Filter-based feature selection for rail defect detection

被引：2
|
作者：
C. Mandriota
M. Nitti
N. Ancona
E. Stella
A. Distante
机构：
[1] Istituto di Studi sui Sistemi Intelligenti per l’Automazione,
[2] C.N.R.,undefined
来源：
Machine Vision and Applications | 2004年 / 15卷
关键词：
Rail detection; Filter bank; Texture feature; K-nearest neighbor classifier;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Over the last few years research has been oriented toward developing a machine vision system for locating and identifying, automatically, defects on rails. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. The aim of this paper is to present an experimental comparison among three filtering approaches, based on texture analysis of rail surfaces, to detect the presence/absence of a particular class of surface defects: corrugation.
引用
收藏
页码：179 / 185
页数：6
相关论文
共 50 条
  • [21] Filter-Based Feature Selection Methods for Industrial Sensor Data: A Review
    Luftensteiner, Sabrina
    Mayr, Michael
    Chasparis, Georgios
    [J]. BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY (DAWAK 2021), 2021, 12925 : 242 - 249
  • [22] Improved Filter-Based Feature Selection Using Correlation and Clustering Techniques
    Atmakuru, Akhila
    Di Fatta, Giuseppe
    Nicosia, Giuseppe
    Badii, Atta
    [J]. MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2023, PT I, 2024, 14505 : 379 - 389
  • [23] Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection
    Balhareth, Ghaida
    Ilyas, Mohammad
    [J]. SENSORS, 2024, 24 (17)
  • [24] Filter-Based Feature Selection Method for Predicting Students' Academic Performance
    Dafid
    Ermatita
    [J]. 2022 INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ITS APPLICATIONS (ICODSA), 2022, : 309 - 314
  • [25] Comparison of Stability for Different Families of Filter-Based and Wrapper-Based Feature Selection
    Wald, Randall
    Khoshgoftaar, Taghi
    Napolitano, Amri
    [J]. 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 2, 2013, : 457 - 464
  • [26] Particle filter-based damage prognosis using online feature fusion and selection
    Li, Tianzhi
    Chen, Jian
    Yuan, Shenfang
    Cadini, Francesco
    Sbarufatti, Claudio
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 203
  • [27] New filter-based feature selection criteria for identifying differentially expressed genes
    Loo, LH
    Roberts, S
    Hrebien, L
    Kam, M
    [J]. ICMLA 2005: FOURTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, 2005, : 135 - 144
  • [28] Filter-Based Feature Selection and Machine-Learning Classification of Cancer Data
    Farsi, Mohammed
    [J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2021, 28 (01): : 83 - 92
  • [29] Filter-based unsupervised feature selection using Hilbert–Schmidt independence criterion
    Samaneh Liaghat
    Eghbal G. Mansoori
    [J]. International Journal of Machine Learning and Cybernetics, 2019, 10 : 2313 - 2328
  • [30] Strong approximate Markov blanket and its application on filter-based feature selection
    Hua, Zhongsheng
    Zhou, Jian
    Hua, Ye
    Zhang, Wei
    [J]. APPLIED SOFT COMPUTING, 2020, 87
12345