Low efficiency

low-segmentation precision

and invalid blurry-edge segmentation are due to disregarding fuzzy image features when combining region-merging and graph-cut (GC) algorithms. To solve these problems

we propose a method that uses information of maximum fuzzy two-partition entropy. The information is acquired by recursive computation to design the likelihood item of energy function in GC

in which the model is built based on the region as vertex. First

bilateral filter and watershed algorithm are used to pre-process the image to oversegment the input image into small regions. Second

based on maximum fuzzy 2-partition entropy of rock particle

corresponding membership functions can be used for setting the GC likelihood item. This way

more real energy functions can be acquired. Meanwhile

to improve the efficiency search of maximum fuzzy 2-partition entropy

a recursive algorithm with time complexity O() is presented to convert the fuzzy entropy computation to a recursive process; and non-repetitive results of processing moments are stored for the succeeding exhaustive optimization. Finally

designed-region merging and GC are used to assign region labels and complete segmentation. Experimental results indicate that the segmentation precision of the proposed algorithm improves by about 23%

and running time is 60% shorter than those of compared region-merging and GC algorithms. Relative error of our statistical results is 2%

with respect to those of artificial statistical results. Our proposed method can improve segmentation efficiency while ensuring segmentation precision. The results provide an important reference for engineering practice of automatic and efficient rock particle segmentation.