学习实时场景上下文的文本模型

A. Flint, I. Reid, D. W. Murray
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引用次数: 5

摘要

提出了一种基于图像内文本分布的场景上下文模型。我们的方法在整个视频序列中提供连续,一致的场景要点,适用于摄像机定期查看场景中无信息部分的应用。我们表明,我们的模型在位置识别方面优于最先进的技术。我们进一步展示了如何从场景要点推断相机方向,最后展示了我们的系统如何应用于活动对象搜索。
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Learning texton models for real-time scene context
We present a new model for scene context based on the distribution of textons within images. Our approach provides continuous, consistent scene gist throughout a video sequence and is suitable for applications in which the camera regularly views uninformative parts of the scene. We show that our model outperforms the state-of-the-art for place recognition. We further show how to deduce the camera orientation from our scene gist and finally show how our system can be applied to active object search.
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