Topological-Stabilization Based Threshold Quantization for Robust Change Detection

Chang Su, A. Amer
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Abstract

A threshold quantization algorithm for robust change detection is proposed in this paper. According to the threshold distribution of difference frames, a 4-level Lloyd-Max quantizer is designed, and then, based on the topological stabilization of video frames, the Lloyd-Max quantizer is refined by a linear adjusting function to form the proposed threshold quantizer. Objective and subjective experiments show that the proposed quantizer greatly improves the robustness of the thresholding methods for change detection thus significantly improves the quality of change masks without increasing computation loads.
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基于拓扑稳定的阈值量化鲁棒变化检测
提出了一种用于鲁棒变化检测的阈值量化算法。根据差分帧的阈值分布,设计了一个4级Lloyd-Max量化器,然后基于视频帧的拓扑稳定性,通过线性调节函数对Lloyd-Max量化器进行细化,形成所提出的阈值量化器。客观和主观实验表明,该量化器在不增加计算量的情况下,大大提高了阈值检测方法的鲁棒性,从而显著提高了变化掩码的质量。
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