Aiming at poor robustness of the threshold auto-selection algorithm in far-infrared images segmentation, an improved K-means clustering centers analysis algorithm based on the mechanism of far-infrared imaging is researched in this paper. According to the character that the cluster centers had a linear distribution before clustering and had a clear turning point after clustering when they belongs to different categories, the absolute difference between the practical cluster centers value and theoretical cluster centers predicting value of a category under test is taken as the measurement function to select the turning point, thus the threshold for image segmentation was determined. Experimental result shows good robustness and anti-noise performance of the algorithm.
infrared image segmentation,
K-means clustering centers analysis,
turning point selection,