School of Intelligent Manufacturing, Weifang University of Science and Technology, Shandong, Weifang 261000, China.
Department of Information and Communication Engineering, Hoseo University, Chungcheongnam-do, Asan, 31499, Republic of Korea.
Comput Intell Neurosci. 2022 Apr 20;2022:7261551. doi: 10.1155/2022/7261551. eCollection 2022.
Shadow detection and removal play an important role in the field of computer vision and pattern recognition. Shadow will cause some loss and interference to the information of moving objects, resulting in the performance degradation of subsequent computer vision tasks such as moving object detection or image segmentation. In this paper, each image is regarded as a small sample, and then a method based on material matching of intelligent computing between image regions is proposed to detect and remove image shadows. In shadow detection, the proposed method can be directly used for detection without training and ensures the consistency of similar regions to a certain extent. In shadow removal, the proposed method can minimize the influence of shadow removal operation on other features in the shadow region. The experiments on the benchmark dataset demonstrate that the proposed approach achieves a promising performance, and its improvement is more than 6% in comparison with several advanced shadow detection methods.
阴影检测和去除在计算机视觉和模式识别领域中起着重要作用。阴影会对运动物体的信息造成一些损失和干扰，从而导致后续计算机视觉任务（如运动物体检测或图像分割）的性能下降。在本文中，每个图像都被视为一个小样本，然后提出了一种基于图像区域智能计算材料匹配的方法来检测和去除图像阴影。在阴影检测中，所提出的方法可以直接用于检测而无需训练，并在一定程度上保证了相似区域的一致性。在阴影去除中，所提出的方法可以将阴影去除操作对阴影区域中其他特征的影响最小化。在基准数据集上的实验表明，所提出的方法具有良好的性能，与几种先进的阴影检测方法相比，其改进超过 6%。
