Improved Two-dimensional Maximum Entropy Image Thresholding and its Fast Recursive Realization
改进的最大嫡闭值分割及其快速实现

The traditional two-dimensional(2-D) maximum entropy(ME) thresholding method has not good segmentation performance mainly owing to approximately processing. So a fast and improved 2-D ME image thresholding method was presented in this paper. Firstly, a 2-D histogram with the improved neighborhood mask was given and the ME method was used on the 2-D histogram to get a more ideal threshold. Then, some values of objects area and background area in the 2-D histogram main-diagonal district in the ME method were calculated precisely to obtain better segmentation performance. Finally, a 2-D histogram was analyzed to get its features and two theorems were proved, and the fcalures and the theorems were employed to infer a new recursive approach to search the best threshold vector to reduce the computational complexity. Experimental results show that the proposed method not only achieves more accurate segmentation results and more robust anti-noise, but also requires much less memory space and its running time is much less, around 0. 04 second, compared to the current 2-D ME thresholding methods.