基于小波多尺度变换的前景分割与阴影消除

Ye-peng Guan
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引用次数: 29

摘要

提出了一种基于小波多尺度变换的前景运动目标分割和阴影抑制算法。阈值的最佳选择是自动确定的,不需要任何复杂的监督训练,人工校准或假设。该算法能够有效分割背景对比度较低的前景运动目标。无论物体是否在捕获前进入视场,都使用参考图像提取前景。该方法不涉及复杂的计算模型、颜色模型或背景统计,具有较高的计算成本。对比结果表明,在不同的室内和室外环境下,该方法在前景检测和阴影抑制方面具有更强的鲁棒性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wavelet Multi-Scale Transform Based Foreground Segmentation and Shadow Elimination
An algorithm using wavelet multi-scale transform for segmenting foreground moving objects and suppressing shadow is proposed. The optimal selection of threshold is automatically determined which does not require any complex supervised training, manual calibration or hypothesis. The proposed algorithm is efficient enough to segment foreground moving objects with low contrast against the background. The reference image is used to extract foreground no matter the objects enter the field of view before captured or not. The developed method is highly computationally cost-effective since it does not concern with complex computation model, color model or background statistics at a time. By compari- sons, it has been shown that the proposed approach is more robust and efficient to detect foreground and suppress shadow during coping with different indoors or outdoors circumstances.
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