With shadow elimination towards effective foreground extraction

Stefan Badura, A. Lieskovsky, M. Mokrys
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引用次数: 1

Abstract

Very important process in the area - image processing, is the task of foreground extraction. To learn a computer to distinguish between foreground objects and background scene is non-trivial. Further, if foreground is extracted, a question can be set: is the foreground credible? What if significant part of detected foreground is unimportant information? Shadow is in most cases as noise considered. Our goal is to analyze possibilities shadow elimination and next to provide improved inputs of detected foreground for more efficient traffic surveillance system.
通过阴影消除来有效提取前景
前景提取是区域图像处理中一个非常重要的过程。学习计算机区分前景物体和背景场景是非常重要的。进一步,如果提取前景,可以设置一个问题:前景可信吗?如果检测到的前景的重要部分是不重要的信息怎么办?阴影在大多数情况下被认为是噪声。我们的目标是分析阴影消除的可能性,然后为更有效的交通监控系统提供改进的检测前景输入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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