Color spectral analysis for spatial structure characterization of textures in IHLS color space

In this article, a linear prediction model based approach for color texture characterization and classification in the improved hue luminance and saturation color space is presented. Pure chrominance structure information is used in addition to the normally used luminance structure information for color texture classification. Hue and saturation channels of a color image in IHLS color space are combined using a complex exponential to give a single channel which holds all the chrominance information of the image. Two dimensional complex multichannel versions of the non-symmetric half plane autoregressive model, the quarter plane autoregressive model and the Gauss Markov random field model are used to perform parametric power spectrum estimation of both luminance and the “combined chrominance” channels of the image. The accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, color texture classification is done with k-nearest neighbor algorithm. Experimental results verify that the IHLS color space exhibits better performance than the RGB color space indicating the significance of using IHLS for such analysis. They also show that color texture characterization and percentage classification obtained by combined luminance and chrominance structure information is better than the color texture classification done using only the luminance structure information.     

基于L*a*b*空间颜色信息度量的边缘检测

对L*a*b*空间进行分析,提出了一种基于颜色信息度量的边缘检测算法.通过在L*a*b*空间中构造立方体,根据体积变化定义各像素点的颜色信息度量,作为确定各像素点是否为彩色图像的边缘点的判据.该方法综合利用了彩色图像的明度和色度信息,将向量空间的计算以自然的方式转换成了标量的计算.实验证明,与传统的方法相比较,能快速有效地检测出图像的边缘.     

A total color difference measure for segmentation in color images

A single-value total color difference (TCD) measurement for scene segmentation is proposed and evaluated experimentally. Both chrominance and luminance difference criteria are considered. The luminance component is defined by a unit in luminance change expressed in terms of MacAdam's Just Noticeable Difference, JND. The chromaticity component is derived directly from JND. Experiments using both pixel and region analysis show that the proposed TCD can effectively indicate object boundaries over a wide range of luminance changes. The results have been evaluated both subjectively and quantitatively. For comparison purposes, results have been obtained in several color spaces.     

基于自然彩色图像的彩色化权值函数建模

针对现有的权值函数采用人为指定的核函数,难以准确地反映图像复杂处像素间的色度相似关系,提出了利用自然彩色图像特性构建权值函数的方法。根据自然图像的像素在RGB空间的分布特征,提取出色度与灰度的局部线性关系,并利用其作为先验知识结合最小二乘法推广至整幅图像,进而获得一种新的权值函数。该权值函数能将相近的两像素间的灰度差异、位置差异及周围像素的灰度分布统一到色度相似程度的计算中。实验结果表明,该权值函数有助于获得更优的彩色化结果,特别是在复杂边界处效果提升明显。     

颜色空间在图像彩色化处理中的应用分析

图像彩色化处理通常遵循颜色的局部相似性原则,选择适当的颜色空间是其重要问题。以能否实现颜色的亮度与色度分离为依据,将常用颜色空间分为颜色构成空间和颜色属性空间。考察了不同颜色空间中颜色分布的光滑程度,评估了各颜色空间在彩色化处理中的适用性。理论和实验结果表明,颜色属性空间更适用于彩色化处理。结合对彩色化本质的分析,引出了有关颜色空间选择的若干结论。     

Retrieval of translated, rotated and scaled color textures

A new method for color texture retrieval using color and edge features is proposed in this study. The proposed method unifies color and edge features rather than simply analyzing only color characteristics. First, the distributions of color and local edge patterns are used to derive a similarity measure for a pair of textures. Then, a retrieval method based on the similarity measure is proposed to retrieve texture images from a database of color textures. Finally, the similarity measure is extended to retrieve texture regions from a database of natural images. Since the proposed feature distributions can resist variations in translation, rotation and scale, our method has the ability to retrieve texture images or regions that change in translation, rotation and/or scale. The effectiveness and practicability of the proposed method have been demonstrated by various experiments.     

Classification with color and texture: jointly or separately?

