1 Citation (Scopus)

Abstract

The paper proposes a distributed system for detecting the types of biological contaminants existing on objects' surfaces. The system implements biofouling detection method based on an image processing technique. The system processes a series of object images obtained in the visible and near infrared spectral ranges. One image in the series is marked as a base imag e. All images of the series are converted to one common shooting point and to one common angle. The object of interest is detected in the base image, and then the background is removed from all images. To recognize the type of biological contaminants, we use a pre-trained classifier based on support vector machine method. The proposed detection method has an obvious parallelism in data processing. Each image in the series, except the base, can be processed independently. Therefore it is quite easy to implement this method on a computing cluster. The central host of the cluster is used to implement non-parallel branches of the image-processing algorithm. These branches are namely interactive segmentation, a search for key points in the base image, and classifier training. The central host also solves the problems of data distribution among cluster nodes and of synchronization of nodes. Other nodes of a cluster are processing other images in a series, except the base image. They implement a search for key points, convert all images to a common shooting point, remove the background, and identify types of biofouling. After processing, we form the map of pollution for each image. This map then gets sent to the storage at the central node of the cluster.

Original languageEnglish
Title of host publicationDistributed Computing and Grid-technologies in Science and Education 2016
Subtitle of host publication Selected Papers of the 7th International Conference Distributed Computing and Grid-technologies in Science and Education Dubna, Russia, July 4-9, 2016
PublisherRWTH Aahen University
Pages245-249
Number of pages5
Publication statusPublished - 2016
Event7th International Conference Distributed Computing and Gridtechnologies in Science and Education, GRID 2016 - Dubna
Duration: 4 Jul 20169 Jul 2016

Publication series

NameCEUR Workshop Proceedings
PublisherRWTH Aahen University
Volume1787
ISSN (Print)1613-0073

Conference

Conference7th International Conference Distributed Computing and Gridtechnologies in Science and Education, GRID 2016
CountryRussian Federation
CityDubna
Period4/07/169/07/16

Fingerprint

Biofouling
Image processing
Impurities
Classifiers
Cluster computing
Support vector machines
Synchronization
Pollution
Infrared radiation
Processing

Scopus subject areas

  • Computer Science(all)

Cite this

Grishkin, V., Smirnov, K., & Stepenko, N. (2016). Distributed system for detection of biological contaminants. In Distributed Computing and Grid-technologies in Science and Education 2016: Selected Papers of the 7th International Conference Distributed Computing and Grid-technologies in Science and Education Dubna, Russia, July 4-9, 2016 (pp. 245-249). (CEUR Workshop Proceedings; Vol. 1787). RWTH Aahen University.
Grishkin, Valery ; Smirnov, Konstantin ; Stepenko, Nikolai. / Distributed system for detection of biological contaminants. Distributed Computing and Grid-technologies in Science and Education 2016: Selected Papers of the 7th International Conference Distributed Computing and Grid-technologies in Science and Education Dubna, Russia, July 4-9, 2016. RWTH Aahen University, 2016. pp. 245-249 (CEUR Workshop Proceedings).
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title = "Distributed system for detection of biological contaminants",
abstract = "The paper proposes a distributed system for detecting the types of biological contaminants existing on objects' surfaces. The system implements biofouling detection method based on an image processing technique. The system processes a series of object images obtained in the visible and near infrared spectral ranges. One image in the series is marked as a base imag e. All images of the series are converted to one common shooting point and to one common angle. The object of interest is detected in the base image, and then the background is removed from all images. To recognize the type of biological contaminants, we use a pre-trained classifier based on support vector machine method. The proposed detection method has an obvious parallelism in data processing. Each image in the series, except the base, can be processed independently. Therefore it is quite easy to implement this method on a computing cluster. The central host of the cluster is used to implement non-parallel branches of the image-processing algorithm. These branches are namely interactive segmentation, a search for key points in the base image, and classifier training. The central host also solves the problems of data distribution among cluster nodes and of synchronization of nodes. Other nodes of a cluster are processing other images in a series, except the base image. They implement a search for key points, convert all images to a common shooting point, remove the background, and identify types of biofouling. After processing, we form the map of pollution for each image. This map then gets sent to the storage at the central node of the cluster.",
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Grishkin, V, Smirnov, K & Stepenko, N 2016, Distributed system for detection of biological contaminants. in Distributed Computing and Grid-technologies in Science and Education 2016: Selected Papers of the 7th International Conference Distributed Computing and Grid-technologies in Science and Education Dubna, Russia, July 4-9, 2016. CEUR Workshop Proceedings, vol. 1787, RWTH Aahen University, pp. 245-249, Dubna, 4/07/16.

