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Ping Zhong; Runsheng Wang;
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Volume: 3
Digital Object Identifier: 10.1109/ICPR.2006.876
Publication Year: 2006 , Page(s): 160 - 163
Cited by: 1
百优得主钟平老师的处女作:结合CRF与MRF做城市区域检测。
一、流程图
二、特征提取
三类特征:线长统计(见上一博文)、梯度幅值、梯度方向
1、梯度幅值
采用梯度幅值的均值、p阶矩、熵、、能量作intrascale特征,作尺度间特征
2、梯度方向
依据:If the window Wc contains a smooth patch, the gradients will be very small and the mean of the histogram over all the bins will also be small. On the other hand, if Wc contains a textured region, the histogram will have approximately uniformly distributed bin magnitudes. Finally, if Wc contains many straight lines embedded in the urban area, a few bins will have significant peaks in the histogram.如果窗口是光滑区域,则梯度的均值和方差都很小,若为纹理区域则呈近均匀分布,而城市区域则会出现显著的峰值。
三、参数估计与推断
首先采用CRF对精确目标建模并获得初始检测结果,然后估计参数,最后进行推断。这一部分看不懂,先记下能看懂的,以后再补充理解。
1、参数估计
CRF参数即为特征向量各分量的权值,采用最大似然法进行估计;
MRF参数采用EM算法进行估计
2、推断
在CRF中采用Belief Propagation(BP)算法产生近似MAP标记;
在MRF中采用iterated Conditional Modes(ICM)方法在对象边界近似估计MAP
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