Point Detection Method Research Articles

Arctic marginal ice zone interannual variability and change point detection using two definitions (1983–2022)

The ongoing decline in Arctic sea ice extent and thickness underscores the scientific significance of monitoring the marginal ice zone (MIZ), a transitional region between the open ocean and pack ice. In this study, we used Bootstrap sea ice concentration (SIC) to detect the trend and change point of the Arctic MIZ over 40 years (1983–2022) using two different MIZ definitions: SIC threshold-based (MIZ t ) and SIC anomaly-based (MIZ). This study marks the exploration of a SIC anomaly-based definition of the MIZ over the Arctic. While the two MIZ definitions yield comparable seasonal trends in marginal ice zone fraction (MIZF), the MIZ fraction values peak during the transition periods (e.g. freeze-up and break-up), while the MIZ t fraction values peak in August. The analysis also uncovers consistently higher MIZF values for the MIZ than for MIZ t across all seasons. Moreover, October and August show the fastest rate of increase in MIZ t fraction and MIZ fraction, reflecting the coinciding rapid decrease in sea ice extent during those particular months. Employing the pruned exact linear time, a multiple change point detection method, highlights a significant increase in the MIZ fraction in October (after 2005) and MIZ fraction in August (after 2007). This can be indicative of the recent climate change impacts in the Arctic region that may be linked with shifts in SIC and sea ice mobility for MIZ t and MIZ, respectively.

Ultra-Short-Term Wind Power Subsection Forecasting Method Based on Extreme Weather

Extreme weather events have become more frequent in recent years. Wind power can fluctuate violently in a short period of time due to the influence of extreme weather, which creates challenges with respect to ultra-short-term wind power forecasting. Thus, this paper proposes an ultra-short-term wind power subsection forecasting method based on extreme weather identification. A power time series trend discrimination method and an inflection point (IP) detection method are proposed to accurately identify extreme weather periods (EWPs). Feature recognition is carried out for power time series with multiple weather models. Finally, a method combining both improved gated recurrent unit (GRU) point forecasting and improved kernel density estimation-wind power probabilistic forecasting is developed. Wind farm data from Texas, USA are used to verify the predictive performance, and the results show the method effectively improves accuracy.

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.

A Low-Complexity Hand Gesture Recognition Framework via Dual mmWave FMCW Radar System.

In this paper, we propose a novel low-complexity hand gesture recognition framework via a multiple Frequency Modulation Continuous Wave (FMCW) radar-based sensing system. In this considered system, two radars are deployed distributively to acquire motion vectors from different observation perspectives. We first independently extract reflection points of the interested target from different radars by applying the proposed neighboring reflection points detection method after processing the traditional 2-dimensional Fast Fourier Transform (2D-FFT). The obtained sufficient corresponding information of detected reflection points, e.g., distances, velocities, and angle information, can be exploited to synthesize motion velocity vectors to achieve a high signal-to-noise ratio (SNR) performance, which does not require knowledge of the relative position of the two radars. Furthermore, we utilize a long short-term memory (LSTM) network as well as the synthesized motion velocity vectors to classify different gestures, which can achieve a significantly high accuracy of gesture recognition with a 1600-sample data set, e.g., 98.0%. The experimental results also illustrate the robustness of the proposed gesture recognition systems, e.g., changing the environment background and adding new gesture performers.

Deep learning-based autonomous real-time digital meter reading recognition method for natural scenes

Natural scenes with variable illumination, variable target scale and angular tilt pose significant challenges to the autonomous recognition of digital meter readings. Based on this, this paper proposes a deep learning-based autonomous real-time digital meter reading recognition method for natural scenes. First, the YOLO-style corner point detection method (YOLO-CPDM) for the reading area is proposed by reconstructing the detection heads and incorporating the corner detection loss function. Its localization accuracy is further refined by embedding attention mechanism module, implementing dynamic loss function and enhancing training data diversity through offline augmentation techniques like image rotation and flipping. Then, the detected corner points are used to geometrically correct the distorted reading area by perspective transformation to mitigate the interference caused by the shooting angle. Next, the YOLO-style end-to-end reading recognition method (YOLO-EERRM) is proposed to accurately extract the characters in the reading area. Finally, the validity of the YOLO-CPDM and YOLO-EERRM was verified on a produced dataset named SYSU-DM and 2 public datasets. Compared with the State of the arts (SOTA) keypoint detection model, the mean Average Precision @ 50:95 scores of the YOLO-CPDM improved by 2.8, 4.1, and 1.1 points, respectively, while the inference latency was only 5.3 ms, and YOLO-EERRM achieved 100 % accuracy and 3.1 ms inference latency on the SYSU-DM dataset. Statistically, the complete digital meter reading recognition method has 99.6 % accuracy and 8.6 ms inference latency, indicating that the system has high recognition accuracy and practicality.

Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR.

Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear-catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor's reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor's reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.

Key point detection method for fish size measurement based on deep learning

AbstractAccurate fish size measurement in breeding areas is crucial for the fishing industry. Unlike acoustic methods with high equipment cost and low measurement accuracy, current image‐based methods offer a promising alternative. However, these image‐based methods still face challenges in selecting measurement points. To address this issue and achieve precise measurements of individual fish, this paper introduces an automatic fish size measurement method based on key point detection. We established a Fish‐Keypoints dataset and utilized deep learning techniques for the detection of fish and their key points. Using a binocular camera system, we reconstruct a three‐dimensional coordinate system to measure key points at the fish's head and tail, facilitating fish length calculation. The detection model achieves an accuracy of 85.1% in key point detection. The proposed method is tested in both land and underwater environments, demonstrating a relative measurement error of approximately 7% for fish in pools. This confirms the proposed method's ability to accurately detect measurement points, offering superior accuracy compared to other methods.

RTV-SIFT: Harnessing Structure Information for Robust Optical and SAR Image Registration

Registration of optical and synthetic aperture radar (SAR) images is challenging because extracting located identically and unique features on both images are tricky. This paper proposes a novel optical and SAR image registration method based on relative total variation (RTV) and scale-invariant feature transform (SIFT), named RTV-SIFT, to extract feature points on the edges of structures and construct structural edge descriptors to improve the registration accuracy. First, a novel RTV-Harris feature point detection method by combining the RTV and the multiscale Harris algorithm is proposed to extract feature points on both images’ significant structures. This ensures a high repetition rate of the feature points. Second, the feature point descriptors are constructed on enhanced phase congruency edge (EPCE), which combines the Sobel operator and maximum moment of phase congruency (PC) to extract edges from structured images that enhance robustness to nonlinear intensity differences and speckle noise. Finally, after coarse registration, the position and orientation Euclidean distance (POED) between feature points is utilized to achieve fine feature point matching to improve the registration accuracy. The experimental results demonstrate the superiority of the proposed RTV-SIFT method in different scenes and image capture conditions, indicating its robustness and effectiveness in optical and SAR image registration.

A Cooperative Detection of DDoS attacks based on CNN-BiLSTM in SDN

In response to the challenges posed by the high overhead and low detection efficiency of traditional SDN, a novel approach has been proposed to detect DDoS attacks. This cooperative method leverages information entropy and deep learning techniques to divide the detection task between the data plane and control plane. An advanced CNN-BiLSTM model with batch normalization and attention mechanism is utilized to identify DDoS attack traffic. The results of experiments demonstrate that this method offers superior accuracy, detection rate, and false alarm rate compared to prior approaches. Moreover, the switch-controller collaborative detection method proposed in this research reduces the occupancy rate of CPU, in contrast to the conventional single point detection method.

Change point detection for high dimensional data via kernel measure with application to human aging brain data.

Identifying the existence and locations of change points has been a broadly encountered task in many statistical application areas. The existing change point detection methods may produce unsatisfactory results for high-dimensional data since certain distributional assumptions are made on data, which are hard to verify in practice. Moreover, some parameters (such as the number of change points) need to be estimated beforehand for some methods, making their powers sensitive to these values. Here, we propose a kernel-based -statistic to identify change points (KUCP) for high dimensional data, which is free of distributional assumptions and sup-parameter estimations. Specifically, we employ a kernel function to describe similarities among the subjects and construct a -statistic to test the existence of change point for a given location. The asymptotic properties of the -statistic are deduced. We also develop a procedure to locate the change points sequentially via a dichotomy algorithm. Extensive simulations demonstrate that KUCP has higher sensitivity in identifying existence of change points and higher accuracy in locating these change points than its counterparts. We further illustrate its practical utility by analyzing a gene expression data of human brain to detect the time point when gene expression profiles begin to change, which has been reported to be closely related with aging brain.

Bone whistle modeling method based on robust scan point tracking

Several excavations at the Hemudu site in Zhejiang Province have led to the discovery of more than 100 pieces of bird bones that were likely used to create a kind of musical instrument. However, in contrast to the Jiahu bone flute, it is not clear for what the Hemudu bone whistle was used, although it was probably used for musical purposes. In this paper, the 7000-year-old Hemudu bone whistle is reproduced with 3D scanning and printing technology. Therefore, we propose a robust scan point detection and tracking method that includes three main stages. First, we conducted robust point detection and selection where noise and irrelevant points were removed. Then, we propose to track the key points and conduct 3D model optimization. With the above efforts, we obtained the 3D model of the bone whistle, and the audio of the bone whistle is replicated. The purpose of the bone whistles is inferred and evaluated through audio analyses. The experimental results show that the method is feasible.

