基于前景分割的自阴影去除算法

Self Shadow Removal Algorithm Based on Foreground Segmentation

从视频序列中识别运动物体是计算机视觉中一项基本且重要的任务,也是从静态背景中有效地分割运动物体的一项基本要求。在视频序列中,由于阴影强度的不同以及背景梯度的变化,分割出的前景物体一般都包含自阴影。而且,自阴影在自然界中一般都是模糊且无明显边界的。在视频序列中,为了消除此类阴影,本文提出一种基于平均统计差异推论的Z检验算法。这种统计模型能处理没有光源和表面方向数目限制的复杂、实时场景。实验结果表明,该算法能有效地从分割帧中检测出自阴影。

Removing identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications and a robust segmentation of motion objects from the static background is generally required. Segmented foreground objects generally include their self shadows as foreground objects since the shadow intensity differs and gradually changes from the background in a video sequence. Moreover, self shadows are vague in nature and have no clear boundaries. To eliminate such shadows from motion segmented video sequences, the paper proposes an algorithm based on inferential statistical difference in Mean （Z） method. This statistical model can deal scenes with complex and time varying illuminations without restrictions on the number of light sources and surface orientations. Results show that the algorithm can effectively and robustly detect associated self shadows from segmented frames.