基于图像导数框架和非负稀疏编码的颜色恒常计算方法

 本文通过模拟初级视皮层神经元感受野在视觉信息处理中的功能,提出了一种基于图像导数框架和非负稀疏编码思想的颜色恒常性计算模型,以实现对色偏图像的颜色矫正.从解决工程问题的角度,本文提出的基于生理机制的计算模型在处理效果上可与目前最好的颜色恒常性算法相媲美;从计算神经科学的角度,本文模型支持了大脑初级视皮层在视觉颜色恒常性中扮演重要角色的观点.     

基于色彩管理的CRT色彩特性分析

彩色显示器是用来传达颜色信息的重要工具,而色彩在不同设备和系统上往往会造成差异,因此国际ICC联盟提出了对显示器进行色彩管理,实现色彩空间转换。采用实验的方法对显示器色域、通道独立性、磷粉恒常性、空间均匀性、时间均匀性等特性进行分析,为下一步建立精确的色彩转换模型、完善的特征文件提供了有效的数字依据和合理的误差处理办法。     

基于CPCT的彩色图像融合算法

彩色融合图像在场景深度感知、目标探测识别、减少判断时间等方面优于灰度融合图像,重点研究了红外与可见光彩色图像融合算法。针对经典的自然感图像融合算法中计算复杂度高、缺乏颜色恒常性问题,提出基于YUV颜色空间恒参数颜色传递(CPCT)图像融合算法,并通过栅栏、山丘、森林和城镇四组实验验证了本文算法在颜色恒常性和算法实时性上有较大改善。     

无色恒常性原理与CLC白平衡方法

由于光在水中的吸收和散射，以及不同波长的光传播距离上的差异，水下图像严重受到对比度降低和颜色偏色的影响，因此色彩的白平衡校正是水下图像处理的一个关键技术。本文提出了一种新的无色恒常性原理（CLC:ColorLess Constancy）及白平衡方法。方法 首先将彩色图像转换到YCbCr颜色空间实现亮色分离，然后通过非线性方法检测色度分量,计算每个像素对应的色度权重，然后根据新的线性加权算法重新计算每个像素的色度，实现对图像中无色区域的保护，以及对偏色区域的色彩校正。与几种经典白平衡方法进行比较，CLC方法克服了传统方法易对无色区域产生不良偏色的缺点，取得了较好的视觉感知效果。 实验表明该算法复杂性低，鲁棒性好，说明了CLC白平衡算法的实用性，以及无色恒常性原理的科学性。      

基于全变分Retinex及梯度域雾天图像增强算法

为提高雾天图像增强的对比度并保持颜色恒常性,提出了基于全变分Retinex及梯度域雾天图像增强算法。首先,采用高斯—赛德尔GS（Gauss-Seidel）迭代算法对基于Retinex的全变分能量泛函数进行求解,从而有效地保持颜色恒常性;其次,采用相对梯度与绝对梯度相结合的方式拉伸雾天图像较亮处的梯度, 在全变分Retinex理论下重建增强后的雾天图像,并将该增强算法应用到彩色图像;最后,加权融合基于全变分Retinex增强算法与梯度域增强算法的增强结果,使得增强结果既能提高对比度又能保持色彩恒常性。实验结果表明,本算法提高了雾天图像增强后的对比度和清晰度,具有颜色恒常性、颜色保真高等特性。     

一种基于LAB色空间拓扑剖分影射的颜色恒常性模型

 本文针对人体肤色和黏膜颜色特点,建立一种室内自然光条件下的医学真彩图像采集分析的颜色恒常性模型.在LAB均匀色度空间中,对一维L*空间进行线性影射,二维a*b*空间进行三角剖分影射和还原,建立了一种拓扑剖分影射TRM模型.通过对SG色标中13个肤色色标进行的校正还原,结果显示,TRM模型还原的颜色色差明显减小,离散度小,性能稳定.     

基于视觉心理学的物体大小恒常性计算

知觉恒常性是人类感知世界最重要、最突出的方面,它为解决计算机图像理解和物体识别等经典计算机视觉难题提供了新的思路.大小恒常性是最重要的知觉恒常性之一.正确的图像物体大小恒常性感知的关键在于准确计算物体在图像中的感知深度.本文总结了人眼使用的各种图像深度线索,提出了这些线索融合与冲突的解决方案,然后用数学方法建立了图像物体大小恒常性计算模型.实验结果表明该模型是有效的.本文是应用视觉心理学来解决计算机视觉问题的一次成功而有益的探索.     

语义驱动的颜色恒常决策算法

不同颜色恒常性算法适用于不同场景下的图像,算法融合是扩展颜色恒常性算法适用范围常用的方法之一,而现有融合性算法在算法选择依据上忽略了语义信息在图像纹理特征描述中的作用,导致光源估计时的精度不高。针对该问题,提出一种语义驱动的颜色恒常决策算法。首先,利用PSPNet(Pyramid Scene Parsing Network)模型对经过一阶灰度边缘算法(1st Gray Edge)偏色预处理后的目标图像进行场景语义分割,并计算场景中各个语义类别的占比;其次,根据语义类别及占比在已训练的决策集合中寻找相似的参考图像,并使用欧氏距离计算两者的语义相似度;最后,将语义相似度与基于多维欧氏空间确定的阈值进行判别,根据判别结果选择合适算法为目标图像实行偏色校正。在Color Checker和NUS-8 camera两种数据集中的实验结果表明,所提算法光源估计角度误差较单一算法均大幅度下降,且较同类型融合性算法分别下降14.02%和8.17%,提高了光源估计的鲁棒性和准确度。     