Improving concave point detection to better segment overlapped objects in images

This study presents a method to improve state-of-the-art concave point detection methods as the first step towards effectively segmenting overlapping objects in images. The approach relies on analysing the curvature of the object contour. This method comprises three main steps. First, the original image is preprocessed to obtain the curvature value at each contour point. Second, the regions with higher curvatures are selected and a recursive algorithm is applied to refine previously selected regions. Finally, a concave point is obtained for each region by analysing the relative position of their neighbourhood. Furthermore, the experimental results indicate that improving the detection of concave points leads to better division of clusters. To evaluate the quality of the concave point detection algorithm, a synthetic dataset was constructed to simulate the presence of overlapping objects. This dataset includes the precise location of concave points, which serve as the ground truth for evaluation. As a case study, the performance of a well-known application, such as the splitting of overlapping cells in images of peripheral blood smears samples from patients with sickle cell anaemia, was evaluated. We used the proposed method to detect concave points in cell clusters and then separated these clusters by ellipse fitting.

Image Intensity Variation Information for Interest Point Detection.

Interest point detection methods are gaining more attention and are widely applied in computer vision tasks such as image retrieval and 3D reconstruction. However, there still exist two main problems to be solved: (1) from the perspective of mathematical representations, the differences among edges, corners, and blobs have not been convincingly explained and the relationships among the amplitude response, scale factor, and filtering orientation for interest points have not been thoroughly explained; (2) the existing design mechanism for interest point detection does not show how to accurately obtain intensity variation information on corners and blobs. In this paper, the first- and second-order Gaussian directional derivative representations of a step edge, four common genres of corners, an anisotropic-type blob, and an isotropic-type blob are analyzed and derived. Multiple interest point characteristics are discovered. The characteristics for interest points that we obtained help us describe the differences among edges, corners, and blobs, explain why the existing interest point detection methods with multiple scales cannot properly obtain interest points from images, and present novel corner and blob detection methods. Extensive experiments demonstrate the superiority of our proposed methods in terms of detection performance, robustness to affine transformations, noise, image matching, and 3D reconstruction.

Measuring Point Planning and Fitting Optimization of the Flange and Spigot Structures of Aeroengine Rotors

An optimized measuring point planning and fitting method for rotor flange and spigot structures was proposed to achieve precise measurement of position and pose of the aeroengine rotors during docking processes. Firstly, the impact of circumferential phase angle, distribution range angle, total number of measuring points, and number of distribution rings on measurement uncertainty was analyzed. The measuring point planning schemes for flange and spigot were proposed. Secondly, the Gauss Newton iterative solution principle considering damping factors was clarified. Subsequently, an optimized iterative reweighting method consisting of weight iterative estimation, singular value detection under the Chauvenet criterion, and clustering detection was proposed for fitting the flange annular end face. A mapping point total least squares method with practical geometric significance was proposed for fitting the spigot cylinder face. Finally, measuring and fitting experiments were performed. The singular measuring point detection methods were verified. Under the optimized fitting methods, the goodness of fit and average orthogonal distance of flange and spigot structures are 0.756 and 0.089 mm, respectively, which have higher fitting accuracy than the other traditional methods.

Retracted: Key Point Detection Method of 3D Network Model Based on Virtual Reality Technology

Retracted: Key Point Detection Method of 3D Network Model Based on Virtual Reality Technology

Features of Cheyne-Stokes respiration automatically extracted from CPAP airflow signal raw data: Identification of discriminating features to detect heart failure

BackgroundContinuous positive airway pressure (CPAP) telemonitoring data is collected daily from millions of sleep apnea patients. This huge amount of data can be used for the early detection of treatment failures and could reveal incident acute or chronic cardiovascular events. However, the available automatically computed metrics do not fully characterize sleep-disordered breathing. ObjectiveTo find methods to process metrics characterizing Cheyne-Stokes respiration (CSR) that are able to discriminate between CSR related to heart failure (HF) and CSR related to other underlying conditions. MethodsThe raw airflow signals recorded by CPAP devices were analysed. Change point detection methods were used to isolate each respiratory cycle and CSR cycle. Simple algorithms were implemented to extract key features from the signal. Binary logistic regression was performed to identify the characteristics of the CSR airflow signal that is associated with the presence of underlying HF. ResultsLonger CSR cycles, a longer CSR episode, a greater variation in the amplitude of inspiration, a smaller increase in big breaths, a lower inter-cycle variability and a shorter breath duration were features associated with the occurrence of CSR in the context of HF. ConclusionThe proposed automated computation of CSR characteristics presents a novel tool to include in the CPAP software or remote monitoring platforms for monitoring patients’ treatment and comorbidities; and a step towards cross-disciplinary patient management.

