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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ünal, M. Onat, M. Bal, A. |
| Copyright Year | 2010 |
| Description | Author affiliation: Elektronik ve Bilgisayar Eğitimi Bölümü Marmara Üniversitesi (Ünal, M.; Onat, M.) || Enformatik Bölümü Yıldız Teknik Üniversitesi (Bal, A.) |
| Abstract | Cellular Neural Networks (CNN) having parallel processing capabilities present important advantages in image processing applications. The coefficients of the template matrices and the threshold values of CNN should be optimized to obtain the desired output image. The learning algorithms designed for classical feed forward neural networks are not suitable for CNN due to its dynamic architecture. Researchers are still working on development of generalized learning algorithms for CNN. In this study, the CNN training is realized by ant colony optimization (ACO) technique. The results obtained by trained CNN show that ant colony based learning algorithm is very successful for image feature extraction problems such as edge, corner, vertical and horizontal edge detections. |
| Starting Page | 471 |
| Ending Page | 474 |
| File Size | 558928 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424496723 |
| e-ISBN | 9781424496716 |
| DOI | 10.1109/SIU.2010.5653917 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-04-22 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Image edge detection Heuristic algorithms Artificial neural networks Classification algorithms Cellular neural networks Optimization |
| Content Type | Text |
| Resource Type | Article |
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