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.