Edge detection using neural network committee

Abstract

This article considers edge detection using neural network approach, and describes the integration of neural networks into a committee. The novelty of the proposed technique comes from two ideas: firstly, since the training set describes a solution, the proposed algorithm is able to detect multidirectional edges on noised image. Secondly, to increase the accuracy of detection, neural networks were combined into a committee.

Original language	English
Pages	66-69
Number of pages	4
Publication status	Published - 2018
Externally published	Yes
Event	28th International Conference on Computer Graphics and Vision, GraphiCon 2018 - Tomsk, Russian Federation Duration: 24 Sep 2018 → 27 Sep 2018

Conference

Conference	28th International Conference on Computer Graphics and Vision, GraphiCon 2018
Country	Russian Federation
City	Tomsk
Period	24.9.18 → 27.9.18

Keywords

Committee classification
Digital image
Edge detection
Neural network
Neural network committee

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition

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title = "Edge detection using neural network committee",

abstract = "This article considers edge detection using neural network approach, and describes the integration of neural networks into a committee. The novelty of the proposed technique comes from two ideas: firstly, since the training set describes a solution, the proposed algorithm is able to detect multidirectional edges on noised image. Secondly, to increase the accuracy of detection, neural networks were combined into a committee.",

keywords = "Committee classification, Digital image, Edge detection, Neural network, Neural network committee",

author = "Diego Oliva and Demin Anton and Demeshko Maria",

year = "2018",

language = "English",

pages = "66--69",

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AB - This article considers edge detection using neural network approach, and describes the integration of neural networks into a committee. The novelty of the proposed technique comes from two ideas: firstly, since the training set describes a solution, the proposed algorithm is able to detect multidirectional edges on noised image. Secondly, to increase the accuracy of detection, neural networks were combined into a committee.

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KW - Neural network

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