计算机视觉中的光照色度估计研究
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摘要
光照的变化会引起图像和视频中物体表面颜色、阴影的变化,这对一些计算机视觉的相关应用可能会产生不利的影响,比如图像分割、物体识别、目标追踪等。因此从图像和视频中估计出场景的光照色度,并基于此将场景中的光照变化影响消除,是非常有意义的研究方向。针对这一内容,论文主要开展了以下三个方面的工作：
当前的光照色度估计算法,可分为无监督的算法和有监督的算法两大类。在无监督算法中,基于高阶导数图像结构的Gray Edge算法,比基于原始图像结构的其他算法具有更好的光照色度估计准确率。而在有监督的算法中,基于支持向量回归(Support Vector Regression, SVR)的算法能够得到最好的光照色度估计结果,但是在原始的SVR的算法中,仅仅利用了原始图像结构信息,因此在本论文中提出了一种基于支持向量回归的高阶导数图像光照色度估计算法,并且为了克服当前使用的高斯滤波做为图像预处理的缺点,新算法将双边滤波结合进来,进一步提高了算法的光照估计准确率。最后通过实验,证明了算法的有效性。
目前的光照色度估计算法大多是针对静态图像的,对视频上的光照色度估计研究却相对较少。当前对视频进行光照色度估计主要是通过将视频分解为帧,对每帧独立地估计该帧的光照,我们把这种方法称为基于帧的视频光照色度估计算法。但是视频的帧与帧之间是有高度相关性的,所以我们提出了一种基于同光照场景分割的视频光照色度估计算法,它能够充分利用帧间的关联信息来估计光照色度。算法的主要思想是将视频的帧分割为不同的场景,这里假设同一场景中的帧图像都是在同一种光照下,所以称之为同光照场景,然后从这些帧图像的组合中估计出同光照场景的光照色度,最后基于此修改每帧的光照色度估计值。
光照色度估计算法可以分为单光照场景和多光照场景两种情况,我们对多光照场景中的一种特殊情况——阴影进行了研究。阴影是一种多光照突变,阴影区域和非阴影区域具有完全不同的光照。阴影去除是将阴影区域中像素点的颜色,恢复到非阴影区域光照的效果下,因此,阴影去除的过程实际上就是多光照色度估计和校正的过程。本文提出了一种基于区域光照色度估计的图像阴影去除算法。算法的主要思路是通过光照色度估计算法对非阴影区和阴影区分别进行光照色度估计,然后计算变换阴影区中像素点到非阴影区光照效果下的比率因子,进而根据该比率因子对阴影区中的像素点进行光照变换,从而达到阴影去除的效果。
Illumination changing will cause object's surface color changing and shadow in images and video clips. This will lead incorrect results for many computer vision tasks, such as image segmentation, object recognition, target tracking and so on. So illumination color estimation and adjusting is an important research area. This dissertation focuses on illumination chromaticity estimation problem in computer vision. Our work is carried out from the following three aspects.
At present, illumination chromaticity estimation algorithms could be divided into two classes, unsupervised methods and supervised methods. Among unsupervised methods, Gray Edge using higher-order image structure performs better than the other algorithms using zero-order image structure. SVR-based illumination estimation algorithm produces the best results among supervised methods. So in this dissertation, we proposed a new illumination chromaticity estimation algorithm which combines Gray Edge and SVR together. This algorithm is proved to be able to improve accuracy of illumination chromaticity estimation.
Almost all of the illumination chromaticity estimation algorithms are designed for still images. Although they could be used on video clips frame by frame, the relative information between frames is abandoned. So an illumination chromaticity estimation algorithm especially for videos is proposed. This method takes advantages of the similarities of illuminations and contents between adjacent frames. Experiments showed that using the proposed method generate better illumination chromaticity estimation results on video clips than the other methods.
