Rail defect classification by adaptive self organized map (Contributo in atti di convegno)

Type
Label
  • Rail defect classification by adaptive self organized map (Contributo in atti di convegno) (literal)
Anno
  • 2001-01-01T00:00:00+01:00 (literal)
Alternative label
  • Massimiliano Nitti; Ettore Stella; Clelia Mandriota; Cosimo Distante (2001)
    Rail defect classification by adaptive self organized map
    in SPIE - Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, Newton, MA-USA, 28th-31st October 2001
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Massimiliano Nitti; Ettore Stella; Clelia Mandriota; Cosimo Distante (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISSIA; CNR-ISSIA; CNR-ISSIA; Università di Lecce (literal)
Titolo
  • Rail defect classification by adaptive self organized map (literal)
Abstract
  • In the last years the detection and classification of surface defects of material is assuming great importance. Visual inspection can help to increase the product quality and, in particular context, the maintenance of products. The railway infrastructure is a particular field in which the periodical surface inspection of rolling plane can help an operator to prevent critical situation. We use a Gabor filter to emphasis the image regions with grey level variation. The Gabor filter h( x,y) is characterised by a frequency F, direction and parameter ?. We have selected experimentally four filters with directions 0, ?/4, ?/2 and ?3/4 with F=?2/8 cycle/pixel and ? = 2. The problem of detection and classification is a crucial part of our work because cannot be defined an exhaustive training set of defect and non-defect images. It is necessary a method able to self-learn changes. Investigating about this problem we propose in the paper a novel Self Organised Map network, appropriately modified, for detection and classification of rail defects. The proposed SOM network learns to classify input vectors according to how they are grouped in the input space. So, SOM learns both the distribution and topology of the input vectors belonging to the training set. During the training phase, the neurons in the layer of a SOM form some cluster or bubble representing the input training with minimum distance among them. The novelty is to modify the SOM network in order to learn continuously during the test phase.or bubble representing the input training with minimum distance among them. The novelty is to modify the SOM network in order to learn continuously during the test phase. (literal)
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