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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Physically-based active shape models: Initialization and optimization
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Physically-based active shape models: Initialization and optimization

机译:基于物理的主动形状​​模型:初始化和优化

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摘要

In this paper we describe a new approach for 2-D object segmentation using an automatic method applied on images with problems as partial information, overlapping objects, many objects in a single scene, severe noise conditions and locating objects with a very high degree of deformation. We use a physically-based shape model to obtain a deformable template, which is defined on a canonical orthogonal coordinate system. The proposed methodology works starting from the output of an edge detector, which is processed to automatically obtain an approximation of the shape. The final estimation of the shapes is obtained fitting a deformable template model, which is defined on a learned surface of deformation. Results from biological images are presented. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 30]
机译:在本文中,我们描述了一种使用自动方法对图像进行二维分割的新方法,该方法适用于存在以下问题的图像:部分信息,重叠的对象,单个场景中的许多对象,严重的噪声条件以及定位变形程度非常高的对象。我们使用基于物理的形状模型来获取可变形模板,该模板在规范的正交坐标系上定义。所提出的方法从边缘检测器的输出开始,对边缘检测器的输出进行处理以自动获得形状的近似值。使用可变形模板模型获得形状的最终估计,该模板模型是在学习的变形表面上定义的。呈现了生物学图像的结果。 (C)1998模式识别学会。由Elsevier Science Ltd.出版。保留所有权利。 [参考:30]

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