Research on Key Point Detection Method of Mechanical Arm

Accurate keypoint localization is an important research direction in the field of robotic arms. Deep learning is being used more and more for motion capture and pose estimation. This is due to their ability to detect user-defined key points without the use of labels. We present an approach for detecting robot 3D key points coordinate based on two perspectives. Two images of different perspectives are processed by the same deep neural network to detect 2D projections of key points (such as joints) associated with the robot. Triangularization is then applied to recover 3D coordinates. In addition, the existing dataset of robot critical points is obtained by simulation. It seriously impedes the advancement of this field. We establish an actual robot key points dataset and corresponding evaluation criterion. The experimental results demonstrate that our framework achieves cutting-edge detection performance. It is reusable for other robot models.

A Cooperative Detection of DDoS Attacks Based on CNN-BiLSTM in SDN

In view of the problem that detecting DDoS attack traffic in traditional SDN depends on the controller continuously collecting traffic and running the detection model, resulting in excessive controller overhead, low detection efficiency, increased traffic forwarding delay, and easy to cause "single point of failure", a cooperative detection method of DDoS attack in SDN based on information entropy and deep learning is proposed, which divides part of the detection task into the data plane for detection based on information entropy and uses the improved CNN-BiLSTM model to detect DDoS attack traffic on control plane. The experimental results show that, compared with the SVC-RF method in recent years, the accuracy of the proposed CNN-BiLSTM model is increased by 0.74%, the detection rate is increased by 1.42%, and the false alarm rate is reduced by 1.5%. Compared with the BiLSTM model, the accuracy is increased by 0.75%, the detection rate is increased by 0.64%, and the false alarm rate is reduced by 1.14%. Compared with the RF method, the accuracy is increased by 2.34%, the detection rate is increased by 3.88%, and the false alarm rate is reduced by 4%. Compared with the traditional single point detection method which only depends on the controller, the proposed switch-controller cooperative detection method reduces the CPU occupancy of the controller by about 12% and the detection time by about 13 seconds.

Automatic meter error detection with a data-driven approach

Meter error is one of the main contributing factors to unexpected fuel losses or gains in storage tanks at service stations. Although fuel dispensers are expected to be calibrated to standard accuracy periodically to ensure fair and reliable trade in the fuel market, some fuel retailers are unable to keep up with the standards. The current industry practice relies on onsite inspection to identify the issue, which leads to a cost burden if inspections are scheduled too frequently. To the best of our knowledge, there is no previously reported research tailored to the remote meter error detection problem. In this paper, we propose a novel framework for remote and automatic meter error detection via a data-driven approach based on inventory data and fuel transaction data. Specifically, we propose to use mean shift change point detection methods, including statistical-based as well as deep learning-based methods (LSTM-VAE, VAE, Kernel learning), to approach the problem. We present results on our data sets containing both real-world and simulated meter error data, and further evaluate these methods on several widely-used benchmark datasets, to assess their validity, advantages and limitations. The obtained results show that LSTM-VAE outperforms other models in most of the settings for the meter error dataset and the benchmark datasets.

LiDAR Inertial Odometry Based on Indexed Point and Delayed Removal Strategy in Highly Dynamic Environments.

Simultaneous localization and mapping (SLAM) is considered a challenge in environments with many moving objects. This paper proposes a novel LiDAR inertial odometry framework, LiDAR inertial odometry-based on indexed point and delayed removal strategy (ID-LIO) for dynamic scenes, which builds on LiDAR inertial odometry via smoothing and mapping (LIO-SAM). To detect the point clouds on the moving objects, a dynamic point detection method is integrated, which is based on pseudo occupancy along a spatial dimension. Then, we present a dynamic point propagation and removal algorithm based on indexed points to remove more dynamic points on the local map along the temporal dimension and update the status of the point features in keyframes. In the LiDAR odometry module, a delay removal strategy is proposed for historical keyframes, and the sliding window-based optimization includes the LiDAR measurement with dynamic weights to reduce error from dynamic points in keyframes. We perform the experiments both on the public low-dynamic and high-dynamic datasets. The results show that the proposed method greatly increases localization accuracy in high-dynamic environments. Additionally, the absolute trajectory error (ATE) and average RMSE root mean square error (RMSE) of our ID-LIO can be improved by 67% and 85% in the UrbanLoco-CAMarketStreet dataset and UrbanNav-HK-Medium-Urban-1 dataset, respectively, when compared with LIO-SAM.