Adaptive clustering for image segmentation
| dc.creator | Neeruganti, Jagadeesh | |
| dc.date.accessioned | 2016-11-14T23:16:05Z | |
| dc.date.available | 2011-02-18T20:28:50Z | |
| dc.date.available | 2016-11-14T23:16:05Z | |
| dc.date.issued | 1998-12 | |
| dc.degree.department | Electrical and Computer Engineering | en_US |
| dc.description.abstract | The purpose of image segmentation is to separate different objects embedded in an image. Many image segmentation techniques are available in the literature. Some of the simple techniques employ thresholding based on the gray level histogram, while a number of other sophisticated techniques have been developed in recent years. Among the recent techniques, limited success has been achieved by employing some fuzzy selfsupervised neural networks for object extraction. This work reviews the basic segmentation techniques and demonstrates the applications of adaptive clustering techniques, which make use of neural networks and fuzzy methods for image segmentation. The adaptive clustering techniques used are two neuro-fuzzy techniques namely, the Integrated Adaptive Fuzzy Clustering (lAFC) and Adaptive Fuzzy Leader Clustering (AFLC). The performances of these techniques are compared with the performance of the fuzzy c-means (FCM algorithm as applied to image segmentation. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2346/13945 | en_US |
| dc.language.iso | eng | |
| dc.publisher | Texas Tech University | en_US |
| dc.rights.availability | Unrestricted. | |
| dc.subject | Pattern recognition systems | en_US |
| dc.subject | Cluster analysis | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Image processing | en_US |
| dc.subject | Fuzzy systems | en_US |
| dc.title | Adaptive clustering for image segmentation | |
| dc.type | Thesis |