设计了一个先验密度惩罚图像当中分水线变换后的相似的区域，图像分割进而变成对目标子集的最大后验估计。
The segmentation problem is the maximizing a posteriori estimation of the set of object area result from the watershed labeled.
核密度估计方法从数据样本本身出发研究数据分布特征，不利用有关数据分布的先验知识，避免了模型估计和参数估计的主观影响。
The kernel estimation method analyzes the data distribution by not using the prior knowledge of data distribution. This method avoids the impaction of model and parameters estimation.
在目标识别级重点讨论了基于D - S证据理论的目标识别融合，通过性能分析可知该算法具有不需要先验概率和条件概率密度等优点。
In object identification level object identification fusion based on D-S proof theory was discussed, performance analyzing is found that the arithmetic did not need probability distribution.
应用推荐