deep learning methodologies to analyze and interpret visual information effectively. Through a series of experiments and simulations, the algorithm's performance is evaluated, showcasing its ability to achieve high accuracy in action recognition tasks across various scenarios. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement. The algorithm achieved an average accuracy of 95% in recognizing walking and running actions, and 80% in identifying jumping actions. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement.

CovXtreme : MATLAB software for non-stationary penalised piecewise constant marginal and conditional extreme value models

The covXtreme software provides functionality for estimation of marginal and conditional extreme value models, non-stationary with respect to covariates, and environmental design contours. Generalised Pareto (GP) marginal models of peaks over threshold are estimated, using a piecewise-constant representation for the variation of GP threshold and scale parameters on the (potentially multidimensional) covariate domain of interest. The conditional variation of one or more associated variates, given a large value of a single conditioning variate, is described using the conditional extremes model of Heffernan and Tawn (2004), the slope term of which is also assumed to vary in a piecewise constant manner with covariates. Optimal smoothness of marginal and conditional extreme value model parameters with respect to covariates is estimated using cross-validated roughness-penalised maximum likelihood estimation. Uncertainties in model parameter estimates due to marginal and conditional extreme value threshold choice, and sample size, are quantified using a bootstrap resampling scheme. Estimates of environmental contours using various schemes, including the direct sampling approach of Huseby et al. 2013, are calculated by simulation or numerical integration under fitted models. The software was developed in MATLAB for metocean applications, but is applicable generally to multivariate samples of peaks over threshold data. The software and case study data can be downloaded from GitHub, with an accompanying user guide.

Inference for New Environmental Contours Using Extreme Value Analysis

AbstractEnvironmental contours are often used in engineering applications to describe risky combinations of variables according to some definition of an exceedance probability. These contours can be used to both understand multivariate extreme events in environmental processes and mitigate against their effects, e.g. in the design of structures. Such ideas are also useful in other disciplines, with the types of extreme events of interest depending on the context. Despite clear connections with extreme value modelling, much of this methodology has so far not been exploited in the estimation of environmental contours; in this work, we provide a way to unify these areas. We focus on the bivariate case, introducing two new definitions of environmental contours. We develop techniques for their inference which exploit a non-standard radial and angular decomposition of the variables, building on previous work for estimating limit sets. Specifically, we model the upper tails of the radial distribution using a generalised Pareto distribution, with adaptable smoothing of the parameters of this distribution. Our methods work equally well for asymptotically independent and asymptotically dependent variables, so do not require us to distinguish between different joint tail forms. Simulations demonstrate reasonable success of the estimation procedure, and we apply our approach to an air pollution data set, which is of interest in the context of environmental impacts on health.Supplementary materials accompanying this paper appear online.

Design optimization of soft robotic fingers biologically inspired by the fin ray effect with intrinsic force sensing

Soft grippers display adaptability, dexterity, and safety, garnering increased interest in their applications. However, accurately modeling their nonlinear deformation and sensing the grasping force remain challenging. Here, a generalized closed-form kinetostatic model of a biomimetic FRE soft finger is established based on the chained-beam-constraint model. A methodology combining a geometry-based contact constraint technique is presented, utilizing the minimum strain energy principle for sensorless, precise, and rapid estimation of the deformed contours and contact force when the FRE finger grasps an arbitrary object with a continuous surface. Method verification using FEA demonstrates a maximum error of no more than 1.28% and 2.44% for the contour and contact force estimation, respectively. Subsequently, a multiobjective genetic algorithm is used to optimize the FRE finger design for better adaptability and greater grasping force in a special scenario. Finally, the finger's capability to accurately perceive grasping force and deformed contours was demonstrated by four groups of grasping tests using cylinder and cube objects, with maximum average absolute error and relative error of 0.083 N, 9.84%, and 1.335 mm, 10.06%, respectively.

Numerical Simulation on the Effect of Infiltration and Evapotranspiration on the Residual Slope

