基于成对区域的单幅图像阴影快速检测方法

Fast Shadow Detection Approach for Single Images Based on Paired Regions

传统基于成对区域的阴影检测方法在复杂纹理区域容易产生过分割现象,计算复杂度高,检测效果也受到了一定程度的影响。本文在原方法的基础上,提出利用聚类的方法对分割后的纹理区域重新进行合并,以减少过分割;同时根据成对分割区域的相关特性,构建支持向量机函数进行分类,以提高算法效率,改善阴影检测效果。仿真实验结果表明,与原方法相比,本文方法的检测效率大大提高,对复杂纹理图像的阴影检测结果也更加准确。

Since traditional shadow detection segmentation in complex texture regions, methods based on paired-regions are prone to over- they always have high computational complexity which affects the detection result to a certain extent. Therefore, an improved shadow detection algorithm is presented, using clustering method to merge divided areas and decrease over-seg- mentation. Besides, support vector machine （SVM） is established to classify the features of paired regions, so both algorithm efficiency and shadow detection effect are improved. Simula- tion results indicate that the running time of the proposed algorithm is remarkably reduced compared with the traditional one. Moreover, the shadow detection effect is more accurate than that of the original method for complicated texture images.