Statistical model for occluded object recognition
Statistical model for occluded object recognition
复制标题
遮挡物体识别的统计模型
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
David Castanon
中科院分区：
文献类型：
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作者：
Zhengrong Ying;David Castanon
在本文中，我们提出了一个基于模型的统计算法识别部分被遮挡的物体从嘈杂的功能。图像特征与模板特征的似然比用于识别。介绍了两种不同的统计遮挡模型：独立先验模型和马尔可夫随机场（MRF）先验模型。我们的实验表明，MRF模型比独立模型在部分遮挡的情况下表现得更鲁棒。
In this paper we present a model-based statistical algorithm for recognition of partially occluded objects from noisy features. The likelihood ratio of the image features to template features is used for recognition. Two different statistical occlusion models are introduced: an independent prior model and a Markov random field (MRF) prior model. Our experiments show that the MRF model performs more robustly than the independent model in the presence of partial occlusion.