一种改进的多特征融合目标跟踪算法

针对仅用单一颜色特征导致跟踪算法鲁棒性不高的问题,提出了一种改进的多特征融合目标跟踪算法。为了获得准确的目标颜色模型,提出了一种自适应选取目标颜色直方图的方法,同时利用LBP算子建立目标纹理特征模型,增强对目标的表征能力。在特征融合中引入特征不确定性度量方法,自适应调整不同特征对跟踪结果的贡献,有效地提高了传统粒子滤波算法的鲁棒性。实验结果表明,融合后的算法比传统的加性融合与乘性融合算法有更强的鲁棒性,能实现复杂场景下的目标跟踪,有效地描述目标。     

基于航空偏振数据的地表BPDF模型优化研究

针对航空偏振气溶胶反演涉及的地表双向偏振辐射分布函数（BPDF）特性问题,分析了地表双向偏振反射分布物理机理,结合经典地表BPDF模型参数特点,提出了基于阴影方程和归一化植被指数的优化地表BPDF模型。利用航空辐射偏振遥感数据,验证了新模型的精度,相对于实测结果,平均相对偏差小于9.9%。与三种经典模型模拟结果相比,新模型值随不同观测角度变化时,其一致性很好。根据新模型精度,还模拟了地表偏振反射率偏差与气溶胶光学厚度反演误差的关系,发现地表偏振反射率偏差为4×10-4时,带来反演气溶胶光学厚度误差小于0.03,满足气溶胶偏振反演应用要求。     

Edge-based color constancy.

Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for color constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Contrary to existing color constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art color constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed color constancy algorithms obtain comparable results as the state-of-the-art color constancy methods with the merit of being computationally more efficient.     

A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data

We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely. The algorithms chosen for close study include two gray world methods, a limiting case of a version of the Retinex method, a number of variants of Forsyth's (1990) gamut-mapping method, Cardei et al.'s (2000) neural net method, and Finlayson et al.'s color by correlation method (Finlayson et al. 1997, 2001; Hubel and Finlayson 2000) . We investigate the ability of these algorithms to make estimates of three different color constancy quantities: the chromaticity of the scene illuminant, the overall magnitude of that illuminant, and a corrected, illumination invariant, image. We consider algorithm performance as a function of the number of surfaces in scenes generated from reflectance spectra, the relative effect on the algorithms of added specularities, and the effect of subsequent clipping of the data. All data is available on-line at http://www.cs.sfu.ca//spl sim/color/data, and implementations for most of the algorithms are also available (http://www.cs.sfu.ca//spl sim/color/code).     

Research on color constancy under open illumination conditions

Color information plays a key role in the research fields of object recognition and image retrieval. However, the actual color varies by the conditions of illumination, especially the open natural daylight. Four different color constancy schemes are proposed in the paper to minimize the effects of open illumination conditions. (1) The color constancy scheme based on the image statistics is proposed,which includes the color cast detection and removal. (2) The color constancy scheme based on the color temperature curve is proposed, which combines Gaussian model with linear fitting to estimate color temperature curve. (3) The color constancy scheme based on the double exposure theory is proposed,which is able to reproduce a color image under typical illumination. (4) According to the concepts of supervised learning, the supervised color constancy scheme is proposed. The transformation of color values from unknown illumination to typical illumination is solved by improved Support Vector Re-gression (SVR).     

视觉神经元感受野与色觉恒常性

提出人的视觉神经元具有感受野是人眼具有色恒常智能的真正根源联系感受野的模型函数,推导出色恒常的明度公式,并论证它是把现有各种色恒常算法综合于一式的总结性公式。最后讨论了用明度公式改造色度计的问题。     

城市航空影像中基于颜色恒常性的阴影消除

基于颜色恒常性理论,对真彩色和彩红外城市航空影像中高大建筑物形成的阴影进行消除。首先采用光谱比技术和最大类间方差法(Otsu)阈值分割技术进行城市航空影像中建筑物阴影的检测,进而就颜色恒常计算的Shades of Gray算法中明可夫斯基范式(Minkowski norm)的p取不同值情况下的阴影去除效果进行实验,利用亮度、对比度及平均梯度值比较阴影去除效果的好坏。实验表明:在基于航空影像阴影区域及非阴影区域划分的基础上,本文方法比一般的阴影区反差拉伸方法效果好;且与一般场景影像的阴影去除不同,对两类航空影像,p取2时阴影去除效果最佳,说明这两类影像不能简单看成是一个灰色世界影像。     

Constrained nonlinear optimization approaches to color-signalseparation

Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.     

Inversion of ocean color observations using particle swarm optimization

Inversion of ocean color reflectance measurements can be cast as an optimization problem, where particular parameters of a forward model are optimized in order to make the forward-modeled spectral reflectance match the spectral reflectance of a given in situ sample. Here, a simulated ocean color dataset is used to test the capability of a recently introduced global optimization process, particle swarm optimization (PSO), in the retrieval of optical properties from ocean color. The performance of the PSO method was compared with the more common genetic algorithms (GA) in terms of model accuracy and computation time. The PSO method has been shown to outperform the GA in terms of model error. Of particular importance to ocean color remote sensing is the speed advantage that PSO affords over GA.     

Integrating color constancy into JPEG2000.

The human visual system is able to perceive colors as approximately constant. This ability is known as color constancy. In contrast, the colors measured by a sensor vary with the type of illuminant used. Color constancy is very important for digital photography and automatic color-based object recognition. In digital photography, this ability is known under the name automatic white balance. A number of algorithms have been developed for color constancy. We review two well-known color constancy algorithms, the gray world assumption and the Retinex algorithm and show how a color constancy algorithm may be integrated into the JPEG2000 framework. Since computer images are usually stored in compressed form anyway, little overhead is required to add color constancy into the processing pipeline.