Soil suction plays an important role in governing the stability of slopes. Environmental sustainability could be jeopardized by hazards, such as slope failures (forest destruction, landscape alteration, etc.). However, the quantification of the suction effect on slope stability is a challenging task as the soil suction is usually affected by the precipitation and evapotranspiration. Numerical simulation plays an important role in the estimation of contour in soil suction due to rainfall and evapotranspiration as long-term and widespread monitoring is rarely conducted. The result of numerical simulation is highly dependent on the accuracy of the input parameters. Hence, suction monitoring plays an important role in verifying the result of numerical simulation. However, as a conventional tensiometer is limited to 100 kPa soil suction, it is hard to verify the performance of numerical simulation where suction is higher than 100 kPa. The osmotic tensiometer developed by Nanyang Technological University (NTU) can overcome this problem. It is now possible to monitor changes in soil suction higher than 100 kPa (up to 2500 kPa) for an extended period in the field. In this study, a procedure was proposed to estimate suction changes in residual soil based on rainfall and evapotranspiration data. Numerical simulation was carried out based on the soil properties and geometry of a residual soil slope from Jurong Formation Singapore. Changes in soil suction due to rainfall and evaporation were simulated and compared with the readings from the NTU osmotic tensiometers installed at 0.15 m and 0.50 m from the slope surface in the field. It was observed that numerical simulation was able to capture the variations of suctions accurately at greater depths. However, at shallow depths, erratic suction changes due to difficulties in capturing transpiration.

Deep learning‐based hybrid reconstruction algorithm for fibre instance segmentation from 3D x‐ray tomographic images

Abstract3D x‐ray tomography is a powerful scanning technique used for generating images of complex fibre structures. A novel machine‐learning algorithm to identify and separate individual fibres using 3D images is proposed in this article. The developed four‐step hybrid 3D fibre segmentation algorithm involves deep‐learning aided semantic segmentation that slices 3D images to create 2D images for fibre extraction, elliptical contour estimation combined with the marker‐controlled watershed algorithm for separating fibres from the background area, identifying individual fibres through 3D reconstruction, and, lastly, the 3D object refining approach based on outlier object detection and replacement. The proposed methodology is implemented on a real‐time sample of nylon fibre bundle under compression and its 3D x‐ray image volume to validate the performance. The results show its superior performance compared to off‐the‐shelf image processing algorithms in terms of precision, that is, with a validation accuracy greater than 90%, and efficiency, that is, preventing the need for a huge data set and reducing the complexity.

Determining desired sorbent properties for proton-coupled electron transfer-controlled CO2 capture using an adaptive sampling-refined classifier

Electrochemical CO2 capture technologies have been found to consume less energy than the industry standard of thermal separations, but their real-world applicability requires that they also operate at comparable rates. Optimizing for both low energy demands and high capture rates is complicated by trade-offs between the two objectives and the many manipulable solution chemistry variables, including species type and concentration. Here, we computationally identified the solution chemistries that are most likely to outperform thermal separations in both energy demand and capture rate for electrochemical capture driven by proton-coupled electron transfer reactions by using an adaptive sampling contour estimation method. This approach provided high confidence inferences with few simulation runs by selecting the most informative conditions to test. We found that moderately basic pKa values of the reduced form of the redox-active compound were the most important variables for low energy and high rate CO2 capture.

Solving an Inverse Problem for Time-Series-Valued Computer Simulators via Multiple Contour Estimation

Computer simulators are often used as a substitute of complex real-life phenomena, which are either expensive or infeasible to experiment with. This paper focuses on how to efficiently solve the inverse problem for an expensive to evaluate time-series-valued computer simulator. The research is motivated by a hydrological simulator which has to be tuned for generating realistic rainfall–runoff measurements in Athens, Georgia, USA. Assuming that the simulator returns g(x, t) over L time points for a given input x, the proposed methodology begins with a careful construction of a discretization (time-) point set of size $$k \ll L$$ , achieved by adopting a regression spline approximation of the target response series at k optimal knot locations $$\{t_1^*, t_2^*,\ldots ,t_k^*\}$$ . Subsequently, we solve k scalar-valued inverse problems for simulator $$g(x,t_j^*)$$ via the contour estimation method. The proposed approach, named MSCE, also facilitates the uncertainty quantification of the inverse solution. Extensive simulation study is used to demonstrate the performance comparison of the proposed method with the popular competitors for several test-function-based computer simulators and a real-life rainfall–runoff measurement model.

Deep learning for the detection of semantic features in tree X-ray CT scans

According to the industry, the value of wood logs is heavily influenced by their internal structure, particularly the distribution of knots within the trees. Nowadays, CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees. Knowing where the tree semantic features, especially knots, contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products. However, this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline, knot localization and log contour, in a robust and efficient manner. In this paper, we propose an effective methodology based on deep learning for performing these different tasks by processing CT-scanner images with deep convolutional neural networks. To meet this objective, three end-to-end trainable pipelines are proposed. The first pipeline is focused on centers detection using CNNs architecture with a regression head, the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture. The different architectures are tested on several tree species. With these experiments, we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness. The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

Clustering and Subsequent Contour and Motion Estimation of Automotive Objects Using a Network of Cooperative Radar Sensors

A method for collaborative target list processing of multiple radar sensors simultaneously estimating multiple extended targets is presented and discussed. A scheme to cluster target points from multiple radar sensors jointly is described that deals with overclustering and separation of objects. Algorithms to estimate the contour (i.e., dimensions) and motion of these objects are proposed and evaluated. Additionally, the influence of the number of sensors on the results is investigated and compared to single sensor estimates. The processing of measurements with three radar sensors and two vehicles shows the advantages of using more than one sensor, as well as the proposed algorithms.

