Determination of size distribution using neural networks

JH Stevens; JAG Nijhuis; L Spaanenburg

Determination of size distribution using neural networks

JH Stevens^*
, JAG Nijhuis
, L Spaanenburg

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In this paper we present a novel approach to the estimation of size distributions of grains in water from images. External conditions such as the concentrations of grains in water cannot be controlled. This poses problems for local image analysis which tries to identify and measure single grains. Our approach uses global image features such as coarseness and uses a neural network to learn a mapping from these feature values to a representation of the cumulative size distribution used in the sand industry.

Original language	English
Title of host publication	COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - NEURAL NETWORKS & ADVANCED CONTROL STRATEGIES
Editors	M Mohammadian
Place of Publication	AMSTERDAM
Publisher	I O S PRESS
Pages	40-45
Number of pages	2
ISBN (Print)	90-5199-473-7
Publication status	Published - 1999
Event	International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 99) - , Austria Duration: 17-Feb-1999 → 19-Feb-1999

Publication series

Name	CONCURRENT SYSTEMS ENGINEERING SERIES
Publisher	I O S PRESS
Volume	54
ISSN (Print)	1383-7575

Other

Other	International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 99)
Country/Territory	Austria
Period	17/02/1999 → 19/02/1999

Keywords

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title = "Determination of size distribution using neural networks",

abstract = "In this paper we present a novel approach to the estimation of size distributions of grains in water from images. External conditions such as the concentrations of grains in water cannot be controlled. This poses problems for local image analysis which tries to identify and measure single grains. Our approach uses global image features such as coarseness and uses a neural network to learn a mapping from these feature values to a representation of the cumulative size distribution used in the sand industry.",

keywords = "TEXTURE",

author = "JH Stevens and JAG Nijhuis and L Spaanenburg",

year = "1999",

language = "English",

isbn = "90-5199-473-7",

series = "CONCURRENT SYSTEMS ENGINEERING SERIES",

publisher = "I O S PRESS",

pages = "40--45",

editor = "M Mohammadian",

booktitle = "COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL \& AUTOMATION - NEURAL NETWORKS \& ADVANCED CONTROL STRATEGIES",

note = "International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 99) ; Conference date: 17-02-1999 Through 19-02-1999",

}

Stevens, JH, Nijhuis, JAG & Spaanenburg, L 1999, Determination of size distribution using neural networks. in M Mohammadian (ed.), COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - NEURAL NETWORKS & ADVANCED CONTROL STRATEGIES. CONCURRENT SYSTEMS ENGINEERING SERIES, vol. 54, I O S PRESS, AMSTERDAM, pp. 40-45, International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 99), Austria, 17/02/1999.

Determination of size distribution using neural networks. / Stevens, JH; Nijhuis, JAG; Spaanenburg, L.
COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - NEURAL NETWORKS & ADVANCED CONTROL STRATEGIES. ed. / M Mohammadian. AMSTERDAM: I O S PRESS, 1999. p. 40-45 (CONCURRENT SYSTEMS ENGINEERING SERIES; Vol. 54).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Determination of size distribution using neural networks

AU - Stevens, JH

AU - Nijhuis, JAG

AU - Spaanenburg, L

PY - 1999

Y1 - 1999

N2 - In this paper we present a novel approach to the estimation of size distributions of grains in water from images. External conditions such as the concentrations of grains in water cannot be controlled. This poses problems for local image analysis which tries to identify and measure single grains. Our approach uses global image features such as coarseness and uses a neural network to learn a mapping from these feature values to a representation of the cumulative size distribution used in the sand industry.

AB - In this paper we present a novel approach to the estimation of size distributions of grains in water from images. External conditions such as the concentrations of grains in water cannot be controlled. This poses problems for local image analysis which tries to identify and measure single grains. Our approach uses global image features such as coarseness and uses a neural network to learn a mapping from these feature values to a representation of the cumulative size distribution used in the sand industry.

KW - TEXTURE

M3 - Conference contribution

SN - 90-5199-473-7

T3 - CONCURRENT SYSTEMS ENGINEERING SERIES

SP - 40

EP - 45

BT - COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION - NEURAL NETWORKS & ADVANCED CONTROL STRATEGIES

A2 - Mohammadian, M

PB - I O S PRESS

CY - AMSTERDAM

T2 - International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 99)

Y2 - 17 February 1999 through 19 February 1999

ER -

Determination of size distribution using neural networks

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