基于SLIC0超像素分割的阴影检测算法

对单幅阴影检测问题，提出了一种基于SLIC0（simple linear iterative clustering zero）超像素分割的阴影检测方法。首先采用SLIC0超像素分割算法对含阴影图像进行分割，生成超像素块检测出阴影轮廓，然后提出一种融合特征的支持向量机方法，将超像素块分类合并，检测出阴影区域。通过实验对比Otsu阈值法、传统SVM分类法与本文算法的检测效果，验证了本文算法的有效性，通过结构相似度（SSIM）与峰值信噪比（PSNR）指标对比表明，本文算法较参考算法的检测性能更优。

A Shadow Detection Method Based on SLIC0 Superpixel Segmentation

This paper proposes a shadow detection algorithm based on SLIC0 superpixel segmentation to address the issues of single-image shadow detection. Firstly, SLIC0 superpixels is used to segment the shadow image to generate superpixel blocks to detect the shadow contour. Then proposes a fusion-characteristics-based SVM classifiers, the superpixel blocks are classified and merged to detect the shadow areas. Finally, the proposed algorithm is compared with Otsu threshold method and traditional SVM detection method. The experiment results verified the effectiveness of the proposed algorithm. The detection performances comparison of SSIM and PSNR indicates that the proposed algorithm obtains relative higher performances than the reference algorithms.