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首页> 外文期刊>Journal of visual communication & image representation >Constellational contour parsing for deformable object detection
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Constellational contour parsing for deformable object detection

机译：星座轮廓解析用于可变形物体检测

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In this paper we propose a novel framework for contour-based object detection from cluttered environments. Given a contour model for a class of object, it is first decomposed into fragments, then in the test image we simultaneously perform selection of relevant contour fragments in edge images, grouping of the selected contour fragments, and finding best geometry-preserving matching to model contours. Finding the best matching is inherently a computationally expensive problem. To address this challenge, we developed local shape descriptors and an additive similarity metric function which can be computed locally while preserving the capability of matching deformable shapes globally. This allows us to establish a constellational shape parsing framework using low-complexity dynamic programming to find optimal configuration of contour segments in test images to match the model contour. To effectively detect objects with large deformation, we augmented the metric function with a local motion search, modeled the relationship between different shape parts using multiple concurrent dynamic programming shape parsers. Our experimental results show that the proposed method outperforms the state-of-the-art contour-based object detection algorithms on two benchmark datasets in terms of average precision. (C) 2016 Elsevier Inc. All rights reserved.
机译：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，然后在测试图像中，我们同时在边缘图像中执行相关轮廓片段的选择，所选轮廓片段的分组，并找到与模型保持最佳几何形状的匹配轮廓。寻找最佳匹配本质上是一个计算量大的问题。为了解决这一挑战，我们开发了局部形状描述符和可加性相似性度量函数，可以在本地计算这些结果，同时保留全局匹配可变形形状的能力。这使我们能够使用低复杂度动态编程来建立星座形状解析框架，以在测试图像中找到轮廓线段的最佳配置以匹配模型轮廓线。为了有效地检测具有较大变形的对象，我们使用局部运动搜索增强了度量功能，并使用多个并发动态编程形状解析器对不同形状零件之间的关系进行了建模。我们的实验结果表明，在平均精度方面，该方法在两个基准数据集上优于最新的基于轮廓的目标检测算法。 （C）2016 Elsevier Inc.保留所有权利。

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