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首页> 外文会议>IFIP(International Federation for Information Processing) TC 12 International Conference on Computer and Computing Technologies in Agriculture(CCTA 2007); 20070818-20; Wuyishan(CN) >ELIMINATING CROP SHADOWS IN VIDEO SEQUENCES BY PROBABLE LEARNING PIXEL CLASSIFICATION
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ELIMINATING CROP SHADOWS IN VIDEO SEQUENCES BY PROBABLE LEARNING PIXEL CLASSIFICATION

机译：通过可能的学习像素分类消除视频序列中的作物阴影

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摘要

Shadows have been one of the most serious problems for vegetation segmetation, espescially under conditions of natural random airflow and human or vehicle disturbance. A video sequence processing method has developed in this paper to identify and eliminate crop shadows. The method comprises pixel models and algorithms explained in a probable learning framework. Expectation maximization (EM) for mixture models is established and an incremental EM method is proposed. This method performs a probable reasoning unsupervised classification of pixels for real-time implementation. The results show that the method is quite robust and can successfully remove shadows under natural lighting conditions.
机译：阴影一直是植被隔离最严重的问题之一，尤其是在自然随机气流和人为干扰或车辆干扰的情况下。本文开发了一种视频序列处理方法来识别和消除作物阴影。该方法包括在可能的学习框架中解释的像素模型和算法。建立了混合模型的期望最大化（EM）并提出了一种增量式EM方法。该方法为实时实现执行可能的推理无监督像素分类。结果表明，该方法非常鲁棒，可以在自然光照条件下成功去除阴影。

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