Dispersion‐enhanced sequential batch sampling for adaptive contour estimation

AbstractIn computer simulation and optimal design, sequential batch sampling offers an appealing way to iteratively stipulate optimal sampling points based upon existing selections and efficiently construct surrogate modeling. Nonetheless, the issue of near duplicates poses tremendous quandary for sequential learning. It refers to the situation that selected critical points cluster together in each sampling batch, which are individually but not collectively informative towards the optimal design. Near duplicates severely diminish the computational efficiency as they barely contribute extra information towards update of the surrogate. To address this issue, we impose a dispersion criterion on concurrent selection of sampling points, which essentially forces a sparse distribution of critical points in each batch, and demonstrate the effectiveness of this approach in adaptive contour estimation. Specifically, we adopt Gaussian process surrogate to emulate the simulator, acquire variance reduction of the critical region from new sampling points as a dispersion criterion, and combine it with the modified expected improvement (EI) function for critical batch selection. The critical region here is the proximity of the contour of interest. This proposed approach is vindicated in numerical examples of a two‐dimensional four‐branch function, a four‐dimensional function with a disjoint contour of interest and a time‐delay dynamic system.

Improvement of Strong Tracking UKF-SLAM Approach using Three-position Ultrasonic Detection

As for the uncertainty problem of detection and estimation of robot localization and mapping using ultrasonic sensor, this paper proposes the improvement of Strong Tracking UKF-SLAM approach using three-position ultrasonic detection. A three-position ultrasonic detection model is first of all built for reducing these uncertainties through topological relationship screening and environmental contour estimation. Then Strong Tracking UKF-SLAM approach is improved by using multiple fade factors to fuse the ultrasonic measurement data and motion model information of robot for obtaining more accurate localization and mapping. Finally, we construct simulation and indoor experimental environments and design the mobile robot system with ultrasonic sensor for verification. The simulation represents that the improved algorithm has less error and more accurate effect than original algorithms in localization and mapping of mobile robot. The indoor environmental experiment is performed for illustrating the feasibility and effectiveness of the proposed method. The proposed method has certain reference value for research of Simultaneous Localization and Mapping.

Automated differentiation of skin melanocytes from keratinocytes in high‐resolution histopathology images using a weakly‐supervised deep‐learning framework

AbstractEarly detection and accurate diagnosis of melanoma skin cancer which accounts for 75% of all skin cancer mortalities would significantly improve survival rates. Melanoma is characterized by an abnormal proliferation of malignant melanocytes in the epidermis of the skin, which is composed predominantly of keratinocytes. Localization and differentiation of melanocytes from keratinocytes in the skin in whole slide images (WSIs) is an important initial task towards the diagnosis of the tumor. Because of the small size of melanocytes and the dominance of keratinocytes in the epidermis, identification of melanocytes can be challenging and prone to errors. We propose a new fully‐automated framework based on deep‐machine learning to identify melanocytes and keratinocytes with detection accuracy of 90.5% and 87.4%, respectively. This framework begins with segmenting the epidermis layer inside 640 × 320 × 3 super tiles of WSIs using a DeepLabV3+ model followed by a weakly‐supervised deep learning approach deploying a fine‐tuned pre‐trained visual geometry group (VGG)‐16 model, gradient‐weighted class activation mapping (Grad‐CAM), Otsu's and contour estimation methods in order to identify melanocytes and keratinocytes inside each 64 × 64 × 3 tile (i.e., a subdivision of the super tile). The proposed framework outperforms the state‐of‐the‐art methods as well as provides a significant improvement of 28% and 17% over the fully supervised faster region‐based convolutional neural network (R‐CNN) in detecting melanocytes and keratinocytes respectively without the need for expensive expert fine labels for model training and validation. The proposed framework offers a promising accurate tool to aid pathologists in differentiating melanocytes from keratinocytes that would eventually support the diagnosis of melanoma.

