ACTIVE SHAPE MODELS - THEIR TRAINING AND APPLICATION
ACTIVE SHAPE MODELS - THEIR TRAINING AND APPLICATION
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DOI：
10.1006/cviu.1995.1004
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发表时间：
1995-01-01
影响因子：
4.5
通讯作者：
GRAHAM, J
中科院分区：
文献类型：
--
作者：
COOTES, TF;TAYLOR, CJ;GRAHAM, J
基于模型的视觉是一种在存在噪声、杂波和遮挡的情况下识别和定位已知刚性物体的稳健方法。尽管已经提出了许多基于灵活模板的方法，但将基于模型的方法应用于外观可以变化的对象图像的问题更多。现有方法的问题在于，为了适应可变性，它们牺牲了模型的特异性，从而损害了图像解释过程中的鲁棒性。我们认为，一个模型应该只能以它所代表的对象类的特征方式变形。我们描述了一种通过从正确注释的图像的训练集中学习可变性模式来构建模型的方法。这些模型可以用于图像搜索的迭代细化算法类似于所采用的主动轮廓模型（蛇）。关键的区别在于，我们的主动形状模型只能以与训练集一致的方式变形以拟合数据。我们展示了几个实际的例子，在这些例子中，我们建立了这样的模型，并使用它们在嘈杂、混乱的图像中定位部分遮挡的物体。(C) 1995学术出版社，Inc。
Model-based vision is firmly established as a robust approach to recognizing and locating known rigid objects in the presence of noise, clutter, and occlusion. It is more problematic to apply model-based methods to images of objects whose appearance can vary, though a number of approaches based on the use of flexible templates have been proposed. The problem with existing methods is that they sacrifice model specificity in order to accommodate variability, thereby compromising robustness during image interpretation. We argue that a model should only be able to deform in ways characteristic of the class of objects it represents. We describe a method for building models by learning patterns of variability from a training set of correctly annotated images. These models can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes). The key difference is that our Active Shape Models can only deform to fit the data in ways consistent with the training set. We show several practical examples where we have built such models and used them to locate partially occluded objects in noisy, cluttered images. (C) 1995 Academic Press, Inc.