26 February 2010 Supervised colour image segmentation using granular reflex fuzzy min-max neural network
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Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75460T (2010) https://doi.org/10.1117/12.856289
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore
Abstract
Granular data classification and clustering is an upcoming and important issue in the field of pattern recognition. This paper proposes a Supervised Colour Image Segmentation technique based on Granular Reflex Fuzzy Min-Max Neural Network (GrRFMN). GrRFMN architecture consists of a reflex mechanism inspired from human brain to handle class overlaps. It has been observed that most of the image segmentation techniques are pixel based. It means that segmentation is done on pixel-by-pixel basis. In this paper, a novel granule based approached for colour image segmentation is proposed. In the proposed technique granules of an image are processed. This results into a fast segmentation process. The image segmentation discussed here is a supervised. In training phase, GrRFMN learns different classes in the image using class granules. A trained GrRFMN is then used to segment the image. As GrRMN is trainable on-line in a single pass through data, the proposed method is easily extended for video sequence segmentation. Results on various standard images are presented.
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Abhijeet V. Nandedkar, Abhijeet V. Nandedkar, } "Supervised colour image segmentation using granular reflex fuzzy min-max neural network", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460T (26 February 2010); doi: 10.1117/12.856289; https://doi.org/10.1117/12.856289
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