Image Filtering with Neural Networks: applications and performance evaluation

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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Abstract

A simple and elegant method to design image filters with neural networks is proposed: using small networks that scan the image and perform position invariant filtering. In the theses examples of image filtering with error backpropagation networks for edge detection, image deblurring and noise suppression are given. The main advantages with respect to other filter design methods are: ease of design and the possibility to tune the filters for a specific application and type of image. The main disadvantage is that no insight in the underlaying processes is gained, only a filter design is obtained. For the evaluation of the performance of image filters the average risk (AVR) is proposed as a performance criterion. The AVR has a solid basis in statistical classification theory. It consists of a weighted sum of the probabilities on different types of errors. The errors are weighted with a cost function. Thus both the frequency and the costs of errors are taken into account. An extension of the theory of AVR is proposed to handle multiple and continuous output. It is shown that the AVR can be regarded as a distance measure. As an example an edge detector evaluation method, based on AVR is described. Finally the optimisation of neural networks for minimum AVR is investigated. An example illustrates that this can lead to considerable performance improvements with respect to the optimisation using the 'standard' optimisation criteria as used in the learning rules of neural networks.
Original languageUndefined
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Bosman, Dick, Supervisor
  • Houkes, Z., Advisor
  • Bosman, D., Supervisor
Award date6 Nov 1992
Place of PublicationEnschede
Publisher
Print ISBNs90-9005555-X
Publication statusPublished - 6 Nov 1992

Keywords

  • SCS-Safety
  • IR-65125
  • EWI-14173
  • METIS-111320

Cite this

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title = "Image Filtering with Neural Networks: applications and performance evaluation",
abstract = "A simple and elegant method to design image filters with neural networks is proposed: using small networks that scan the image and perform position invariant filtering. In the theses examples of image filtering with error backpropagation networks for edge detection, image deblurring and noise suppression are given. The main advantages with respect to other filter design methods are: ease of design and the possibility to tune the filters for a specific application and type of image. The main disadvantage is that no insight in the underlaying processes is gained, only a filter design is obtained. For the evaluation of the performance of image filters the average risk (AVR) is proposed as a performance criterion. The AVR has a solid basis in statistical classification theory. It consists of a weighted sum of the probabilities on different types of errors. The errors are weighted with a cost function. Thus both the frequency and the costs of errors are taken into account. An extension of the theory of AVR is proposed to handle multiple and continuous output. It is shown that the AVR can be regarded as a distance measure. As an example an edge detector evaluation method, based on AVR is described. Finally the optimisation of neural networks for minimum AVR is investigated. An example illustrates that this can lead to considerable performance improvements with respect to the optimisation using the 'standard' optimisation criteria as used in the learning rules of neural networks.",
keywords = "SCS-Safety, IR-65125, EWI-14173, METIS-111320",
author = "Spreeuwers, {Lieuwe Jan}",
note = "http://www.sas.el.utwente.nl/home/spreeuwers/luukspreeuwers_phdthesis.pdf",
year = "1992",
month = "11",
day = "6",
language = "Undefined",
isbn = "90-9005555-X",
publisher = "University of Twente",
address = "Netherlands",
school = "University of Twente",

}

Image Filtering with Neural Networks: applications and performance evaluation. / Spreeuwers, Lieuwe Jan.

Enschede : University of Twente, 1992. 169 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

TY - THES

T1 - Image Filtering with Neural Networks: applications and performance evaluation

AU - Spreeuwers, Lieuwe Jan

N1 - http://www.sas.el.utwente.nl/home/spreeuwers/luukspreeuwers_phdthesis.pdf

PY - 1992/11/6

Y1 - 1992/11/6

N2 - A simple and elegant method to design image filters with neural networks is proposed: using small networks that scan the image and perform position invariant filtering. In the theses examples of image filtering with error backpropagation networks for edge detection, image deblurring and noise suppression are given. The main advantages with respect to other filter design methods are: ease of design and the possibility to tune the filters for a specific application and type of image. The main disadvantage is that no insight in the underlaying processes is gained, only a filter design is obtained. For the evaluation of the performance of image filters the average risk (AVR) is proposed as a performance criterion. The AVR has a solid basis in statistical classification theory. It consists of a weighted sum of the probabilities on different types of errors. The errors are weighted with a cost function. Thus both the frequency and the costs of errors are taken into account. An extension of the theory of AVR is proposed to handle multiple and continuous output. It is shown that the AVR can be regarded as a distance measure. As an example an edge detector evaluation method, based on AVR is described. Finally the optimisation of neural networks for minimum AVR is investigated. An example illustrates that this can lead to considerable performance improvements with respect to the optimisation using the 'standard' optimisation criteria as used in the learning rules of neural networks.

AB - A simple and elegant method to design image filters with neural networks is proposed: using small networks that scan the image and perform position invariant filtering. In the theses examples of image filtering with error backpropagation networks for edge detection, image deblurring and noise suppression are given. The main advantages with respect to other filter design methods are: ease of design and the possibility to tune the filters for a specific application and type of image. The main disadvantage is that no insight in the underlaying processes is gained, only a filter design is obtained. For the evaluation of the performance of image filters the average risk (AVR) is proposed as a performance criterion. The AVR has a solid basis in statistical classification theory. It consists of a weighted sum of the probabilities on different types of errors. The errors are weighted with a cost function. Thus both the frequency and the costs of errors are taken into account. An extension of the theory of AVR is proposed to handle multiple and continuous output. It is shown that the AVR can be regarded as a distance measure. As an example an edge detector evaluation method, based on AVR is described. Finally the optimisation of neural networks for minimum AVR is investigated. An example illustrates that this can lead to considerable performance improvements with respect to the optimisation using the 'standard' optimisation criteria as used in the learning rules of neural networks.

KW - SCS-Safety

KW - IR-65125

KW - EWI-14173

KW - METIS-111320

M3 - PhD Thesis - Research UT, graduation UT

SN - 90-9005555-X

PB - University of Twente

CY - Enschede

ER -