Shadow is a kind of multi-illumination situation. Illumination in shadow region is totally different with the illumination in non-shadow region. So shadow removal process is a multi-illumination chromaticity estimation and adjusting process. A shadow removal algorithm based on regional illumination chromaticity estimation is proposed here. Pixel values in shadow region are transformed to what they should appear under non-shadow illumination. Experiments on synthetic images and real images proved the proposed algorithm is effective.
当前的光照色度估计算法,可分为无监督的算法和有监督的算法两大类。在无监督算法中,基于高阶导数图像结构的Gray Edge算法,比基于原始图像结构的其他算法具有更好的光照色度估计准确率。而在有监督的算法中,基于支持向量回归(Support Vector Regression, SVR)的算法能够得到最好的光照色度估计结果,但是在原始的SVR的算法中,仅仅利用了原始图像结构信息,因此在本论文中提出了一种基于支持向量回归的高阶导数图像光照色度估计算法,并且为了克服当前使用的高斯滤波做为图像预处理的缺点,新算法将双边滤波结合进来,进一步提高了算法的光照估计准确率。最后通过实验,证明了算法的有效性。
目前的光照色度估计算法大多是针对静态图像的,对视频上的光照色度估计研究却相对较少。当前对视频进行光照色度估计主要是通过将视频分解为帧,对每帧独立地估计该帧的光照,我们把这种方法称为基于帧的视频光照色度估计算法。但是视频的帧与帧之间是有高度相关性的,所以我们提出了一种基于同光照场景分割的视频光照色度估计算法,它能够充分利用帧间的关联信息来估计光照色度。算法的主要思想是将视频的帧分割为不同的场景,这里假设同一场景中的帧图像都是在同一种光照下,所以称之为同光照场景,然后从这些帧图像的组合中估计出同光照场景的光照色度,最后基于此修改每帧的光照色度估计值。
光照色度估计算法可以分为单光照场景和多光照场景两种情况,我们对多光照场景中的一种特殊情况——阴影进行了研究。阴影是一种多光照突变,阴影区域和非阴影区域具有完全不同的光照。阴影去除是将阴影区域中像素点的颜色,恢复到非阴影区域光照的效果下,因此,阴影去除的过程实际上就是多光照色度估计和校正的过程。本文提出了一种基于区域光照色度估计的图像阴影去除算法。算法的主要思路是通过光照色度估计算法对非阴影区和阴影区分别进行光照色度估计,然后计算变换阴影区中像素点到非阴影区光照效果下的比率因子,进而根据该比率因子对阴影区中的像素点进行光照变换,从而达到阴影去除的效果。
Illumination changing will cause object's surface color changing and shadow in images and video clips. This will lead incorrect results for many computer vision tasks, such as image segmentation, object recognition, target tracking and so on. So illumination color estimation and adjusting is an important research area. This dissertation focuses on illumination chromaticity estimation problem in computer vision. Our work is carried out from the following three aspects.
At present, illumination chromaticity estimation algorithms could be divided into two classes, unsupervised methods and supervised methods. Among unsupervised methods, Gray Edge using higher-order image structure performs better than the other algorithms using zero-order image structure. SVR-based illumination estimation algorithm produces the best results among supervised methods. So in this dissertation, we proposed a new illumination chromaticity estimation algorithm which combines Gray Edge and SVR together. This algorithm is proved to be able to improve accuracy of illumination chromaticity estimation.
Almost all of the illumination chromaticity estimation algorithms are designed for still images. Although they could be used on video clips frame by frame, the relative information between frames is abandoned. So an illumination chromaticity estimation algorithm especially for videos is proposed. This method takes advantages of the similarities of illuminations and contents between adjacent frames. Experiments showed that using the proposed method generate better illumination chromaticity estimation results on video clips than the other methods.
Shadow is a kind of multi-illumination situation. Illumination in shadow region is totally different with the illumination in non-shadow region. So shadow removal process is a multi-illumination chromaticity estimation and adjusting process. A shadow removal algorithm based on regional illumination chromaticity estimation is proposed here. Pixel values in shadow region are transformed to what they should appear under non-shadow illumination. Experiments on synthetic images and real images proved the proposed algorithm is effective.
引文
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