Traditional methods for performing biofouling detection are based upon probing object surfaces for subsequent laboratory research. Such methods result in large effort, they are expensive, and require expert consultations. Knowledge of the type of biological contaminants is necessary to protect objects from their harmful impact. In this paper we propose a method for determination of types of biological contaminants existing on the objects surface. The proposed method uses a collection of object's images as input. The collection contains images obtained in the visible and near infrared spectral bands. During pre-processing the series, all images are converted to a selected shooting point, and the background is removed. Feature vector is built from combinations of formal vegetation indices. To recognize the type of biological contaminants, we used a pre-trained classifier based on SVM method with RBF kernel.

Original languageEnglish
Title of host publication11th International Conference on Computer Science and Information Technologies, CSIT 2017
EditorsSamvel Shoukourian
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-161
Number of pages4
Volume2018-March
ISBN (Electronic)9781538628300
ISBN (Print)9781538628300
DOIs
StatePublished - 9 Mar 2018
Event11th International Conference on Computer Science and Information Technologies, CSIT 2017 - Yerevan, Armenia
Duration: 20 Sep 201725 Sep 2017

Publication series

Name11th International Conference on Computer Science and Information Technologies, CSIT 2017
Volume2018-March

Conference

Conference11th International Conference on Computer Science and Information Technologies, CSIT 2017
Country/TerritoryArmenia
CityYerevan
Period20/09/1725/09/17

    Research areas

  • biological fouling identification, image processing, pattern recognition

    Scopus subject areas

  • Computer Science (miscellaneous)
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Information Systems
  • Control and Optimization

ID: 35949410