Small Probability Strategy Based Otsu Thresholding Method for Image Segmentation

Otsu adaptive threshold algorithm is a classic image segmentation method. The two-dimensional Otsu algorithm and its improvements which based on original Otsu algorithm are restricted, due to their computation(or space) complexity, inability for anti-noise, difficulty to extend to multilevel thresholding. In order to improve these shortages, regarding noise points appearances as small probability events, noise point is changed to objective(or background) pixel by using its neighborhood average gray level to instead its gray level. Then the processed image is segmented through one-dimensional Otsu. So this method obtain good performance at low cost. The experimental result shows that this method has significant improvements in complexity, ability for anti-noise, ability for extending to multilevel thresholding and so on.