首页> 外文会议>9th ACM/IEEE international conference on information processing in sensor networks 2010 >Poster Abstract: Image Sensing under Unfavorable Photographic Conditions with a Group of Wireless Image Sensors
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Poster Abstract: Image Sensing under Unfavorable Photographic Conditions with a Group of Wireless Image Sensors

机译:海报摘要:在不利的摄影条件下使用一组无线图像传感器进行图像感应

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摘要

We investigate the problem of image sensing under unfavorable photographic conditions in a wireless image sensor network. In the scenes with deflective and/or reflective medium such as fogs, mirrors, glasses, degraded images are captured by those image sensors. Such degraded images often lack perceptual vividness and they offer a poor visibility of the scene contents. Notably, computation-intensive method to recover a better image based on single image [2] may not be applicable for wireless image sensors due to the limited computation capacities and the limited power resources (batteries) typically equipped at those wireless image sensors. In this paper, we propose a framework to recover better images under unfavorable photographic conditions in a wireless image sensor network, where an efficient decision fusion approach based on the reinforcement learning technique to infer the presence of an unfavorable photographic condition is achieved among image sensors on the fly and a subsequent light-weighted computation method based on multiple images is employed to recover better images. The preliminary results show the effectiveness of the proposed framework.
机译:我们调查在无线图像传感器网络中不利的摄影条件下图像传感的问题。在具有偏转和/或反射介质(例如雾,镜子,眼镜)的场景中,这些图像传感器会捕获退化的图像。这样的降级图像通常缺乏感知生动性,并且它们对场景内容的可见性很差。值得注意的是,基于计算的方法来恢复基于单个图像的更好的图像[2]可能不适用于无线图像传感器,因为这些无线图像传感器通常配备了有限的计算能力和有限的电源(电池)。在本文中,我们提出了一个在不利图像条件下在无线图像传感器网络中恢复更好图像的框架,其中基于增强学习技术来推断存在不利图像条件的图像传感器之间的有效决策融合方法得以实现。飞行和随后的基于多个图像的轻量计算方法被用于恢复更好的图像。初步结果表明了该框架的有效性。

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