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首页> 外文期刊>Image Processing, IET >Shadow identification for digital imagery using colour and texture cues
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Shadow identification for digital imagery using colour and texture cues

机译：使用颜色和纹理提示对数字图像进行阴影识别

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摘要

Shadows cause a significant problem for automated systems which attempt to understand scenes, since shadow boundaries may be incorrectly recognised as a material change, and incorrectly recognised as an object. Shadow identification is therefore an important pre-processing step for applications such as shadow removal, shadow invariant object recognition and shadow invariant object tracking. Many existing shadow identification methods are often limited by the types of shadow boundaries (penumbra) which can be found, by the density (darkness) of the shadows and by the type of surface texture on which the shadows are cast. In addition many of these methods are limited to a specific type of scene, while others result in high levels of false positive (FP) shadow identification. To address these problems, a novel algorithm for automatic shadow identification is proposed, which makes use of a new tree-structured segmentation algorithm for candidate shadow region identification, as well as a combination of colour illumination invariance and texture analysis for shadow verification. The method is tested on a number of indoor and outdoor images exhibiting different types of shadows, surfaces and illumination sources. These results indicate that the proposed method performs well against the state of the art; in particular, the rate of FP identification is reduced from 26 to below 13% when compared with using illumination invariance alone.
机译：对于试图理解场景的自动化系统，阴影会引起严重的问题，因为阴影边界可能被错误地识别为物质变化，而错误地被识别为物体。因此，阴影识别对于诸如阴影去除，阴影不变对象识别和阴影不变对象跟踪之类的应用而言，是重要的预处理步骤。许多现有的阴影识别方法经常受到可以找到的阴影边界（半影）的类型，阴影的密度（暗度）以及投射阴影的表面纹理类型的限制。此外，这些方法中的许多方法仅限于特定类型的场景，而其他方法则导致高水平的假阳性（FP）阴影识别。为了解决这些问题，提出了一种新颖的阴影自动识别算法，该算法利用一种新的树结构分割算法进行候选阴影区域识别，并结合了颜色照度不变性和纹理分析来进行阴影验证。在显示不同类型的阴影，表面和照明源的大量室内和室外图像上测试了该方法。这些结果表明，所提出的方法相对于现有技术表现良好。特别是，与仅使用照明不变性相比，FP识别率从26％降至13％以下。

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