Abstract
This 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.
Highlights
Image segmentation plays an important role in computer vision and image analysis
The objective of this paper is to present an effective digital fuzzy c-means algorithm (FCM)-S architecture for image segmentation
The proposed architecture can be directly applied to color image segmentation by implementing xk as a 3-dimension vector
Summary
Image segmentation plays an important role in computer vision and image analysis. The segmentation results can be used to identify regions of interest and objects in the scene, which is very beneficial to the subsequent image analysis or annotation. The fuzzy c-means algorithm (FCM) [1] is one of the most used technique for image segmentation. The accuracy of FCM is due to the employment of fuzziness for the clustering of each image pixel. This enables the fuzzy clustering methods to retain more information from the original image than the crisp or hard segmentation. The original intensity-based FCM algorithm functions well on segmenting most noise-free images, it fails to segment images corrupted by noise, outliers and other imaging artifacts.
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