Segmentation of degraded images has always been a difficult problem to solve. Efficient object extraction from noisy images can be achieved by neuro-fuzzy clustering algorithms where noise pixels are identified during the clustering process and assigned low weights to avoid their degradation effect on prototype validity. We present here a new approach to noise reduction prior to segmentation by using a two step process namely AFLC (adaptive fuzzy leader clustering)-median. This new two step process has been specifically tailored to remove speckle noise. The first step is to use the AFLC that has been designed to follow leader clustering using a hybrid neuro-fuzzy model developed by integrating a modified ART-1 model with fuzzy-C-means (FCM). This integration provides a powerful yet fast method for recognizing embedded data structure. In speckled imagery, AFLC is used to isolate the speckle noise pixels by segmenting the image into several clusters controlled by a vigilance parameter. Once the speckles have been identified, a median filter is used centered on each speckle noise pixel. The resulting image after undergoing the AFLC-median process demonstrates reduction of speckle noise while retaining sharp edges for improved segmentation.
|Number of pages||5|
|State||Published - 1999|
|Event||Proceedings of the 1999 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS'99 - New York, NY, USA|
Duration: Jun 10 1999 → Jun 12 1999
|Conference||Proceedings of the 1999 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS'99|
|City||New York, NY, USA|
|Period||06/10/99 → 06/12/99|