A New Image Segmentation Algorithm and It’s Application in lettuce object segmentation

Jun Sun, Yan Wang, Xiaohong Wu, Xiaodong Zhang, Hongyan Gao,

Indonesian Journal of Electrical Engineering and Computer Science, Vol 10, No 3: July 2012 , pp. 557-563

Abstract

Lettuce image segmentation which based on computer image processing is the premise of non-destructive testing of lettuce quality. The traditional 2-D maximum entropy algorithm has some faults, such as low accuracy of segmentation, slow speed, and poor anti-noise ability. As a result, it leads to the problems of poor image segmentation and low efficiency. An improved 2-D maximum entropy algorithm is presented in this paper. It redistricts segmented regions and furtherly classifies the segmented image pixels with the method of the minimum fuzzy entropy, and reduces the impact of noise points, as a result the image segmentation accuracy is improved. The improved algorithm is used to lettuce object segmentation, and the experimental results show that the improved segmentation algorithm has many advantages compared with the traditional 2-D maximum entropy algorithm, such as less false interference, strong anti-noise ability, good robustness and validity. DOI: http://dx.doi.org/10.11591/telkomnika.v10i3.618

Image segmentation; 2 - D maximum entropy; Lettuce quality; Minimum fuzzy entropy