Image Segmentation Algorithm of Colorimetric Sensor Array Based on Fuzzy C-Means Clustering

In the real world, boundaries between many objective things are often fuzzy. When classifying things, they accompanied by ambiguity, which leads to fuzzy cluster analysis. The most typical in clustering analysis is C-means algorithm. algorithm obtains membership degree of each sample point all class centers optimizing function, so as determine category achieve purpose automatically data. Based on clustering, this paper analyzes image segmentation chroma sensor array. (FCM) for colorimetric array an unsupervised and recalibration process, suitable existence blur uncertainty images. However, has inherent defects; that is, it does not combine characteristics current diversity instability, consider spatial information pixels, only uses grayscale image, making effective noise. effect ideal. Therefore, proposes a new based clustering. Through test, proposed demonstrates overall optimal accuracy 96.62% segmentation, can effectively accurately target extraction