The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects
The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects
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DOI：
10.1109/cvpr.2006.305
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
J. Winn;J. Shotton
中科院分区：
文献类型：
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作者：
J. Winn;J. Shotton
本文讨论的问题，检测和分割部分被遮挡的对象的一个已知的类别。我们首先定义一个部分标签，它密集地覆盖了对象。然后，我们的布局一致随机场（LayoutCRF）模型对这些标签施加不对称的局部空间约束，以确保部件的一致布局，同时允许对象变形。通过避免假设整个对象可见来处理对象的任意遮挡。由此产生的系统是有效的训练和应用到新的图像，由于一个新的退火布局一致的扩展移动算法与随机决策树分类器配对。我们将我们的技术应用于图像的汽车和人脸，并展示了国家的最先进的检测和分割性能，即使在存在部分遮挡。
This paper addresses the problem of detecting and segmenting partially occluded objects of a known category. We first define a part labelling which densely covers the object. Our Layout Consistent Random Field (LayoutCRF) model then imposes asymmetric local spatial constraints on these labels to ensure the consistent layout of parts whilst allowing for object deformation. Arbitrary occlusions of the object are handled by avoiding the assumption that the whole object is visible. The resulting system is both efficient to train and to apply to novel images, due to a novel annealed layout-consistent expansion move algorithm paired with a randomised decision tree classifier. We apply our technique to images of cars and faces and demonstrate state-of-the-art detection and segmentation performance even in the presence of partial occlusion.