基于视图的识别中部分遮挡的特定对象分割

Minsu Cho, Kyoung Mu Lee
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引用次数: 4

摘要

提出了一种新的目标分割方法,可用于基于视图的目标识别系统。以往的目标分割方法由于其自上而下的策略无法解释各种特定对象的细节,导致结果不精确,特别是在部分遮挡和混乱的环境中。相反,我们的分割方法有效地利用了基于视图的识别中匹配模型视图的信息,因为与输入图像对齐的模型视图可以作为最佳的自上而下的对象分割线索。在本文中,我们将部分遮挡的目标分割问题视为对齐模型视图和输入图像之间的每个像素同时标记位移和前景状态的问题。该问题是由一个极大后验马尔可夫随机场(MAP-MRF)模型来表达的,该模型最小化了一个特定的能量函数。该方法克服了复杂的遮挡和杂波,结合自下而上的分割线索提供了准确的分割边界。通过对遮挡和杂乱环境下各种目标的实验结果,验证了该方法的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Partially Occluded Object-Specific Segmentation in View-Based Recognition
We present a novel object-specific segmentation method which can be used in view-based object recognition systems. Previous object segmentation approaches generate inexact results especially in partially occluded and cluttered environment because their top-down strategies fail to explain the details of various specific objects. On the contrary, our segmentation method efficiently exploits the information of the matched model views in view-based recognition because the aligned model view to the input image can serve as the best top-down cue for object segmentation. In this paper, we cast the problem of partially occluded object segmentation as that of labelling displacement and foreground status simultaneously for each pixel between the aligned model view and an input image. The problem is formulated by a maximum a posteriori Markov random field (MAP-MRF) model which minimizes a particular energy function. Our method overcomes complex occlusion and clutter and provides accurate segmentation boundaries by combining a bottom-up segmentation cue together. We demonstrate the efficiency and robustness of it by experimental results on various objects under occluded and cluttered environments.
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