Fuzzy neural network for edge detection and Hopfield network for edge enhancement
Thesis (M.Sc.)--Memorial University of Newfoundland, 1999. Computer Science Bibliography: leaves 109-120 This thesis presents an artificial neural network system for edge detection and edge enhancement. The system can accomplish the following tasks: (a) obtain edges; (b) enhance edges by recovering...
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis |
Language: | English |
Published: |
1999
|
Subjects: | |
Online Access: | http://collections.mun.ca/cdm/ref/collection/theses3/id/49087 |
id |
ftmemorialunivdc:oai:collections.mun.ca:theses3/49087 |
---|---|
record_format |
openpolar |
spelling |
ftmemorialunivdc:oai:collections.mun.ca:theses3/49087 2023-05-15T17:23:32+02:00 Fuzzy neural network for edge detection and Hopfield network for edge enhancement Wang, Tzu-ch'ing, 1964- Memorial University of Newfoundland. Dept. of Computer Science 1999 x, 143 leaves : ill. Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses3/id/49087 eng eng Electronic Theses and Dissertations (14.66 MB) -- http://collections.mun.ca/PDFs/theses/Wang_Ziqing.pdf a1357900 http://collections.mun.ca/cdm/ref/collection/theses3/id/49087 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Neural networks (Computer science) Fuzzy systems Image processing Text Electronic thesis or dissertation 1999 ftmemorialunivdc 2015-08-06T19:17:53Z Thesis (M.Sc.)--Memorial University of Newfoundland, 1999. Computer Science Bibliography: leaves 109-120 This thesis presents an artificial neural network system for edge detection and edge enhancement. The system can accomplish the following tasks: (a) obtain edges; (b) enhance edges by recovering missing edges and eliminate false edges caused by noise. The research is comprised of three stages, namely, adaptive fuzzification which is employed to fuzzify the input patterns, edge detection by a three-layer feedforward fuzzy neural network, and edge enhancement by a modified Hopfield neural network. The typical sample patterns are first fuzzified. Then they are used to train the proposed fuzzy neural network. After that, the trained network is able to determine the edge elements with eight orientations. Pixels having high edge membership are traced for further processing. Based on constraint satisfaction and the competitive mechanism, interconnections among neurons are determined "n the Hopfield neural network. A criterion is provided to find the final stable result which contains the enhanced edge measurement. -- The proposed neural networks are simulated on a SUN Sparc station. One hundred and twenty-three training samples are well chosen to cover all the edge and non-edge cases and the performance of the system will not be improved by adding more training samples. Test images are degraded by random noise up to 30% of the original images. Compared with standard edge detection operators, the proposed fuzzy neural network obtains very good results. Thesis Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
institution |
Open Polar |
collection |
Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
op_collection_id |
ftmemorialunivdc |
language |
English |
topic |
Neural networks (Computer science) Fuzzy systems Image processing |
spellingShingle |
Neural networks (Computer science) Fuzzy systems Image processing Wang, Tzu-ch'ing, 1964- Fuzzy neural network for edge detection and Hopfield network for edge enhancement |
topic_facet |
Neural networks (Computer science) Fuzzy systems Image processing |
description |
Thesis (M.Sc.)--Memorial University of Newfoundland, 1999. Computer Science Bibliography: leaves 109-120 This thesis presents an artificial neural network system for edge detection and edge enhancement. The system can accomplish the following tasks: (a) obtain edges; (b) enhance edges by recovering missing edges and eliminate false edges caused by noise. The research is comprised of three stages, namely, adaptive fuzzification which is employed to fuzzify the input patterns, edge detection by a three-layer feedforward fuzzy neural network, and edge enhancement by a modified Hopfield neural network. The typical sample patterns are first fuzzified. Then they are used to train the proposed fuzzy neural network. After that, the trained network is able to determine the edge elements with eight orientations. Pixels having high edge membership are traced for further processing. Based on constraint satisfaction and the competitive mechanism, interconnections among neurons are determined "n the Hopfield neural network. A criterion is provided to find the final stable result which contains the enhanced edge measurement. -- The proposed neural networks are simulated on a SUN Sparc station. One hundred and twenty-three training samples are well chosen to cover all the edge and non-edge cases and the performance of the system will not be improved by adding more training samples. Test images are degraded by random noise up to 30% of the original images. Compared with standard edge detection operators, the proposed fuzzy neural network obtains very good results. |
author2 |
Memorial University of Newfoundland. Dept. of Computer Science |
format |
Thesis |
author |
Wang, Tzu-ch'ing, 1964- |
author_facet |
Wang, Tzu-ch'ing, 1964- |
author_sort |
Wang, Tzu-ch'ing, 1964- |
title |
Fuzzy neural network for edge detection and Hopfield network for edge enhancement |
title_short |
Fuzzy neural network for edge detection and Hopfield network for edge enhancement |
title_full |
Fuzzy neural network for edge detection and Hopfield network for edge enhancement |
title_fullStr |
Fuzzy neural network for edge detection and Hopfield network for edge enhancement |
title_full_unstemmed |
Fuzzy neural network for edge detection and Hopfield network for edge enhancement |
title_sort |
fuzzy neural network for edge detection and hopfield network for edge enhancement |
publishDate |
1999 |
url |
http://collections.mun.ca/cdm/ref/collection/theses3/id/49087 |
genre |
Newfoundland studies University of Newfoundland |
genre_facet |
Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (14.66 MB) -- http://collections.mun.ca/PDFs/theses/Wang_Ziqing.pdf a1357900 http://collections.mun.ca/cdm/ref/collection/theses3/id/49087 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
_version_ |
1766113076451475456 |