曾祥进, 黄心汉, 吴倩, 王敏. 马尔可夫随机场在显微图像散焦深度信息估计中的应用[J]. 机器人, 2008, 30(5): 416-420.
引用本文: 曾祥进, 黄心汉, 吴倩, 王敏. 马尔可夫随机场在显微图像散焦深度信息估计中的应用[J]. 机器人, 2008, 30(5): 416-420.
ZENG Xiang-jin, HUANG Xin-han, WU Qian, WANG Min. Application of MRF to Depth Information Estimation of Micro Image Defocus[J]. ROBOT, 2008, 30(5): 416-420.
Citation: ZENG Xiang-jin, HUANG Xin-han, WU Qian, WANG Min. Application of MRF to Depth Information Estimation of Micro Image Defocus[J]. ROBOT, 2008, 30(5): 416-420.

马尔可夫随机场在显微图像散焦深度信息估计中的应用

Application of MRF to Depth Information Estimation of Micro Image Defocus

  • 摘要: 针对显微视觉图像深度信息估计问题,提出了一种基于马尔可夫随机场的散焦特征参数模型:该模型将散焦特征深度信息的估计转化为能量函数的优化问题.应用迭代条件模式(Iterated Conditional Mode,ICM)算法进行优化,在ICM算法中应用最小二乘估计(LSE)算法对初始点参数进行估计,从而改进了ICM算法的性能,防止了其进入局部最优解.实验与仿真证实了该模型和算法的有效性和可行性.

     

    Abstract: For the problem of depth information estimation of micro vision image,a defocus characteristic parameter model based on Markov random field(MRF)is presented,which converts the depth information estimation problem of defocus characteristics into the optimization problem of energy function.With iterated conditional mode(ICM)algorithm applied to fulfill optimization and least squares estimate(LSE)algorithm employed to estimate the parameters of initial point,performance of the ICM algorithm is improved and the result of the algorithm is prevented from getting into local optimum.The experiments and simulations prove that the model and algorithm are effective and feasible.

     

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