Fast Recursive Two-dimensional Maximum between-cluster Average Deviation Thresholding Algorithms
2维最大类间平均离差阈值选取快速递推算法

Thresholding is one of the widely used and efficient techniques for image segmentation in digital image processing. Threshold selection is crucial to thresholding. The maximum between-cluster variance algorithm based on L2-Norm, which was proposed by Otsu, is one of the most famous methods. And the maximum between-cluster average deviation thresholding algorithm based on L1 -Norm is simpler and has good performance. The two-dimensional maximum between-cluster average deviation thresholding algorithm, which has high accuracy of segmentation and good resistance to noise, has better performance than the maximum between-cluster variance algorithm, but the two-dimensional algorithm requires a large amount of computation and is impractical in applications. In this paper, two fast recursive two-dimensional maximum between-cluster average deviation thresholding algorithms are proposed, whose computational complexities are only O(L2), while the computational complexity of the original1 algorithm is O(L4) . Using those two recursive algorithms, the results and processing time of the two-dimensional maximum between-cluster average deviation thresholding algorithm are given, which are compared with the original algorithm. Experimental results show that both of those two recursive algorithms can greatly reduce the processing time, which is only 0.1% of that of the original algorithm. Currently the proposed algorithms have been used in automatic infrared target,vehicle license plate and fingerprint recognition system.