In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.

中文翻译：

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使用零件包进行基于轮廓的对象检测。

在本文中，我们提出了一种用于从杂乱环境中进行基于轮廓的目标检测的新颖框架。给定一类对象的轮廓模型，首先将其分解为片段。然后，我们将这些片段分组为零件束，其中零件束可以包含重叠的片段。给定带有边缘片段集的新图像，我们使用零件束和边缘片段之间的局部形状相似性来开发一种有效的投票方法，该方法可以生成高质量的候选零件配置。然后，我们使用零件配置和模型轮廓之间的全局形状相似性来找到最佳配置。此外，我们表明，当模型轮廓不能充分捕获对象的变形时，外观信息可用于改善具有独特纹理的对象的检测。