Distributed system for detection of biological contaminants. / Grishkin, Valery; Smirnov, Konstantin; Stepenko, Nikolai.

Distributed Computing and Grid-technologies in Science and Education 2016: Selected Papers of the 7th International Conference Distributed Computing and Grid-technologies in Science and Education Dubna, Russia, July 4-9, 2016. RWTH Aahen University, 2016. p. 245-249 (CEUR Workshop Proceedings; Vol. 1787).

Research outputpeer-review

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AU - Smirnov, Konstantin

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N2 - The paper proposes a distributed system for detecting the types of biological contaminants existing on objects' surfaces. The system implements biofouling detection method based on an image processing technique. The system processes a series of object images obtained in the visible and near infrared spectral ranges. One image in the series is marked as a base imag e. All images of the series are converted to one common shooting point and to one common angle. The object of interest is detected in the base image, and then the background is removed from all images. To recognize the type of biological contaminants, we use a pre-trained classifier based on support vector machine method. The proposed detection method has an obvious parallelism in data processing. Each image in the series, except the base, can be processed independently. Therefore it is quite easy to implement this method on a computing cluster. The central host of the cluster is used to implement non-parallel branches of the image-processing algorithm. These branches are namely interactive segmentation, a search for key points in the base image, and classifier training. The central host also solves the problems of data distribution among cluster nodes and of synchronization of nodes. Other nodes of a cluster are processing other images in a series, except the base image. They implement a search for key points, convert all images to a common shooting point, remove the background, and identify types of biofouling. After processing, we form the map of pollution for each image. This map then gets sent to the storage at the central node of the cluster.

AB - The paper proposes a distributed system for detecting the types of biological contaminants existing on objects' surfaces. The system implements biofouling detection method based on an image processing technique. The system processes a series of object images obtained in the visible and near infrared spectral ranges. One image in the series is marked as a base imag e. All images of the series are converted to one common shooting point and to one common angle. The object of interest is detected in the base image, and then the background is removed from all images. To recognize the type of biological contaminants, we use a pre-trained classifier based on support vector machine method. The proposed detection method has an obvious parallelism in data processing. Each image in the series, except the base, can be processed independently. Therefore it is quite easy to implement this method on a computing cluster. The central host of the cluster is used to implement non-parallel branches of the image-processing algorithm. These branches are namely interactive segmentation, a search for key points in the base image, and classifier training. The central host also solves the problems of data distribution among cluster nodes and of synchronization of nodes. Other nodes of a cluster are processing other images in a series, except the base image. They implement a search for key points, convert all images to a common shooting point, remove the background, and identify types of biofouling. After processing, we form the map of pollution for each image. This map then gets sent to the storage at the central node of the cluster.

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Grishkin V, Smirnov K, Stepenko N. Distributed system for detection of biological contaminants. In Distributed Computing and Grid-technologies in Science and Education 2016: Selected Papers of the 7th International Conference Distributed Computing and Grid-technologies in Science and Education Dubna, Russia, July 4-9, 2016. RWTH Aahen University. 2016. p. 245-249. (CEUR Workshop Proceedings).