Research of image segmentation based on 2-D maximum entropy optimal threshold
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摘要: 为了提高图像分割的质量,采用2维最大熵最佳阈值方法,首先通过灰度区域确定该域像素的2维随机向量,在准则函数下求得到2维最大熵最佳阈值;接着通过递推优化对2维最大熵最佳阈值计算数据优化处理,减少重复性数据计算量;最后通过分割图像区域与原目标空间位置的互信息量最大准则,把误分割误差函数作为检测分割标准,给出了算法流程;并仿真出了不同算法的图像分割结果。结果表明,该算法得到图像分割的精度较高,没有背景与噪声的残留,保留了图像信息,执行速度快、分割效果视觉好、误分割误差最小。这对提升图像分割效率是有帮助的。Abstract: In order to improve the quality of image segmentation, two-dimensional maximum entropy optimal threshold (TDMEOT) method was used. Firstly, 2-D random vector of the domain pixels was defined through the gray region and TDMEOT value was gotten by the criterion function. Secondly, calculation data of 2-D maximum entropy threshold were optimized through the recursive optimization and the repetitive data calculation was reduced. Finally, based on the maximum mutual information criterion between the segmentation image area and the target space position and choosing error segmentation function as the segmentation standard, the algorithm flow and the image segmentation results of different algorithms were given after experimental simulation. The results show that this method has higher precision of image segmentation and has no residual background noise, and retains the image information with fast speed, good segmentation visual and minimum segmentation error. The research is helpful to improve the efficiency of image segmentation.
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Keywords:
- image processing /
- 2-D maximum entropy /
- optimal threshold /
- pixel
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