使用阈值分割和自动阴影去除的前景对象提取

M. Singh, N. Nain, S. Panwar
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引用次数: 3

摘要

在计算机视觉、视觉监控或智能交通系统中,最具挑战性的任务之一是运动物体的提取或前景的提取。背景减法是使用单一固定阈值提取前景最直观的方法。一些技术利用了RGB强度的相关特性,但不能解决使用单一固定阈值的模糊性。此外,分割有副作用,如移动投射阴影和SelfShadow,两者都会降低准确性。本文提出了一种自动阴影去除方法来提取目标。并与其他前景提取、阴影检测和去除算法进行了分析比较,得出了更好的理解结果。该方法不需要任何阴影去除的前、后处理步骤,即可提取轮廓。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Foreground Object Extraction Using Thresholding with Automatic Shadow Removal
In Computer Vision, Visual Surveillance or in caseof Intelligent Transportation System, one of the most challengingtasks is the extraction of moving object or foreground extraction. Background Subtraction is the most intuitive method using singlefixed threshold for foreground extraction. Some techniques makeuse of correlated nature of RGB intensities, but can't resolvethe ambiguities of using a single fixed threshold. Moreover, segmentation has side effects like moving cast shadows and SelfShadow, both of which reduce accuracy. In this paper, we presentan automatic shadow removal method to extract objects. Theanalytical comparison with other foreground extraction, shadowdetection and removal algorithms and their results are alsopresented for better understanding. The proposed method extractsilhouette without any pre or post processing step of shadow removal.
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