Disbond contour estimation in aluminum/CFRP adhesive joint based on the phase velocity variation of Lamb waves

Adhesive lap joints between composite and metal plates have been widely used in industrial fields including the automotive industry, marine manufacturing and aerospace engineering. Low quality of operation, harsh environment, adhesive aging and other disadvantages may lead to disbonding. To assess the disbond contour at an adhesive interface, this study proposes a detection method based on the phase velocity variation of Lamb waves. First, the dispersion curves of Lamb waves in both single-layer and bonded multi-layer areas are acquired using the semi-analytical finite element (FE) method. Subsequently, numerical models of Lamb wave propagation in intact and disbonded joints are established. Due to the difference in phase velocity between relevant modes, the phase difference of Lamb wave between disbonded and intact joints is quantitatively linear with the disbond length under specific excitation, which is verified by the simulated signals based on FEs. Then, a probabilistic reconstruction algorithm based on phase delay is employed to localize the disbond center. On this basis, the edge points of the disbond are acquired, and the convex envelope of these points is sketched for disbond contour estimation. As a result, both the location and shape of the disbond can be obtained, thereby providing information for subsequent assessment. The experiment is carried out on an adhesive lap joint specimen composed of an aluminum plate and a quasi-isotropic carbon fiber reinforced plastic laminate, and the results demonstrate the effectiveness of the proposed method.

Seismic demand analysis using environmental contours of vector-valued ground motion intensity measures

A study is presented on seismic demand analysis using environmental contours of vector-valued ground motion intensity measures. The environmental contours specify combinations of intensity measures for estimating an engineering demand parameter with a given exceedance probability. Copulas are employed to model the joint distribution of vector-valued ground motion intensity measures for developing the environmental contours. A framework is presented for copula estimation of spectral accelerations using ground motion records from stations on stratified sand and silt deposits in Mexico City. The statistical dependence between pairs of spectral accelerations at a range of periods is analyzed in terms of Kendall’s τ and used for estimating parametric copula models. Goodness of fit of the estimated copulas is examined and the Akaike information criteria is applied for selection of copula models. Environmental contours of spectral accelerations are developed adopting the inverse FORM approach and employed to estimate mean exceedance rates of maximum interstory drift of a building model. Both, the cases of deterministic and stochastic response models of interstory drift are examined. The accuracy of the environmental contour estimates is assessed by comparison with Monte Carlo simulations and estimates from long-term analyses. It is found that the approximation of the failure surface in the inverse FORM approach is quite appropriate for a range of return periods for which very precise or reasonably accurate response estimates were obtained from the environmental contours.

Single image depth estimation based on sculpture strategy

Supervised deep learning approaches have achieved state-of-the-art performance in single image depth estimation. However, the procedure to train the network is not intervened, due to the black box character of deep learning. To enhance the generalization ability of trained models, we employ a learned prior that is inspired by the process of sculpturing, to guide the model’s learning. From the view of sculpturing, the depth estimation can be divided into two consecutive steps: the initial contour estimation and the detail refinement. The contour of scene can be introduced as the intermediate supervision which enables the network to learn true knowledge and accelerate training. In addition, the edges of the scene contain much semantic information. To better use this structured knowledge, we use an attention mechanism to integrate the edges of the scene to guide training. We also create a more precise ground truth by using self-adaptive Gaussian interpolation. Compared to other datasets, our dataset is denser, and the details of small objects and the ordinal relation in the depths of different objects is preserved. Additionally, the distribution of depth values does not change. The experimental results on the KITTI, Make3D and NYU Depth V2 datasets show that the method achieved better performance with respect to other state-of-the-art depth estimation approaches. Code is available at https://github.com/XTU-PR-LAB/HG-Net.

Environmental contours for wind-resistant bridge design in complex terrain

Accurate estimation of the extreme wind fields is crucial for long-span bridge design. The current practice is focused on estimating the extreme mean wind speed, neglecting the inherent uncertainty in the turbulence model parameters. However, full-scale measurements on bridges show that such uncertainties are significant and should be considered in design. Here, the environmental contour method (ECM) is used to obtain long-term extreme wind fields considering uncertainties from the mean wind speed, turbulence intensities and spectral parameters measured at the Sulafjord Bridge site. Design contours of combinations of wind field parameters are obtained for target return periods of 4, 50 and 100 years. The contours are based on a proposed probabilistic modeling strategy that combines hindcast mesoscale simulations and field measurements. The contour estimates are also compared with state-of-the-art design values from the design recommendations. It is concluded that the environmental contours provide a more complete and yet intuitive description of the wind field at the bridge site compared to the current design methodology. The ECM is found suitable for obtaining design wind fields at new long-span bridge sites as it makes use of the limited site data more efficiently and it is still easy-to-use for the practicing engineer.