Shadow Detection Algorithm Based on Fuzzy Markov Random Fields

BAI Kejia; LIU Weiming

doi:10.11834/jig.20100309

Views : 0 下载量: 364 CSCD: 0

PDF
Export
Share
Collection
Album

Shadow Detection Algorithm Based on Fuzzy Markov Random Fields
Vol. 15, Issue 3, Pages: 409(2010)
Published：2010
DOI： 10.11834/jig.20100309
稿件说明：

移动端阅览

DOI：

BAI Kejia, LIU Weiming. Shadow Detection Algorithm Based on Fuzzy Markov Random Fields[J]. Journal of Image and Graphics, 2010, 15(3): 409. DOI： 10.11834/jig.20100309.

摘要

阴影的检测是目标检测、目标跟踪、视频监控等领域的一个关键问题。提出了一种基于模糊马尔可夫随机场的阴影检测算法。该算法把阴影检测问题看做是一个求最优化的像素点分类问题。对于输入的视频，提取背景图像，找出阴影和前景目标物体区域。通过计算阴影概率分布，前景概率分布，隶属度函数，建立模糊马尔可夫随机场。应用贝叶斯准则，最大后验(MAP)估计和条件迭代模式(ICM)算法，寻找最优化的模糊马尔可夫随机场，并利用最大隶属度原则消除模糊性，得到阴影检测的结果。实验证明，文中算法具有较好的阴影检测率和目标检测率。

Abstract

Shadow detection is a key problem in object detection

object tracking and visual surveillance. In this paper

a new shadow detection algorithm is proposed based on fuzzy Markov random fields. The shadow detection problem is regarded as a problem of searching the optimal labeling of the total foreground pixels. The background abstract algorithm is used to find out the shadow and foreground pixels in the image. The fuzzy Markov random fields are created after the calculation of the shadow probabilities

the foreground probabilities and the membership functions. Bayesian principle

maximum a posteriori(MAP) estimation

iterated conditional mode(ICM) algorithm are used to search the optimal fuzzy Markov random field. The result of the shadow detection is obtained by defuzzifying the fuzzy Markov random field according to the maximum membership principle. Experimental results demonstrate the performance of the proposed algorithm.