Efficient Fuzzy C-Means Architecture for Image Segmentation
AbstractThis paper presents a novel VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the the proposed circuit is an effective alternative for real-time image segmentation with low area cost and low misclassification rate. View Full-Text
Share & Cite This Article
Li, H.-Y.; Hwang, W.-J.; Chang, C.-Y. Efficient Fuzzy C-Means Architecture for Image Segmentation. Sensors 2011, 11, 6697-6718.
Li H-Y, Hwang W-J, Chang C-Y. Efficient Fuzzy C-Means Architecture for Image Segmentation. Sensors. 2011; 11(7):6697-6718.Chicago/Turabian Style
Li, Hui-Ya; Hwang, Wen-Jyi; Chang, Chia-Yen. 2011. "Efficient Fuzzy C-Means Architecture for Image Segmentation." Sensors 11, no. 7: 6697-6718.