Pictorial structures for object recognition
Pictorial structures for object recognition
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DOI：
10.1023/b:visi.0000042934.15159.49
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发表时间：
2005-01-01
影响因子：
19.5
通讯作者：
Huttenlocher, DP
中科院分区：
文献类型：
--
作者：
Felzenszwalb, PF;Huttenlocher, DP
在本文中，我们提出了一个计算效率的框架，基于部分的建模和识别的对象。我们的工作是由Fischler和Elschlager介绍的图形结构模型的动机。其基本思想是通过以可变形配置排列的部件的集合来表示对象。每个零件的外观是单独建模的，可变形的配置是由成对零件之间的弹簧状连接表示。这些模型允许的视觉外观的定性描述，并适用于通用的识别问题。我们解决的问题，使用图像结构模型，以找到一个对象在图像中的实例，以及学习的对象模型从训练的例子，在这两种情况下，提出有效的算法的问题。我们通过学习代表人脸和人体的模型，并使用所产生的模型来定位新图像中的相应对象来演示这些技术。
In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images.