【作者】 鹿瑞；

【导师】 须德；

【作者基本信息】 北京交通大学 ， 计算机软件与理论， 2010， 博士

【摘要】 颜色作为计算机视觉一个重要且有效的特征,已经被应用到计算机视觉的许多领域中。但是颜色特征很不鲁棒,容易受到场景光照的影响。颜色恒常性计算的目的就是要消除场景中光照的影响,得到物体表面真正的颜色特性,从而为计算机视觉系统提供一种对光照鲁棒的颜色特征。论文研究了单光照条件下和多光照条件下的颜色恒常性计算,主要开展了以下四个方面的工作：在由底层特征驱动的单光照估计研究方向上,由于目前的颜色恒常性算法大都利用图像的全部像素信息进行光照估计,然而实际上并不是所有的像素都包含有助于光照估计的信息。为此,论文提出了一种基于图像有效区域的颜色恒常性算法。该算法基于自然图像统计特征的相似性为图像选择一个适合的颜色恒常性算法及该算法对应的有效区域,进而在该区域上估计场景光照。实验结果表明该算法可以提高自然图像光照估计的准确度。在由底层特征驱动的单光照条件下候选颜色恒常性算法的融合或选择上,论文基于岭回归分别从光照估计结果的融合及最优算法的选择两个不同的角度开展研究,提出了两个基于岭回归的颜色恒常性算法。一个是基于岭回归获得各个候选颜色恒常性算法估计光照值和真实光照值之间的映射关系；另外一个是基于岭回归寻找自然图像的统计特征和其适合的颜色恒常性算法之间的映射关系。在高层视觉信息指导的单光照颜色恒常性计算的研究上,目前该方向上的研究还很少并且仅限于利用户外——室内场景类信息。为了克服这种场景分类的局限性,论文提出利用一个更为泛化的场景分类方法以提高光照估计的准确性。具体地说,论文提出了一种基于场景的三维几何结构的颜色恒常性算法。利用图像的空间布局信息指导颜色恒常性计算,而非简单的利用场景的二维平面信息。此外,该算法借助于场景的三维几何结构可以识别出近处光照和远处光照,从而为遥感图像的颜色恒常性计算研究提供了一个新的研究思路。在多光照颜色恒常性计算的研究上,采集了三个不同的数据集,即超光谱、实验室设置及自然场景下拍摄的多光照数据集。在此基础上提出了一个多光照条件下光照估计的计算框架。与现有的多光照颜色恒常性算法相比,该算法具有简单易行、不需要人工干涉、对成像设备无要求等优点。为了客观地评价算法的性能,提出了一个多光照条件下颜色恒常性计算性能评价标准。图45幅,表14个,参考文献121篇。更多还原

【Abstract】 Color is an important and effective feature, which has been widely used in computer vision. However, it is not quite robust and easily influenced by the color of the light source. Computational color constancy is to remove the effect of the color of the light source, get the original objects’color under canonical light source, in order to make the color feature much more robust to the color of the light sources. This dissertation focuses on computational color constancy for natural images under one or multiple illuminations. Our work is carried out from the following four aspects.For color constancy under uniform illumination, which driven by low-level image information, as not all pixels are helpful to illuminant estimation, a color constancy algorithm using effective regions is proposed. This algorithm makes use of natural image statistics to select the proper color constancy algorithm from the committee algorithms and corresponding effective regions for unseen images. Then estimate the color of the light source on the effective regions using the selected color constancy algorithm. This algorithm is proved to be able to improve accuracy of illuminant estimation.For the fusion or selection of color constancy algorithms under uniform illumination, which is driven by low-level image information, a simple but effective machine learning approach ridge regression is used in two ways:one is to get the contribution of color constancy algorithms’estimations to the final illuminant estimation; the other one is to get the relationship between color constancy algorithm selection and natural image statistics.For color constancy under uniform illumination, which is supervised by high-level visual information, there is little work on this direction. Now only indoor-outdoor information is made use of. To overcome such shortcoming, a much more generalized scene classification method is introduced. Specifically, a novel color constancy algorithm using 3D scene geometry is proposed. In this algorithm,3D scene geometry is used to determine which color constancy method to use for the different geometrical regions found in images. Our algorithm opens the possibility to estimate the remote scene illumination color, by distinguishing nearby light source from distant illuminations.For color constancy under multiple illuminations, first we collect three different datasets, i.e. hyper-spectral dataset, dataset under laboratory setting and real-world dataset; after that a novel color constancy algorithm framework for multiple light sources. Compared with the algorithms available, this algorithm is easy to carry out, not specific for any imaging devices and no human intervention needed. In order to evaluate the performances of algorithms, a new measurement is proposed.更多还原