Search alternatives:
feature » features (Expand Search)
Showing 1,401 - 1,420 results of 4,166 for search 'Feature detection algorithms', query time: 0.12s Refine Results
  1. 1401

    Defect identification method for overhead transmission lines based on SIFT algorithm by Qiang Liu, Xi Zheng, Qiuhan Zhang, Hongjie Sun, Jun Yan

    Published 2025-12-01
    “…The system uses a Scale-Invariant Feature Transform (SIFT) algorithm to precisely identify defect markers by initially extracting the texture features of standard wires and subsequently identifying variations that indicate faults. …”
    Get full text
    Article
  2. 1402

    Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning by Wan-Peng Sun, Ji-Guo Zhang, Yichao Li, Wan-Ting Hou, Fu-Wen Zhang, Jing-Fei Zhang, Xin Zhang

    Published 2025-01-01
    “…We find that the spectral morphology parameters, specifically spectral running ( r ), represent the key features for identifying repeaters from the nonrepeaters. …”
    Get full text
    Article
  3. 1403
  4. 1404

    Robustness evaluation of commercial liveness detection platform by Pengcheng WANG, Haibin ZHENG, Jianfei ZOU, Ling PANG, Hu LI, Jinyin CHEN

    Published 2022-02-01
    “…Liveness detection technology has become an important application in daily life, and it is used in scenarios including mobile phone face unlock, face payment, and remote authentication.However, if attackers use fake video generation technology to generate realistic face-swapping videos to attack the living body detection system in the above scenarios, it will pose a huge threat to the security of these scenarios.Aiming at this problem, four state-of-the-art Deepfake technologies were used to generate a large number of face-changing pictures and videos as test samples, and use these samples to test the online API interfaces of commercial live detection platforms such as Baidu and Tencent.The test results show that the detection success rate of Deepfake images is generally very low by the major commercial live detection platforms currently used, and they are more sensitive to the quality of images, and the false detection rate of real images is also high.The main reason for the analysis may be that these platforms were mainly designed for traditional living detection attack methods such as printing photo attacks, screen remake attacks, and silicone mask attacks, and did not integrate advanced face-changing detection technology into their liveness detection.In the algorithm, these platforms cannot effectively deal with Deepfake attacks.Therefore, an integrated live detection method Integranet was proposed, which was obtained by integrating four detection algorithms for different image features.It could effectively detect traditional attack methods such as printed photos and screen remakes.It could also effectively detect against advanced Deepfake attacks.The detection effect of Integranet was verified on the test data set.The results show that the detection success rate of Deepfake images by proposed Integranet detection method is at least 35% higher than that of major commercial live detection platforms.…”
    Get full text
    Article
  5. 1405

    Deep Learning-Based Atmospheric Visibility Detection by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin, Fan Que

    Published 2024-11-01
    “…Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. …”
    Get full text
    Article
  6. 1406

    Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm by Shun Cheng, Zhiqian Wang, Shaojin Liu, Yan Han, Pengtao Sun, Jianrong Li

    Published 2024-11-01
    “…The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.…”
    Get full text
    Article
  7. 1407

    Anomaly Detection Dataset for Industrial Control Systems by Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson

    Published 2023-01-01
    “…Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. …”
    Get full text
    Article
  8. 1408

    Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes by Yufeng Xu, Yan Fu

    Published 2024-11-01
    “…However, the complex indoor environment and background pose challenges to the detection task. The YOLOv8 algorithm is a cutting-edge technology in the field of object detection, but it is still affected by indoor low-light environments and large changes in human scale. …”
    Get full text
    Article
  9. 1409

    Image Inpainting Algorithm Based on Structure-Guided Generative Adversarial Network by Li Zhao, Tongyang Zhu, Chuang Wang, Feng Tian, Hongge Yao

    Published 2025-07-01
    “…The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. …”
    Get full text
    Article
  10. 1410

    Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm by Lijuan Xu, Dalong Liu, Huanjian Ma

    Published 2024-01-01
    “…Therefore, this paper studies the use of the You Only Look Once series algorithm and the use of the regression network general target tracking algorithm to improve the detection and tracker part of the track-learning-detection algorithm. …”
    Get full text
    Article
  11. 1411

    Application of computer vision algorithm in ceramic surface texture analysis and prediction by Yao Tian, Feifei Zhu

    Published 2025-03-01
    “…In order to improve the surface quality of ceramic products, this paper proposes a ceramic surface texture recognition analysis and texture generation prediction method based on computer vision algorithm. In this method, laser lines are used to scan along the radial direction of ceramic, and the position of laser stripe is located by straight line detection algorithm. …”
    Get full text
    Article
  12. 1412
  13. 1413

    YOLO-HF: Early Detection of Home Fires Using YOLO by Bo Peng, Tae-Kook Kim

    Published 2025-01-01
    “…This gap causes object detection algorithms to produce high false positive and false negative rates in research on the early detection of such fires. …”
    Get full text
    Article
  14. 1414
  15. 1415
  16. 1416

    Emotion Recognition in the Eye Region Using Textural Features, IBP and HOG by Laura Jalili, Josue Espejel, Jair Cervantes, Farid Lamont

    Published 2024-01-01
    “…By focusing on these regions, we aim to accurately capture the nuances of emotional states. Methodology: The algorithm we devised not only detects facial features but also autonomously isolates the eyes and mouth regions. …”
    Get full text
    Article
  17. 1417
  18. 1418

    Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi, Hamed Zolbanin

    Published 2024-12-01
    “…We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. …”
    Get full text
    Article
  19. 1419
  20. 1420

    Rapid video copy detection on compressed domain by ZHANG Yong-dong, ZHANG Dong-ming, GUO Jun-bo, TANG Sheng

    Published 2009-01-01
    “…To reduce the detection time efficiency under large scale data environment, a rapid algorithm was proposed on compressed domain using a two-level hierarchical detection scheme.The ordinal measures of DCT coefficients were adopted as visual features for similarity-matching in order to reduce the computational load in video decoding.Inverted indexing structure was used to accelerate the first level detection process.The experiment results show, compared with the previous algorithm, the algorithm can improve the detection speed obviously with the similar detection precision.…”
    Get full text
    Article