目的 针对现有大多数阴影检测算法在检测细长阴影、自阴影、区分阴影与暗色像素等方面的不足,提出一种新的结合区域配对的阴影检测算法.方法 首先通过均值漂移算法和canny检测算法,分割图像得到每个独立的区域;然后从每个区域中提取纹理和亮度建立单个区域的阴影模型,再从区域对中提取纹理直方图的距离、颜色比(分别在RGB和Lab空间下)以及HSI空间下H和I两通道的比值等特征建立区域对的阴影模型;最后根据上述两个模型运用图割理论检测阴影.结果 实验结果表明,本文算法在阴影检测上的准确率高达85.2%,远高于其他算法,检测速度也比其他算法快34%左右.该算法不仅能有效地检测细长阴影和自阴影,还能较好地区分阴影与暗色像素.结论 提出了一种新的阴影检测算法,通过区域配对的方法实时处理单幅室外图像.实验结果表明,该算法在检测细长阴影、自阴影以及区分阴影与暗色像素等方面有良好的效果.

Objective Shadows usually degrade image quality and cause undesirable problems. Hence, shadow detection is a fundamental step in computer vision and image analysis, including such processes as image segmentation, object recognition, stereo registration, and scene analysis. For a single image, shadow detection is particularly challenging because of limited information. Most shadow detection algorithms have difficulty in detecting lathy shadows and self-shadows,as well as in distinguishing between shadows and dark pixels. To address these problems, a novel algorithm with pairwise regions for shadow detection is proposed in this study. Method Unlike traditional algorithms that explore pixel or edge information, the proposed algorithm involves the training of two models with support vector machine to learn shadow features and to classify shadow and nonshadow regions. Our algorithm has two stages: offline learning and online detecting. In the offline stage, the image is first segmented,after which every single regionis obtained by using the mean shift and canny detection algorithms. Support vector machine is then employed to construct a single region shadow model with the use of the texture and intensity features in each region. A pairwise region shadow model is finally constructed after manually marking pairwise regions of shadow and nonshadow with the distances of texture histograms, color ratios(in RGB color space and lab color space), and the ratios of H channel to I channel in HSI color space. In the online stage, the same segmentation manipulation as that in the prior stage is performed for the input image. Thereafter, the features of the single and pairwise region models are extracted and integrated into the corresponding model to obtain the classification results separately. Finally, a graph is built using the two models, and the graph-cut algorithm is employed to label shadow and nonshadow regions. The following are the advantages of our method: 1) We consider both pixels and edges to achieve accurate segmentation, particularly for long and thin shadows; and 2) Except for common shadow features, we employ the ratios of H channel to I channel in HSI color space to detect self-shadows and to remove dark pixels from shadows. Result Visual experimental results show that our algorithm not only detects spindly shadows and self-shadows effectively but also separates shadows from dark pixels correctly. In terms of confusion matrix in shadow detection, our algorithm achieves an 85.2% performance versus 70.1% for the algorithm reported by Guo et al. and 60.2% for the algorithm by Tian et al. In addition, our algorithm runs 34% faster than that of Guo et al. under the same situation because of the use of a simple feature set. Conclusion Shadow detection algorithms based on regions are commonly used for outdoor image processing. However, few algorithms can detect some special shadows, such as threadlike shadows and self-shadows, or to distinguish shadows from dark pixels. In this study, a new algorithm is presented to solve the problems arising from a single outdoor image by using paired regions. Experiment results indicate that our algorithm has satisfactory performance in detecting spindly shadows and self-shadows, as well as in distinguishing shadows from dark pixels.