Current approaches to color texture analysis can be roughly divided into two categories: methods that process color and texture information separately, and those that consider color and texture a joint phenomenon. In this paper, both approaches are empirically evaluated with a large set of natural color textures. The classification performance of color indexing methods is compared to gray-scale and color texture methods, and to combined color and texture methods, in static and varying illumination conditions. Based on the results, we argue that color and texture are separate phenomena that can, or even should, be treated individually.     

多投影面沉浸式虚拟环境系统的颜色校正

提出一种颜色校正的函数逼近优化模型来解决多投影面沉浸式虚拟环境系统的颜色校正问题。该颜色校正模型,采用数码相机作为颜色反馈的测量仪器,获得一个基准投影面的亮度和色度转换函数;为其他投影面分别寻找一个亮度修正函数和色度修正函数,使得各投影面与基准投影面的亮度和色度转换函数之间的L2距离最小,进而根据各修正函数来补偿各投影机的输入响应差别以达到它们之间的颜色一致。给出了逼近校正方法的算法及其程序实现,通过一个三通道的试验系统,证明了此方法对解决多投影面沉浸式虚拟环境系统颜色校正问题的有效性。     

A study of Gaussian mixture models of color and texture features for image classification and segmentation

The aims of this paper are two-fold: to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct GMMs over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the ‘color’ and ‘structure’ information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.     

Multi-component attention-based convolution network for color difference recognition with wavelet entropy strategy

The recognition of color differences in solar cells with complex textures is a significant challenge in cell manufacturing. Traditional methods fail to detect the color difference effectively. Deep learning models have exhibited promise in many engineering fields. A multi-component attention-based convolution approach is proposed for the surface inspection based on the feature information in different color spaces. Wavelet entropy is employed to represent the information of different components, remove redundant components and extract effective feature information. Additionally, a residual attention mechanism is developed to capture local features with contextual semantic information. The best network structure is determined by evaluating the layer depth of the basic model and convolution kernel size. A multi-component network model is constructed based on the formed structure to improve the ability to distinguish different color difference features. Experimental results indicated that the proposed approach exhibits competitive performance. The research solution provides guidance for applications of deep learning to improve the quality of solar cells in manufacturing.     

Color texture classification by integrative Co-occurrence matrices

Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively.     

Local MAP estimation for quality improvement of compressed color images

This paper concerns color image restoration aiming at objective quality improvement of compressed color images in general rather than merely artifact reduction. In compressed color images, colors are usually represented by luminance and chrominance components. Considering characteristics of human vision system, chrominance components are generally represented more coarsely than luminance component. To recover such chrominance components, we previously proposed a model-based chrominance restoration algorithm where color images are modeled by a Markov random field. This paper presents a color image restoration algorithm derived by the MAP estimation, where all components are totally estimated. Experimental results show that the proposed restoration algorithm is more effective than the previous one.     

Supervised texture classification: color space or texture feature selection?

The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times.     

基于均匀颜色空间的木材分类研究

木材材色在各种颜色空间中的分布范围都比较窄,而利用CIEl976L*a*b*均匀颜色空间的等距性和色差高分辨力的特点可以很好地对其进行测量与辨别.在l*a*b*颜色空间中,提取了东北常见的5种树种的径切和弦切木材图像的颜色特征,然后采用模拟退火算法对这些特征进行了选择,并结合BP神经网络和k-近邻两种分类方法进行了仿真试验,得到了满意的分类结果.     

Multispectral and joint colour-texture feature extraction for ore-gangue separation

Ore sorting is a useful tool to remove gangue material from the ore and increase the quality of the ore. The vast developments in the area of artificial intelligence allow fast processing of full color digital images for the preferred investigations. Three different approaches to color texture analysis were used for the classification of associated gangue from limestone and iron ore. All the methods were based on extensions of the co-occurrence matrix method. The first approach was a correlation method, in which co-occurrence matrices are computed both between and within the color bands. In the second approach, joint color-texture features, where color features were extracted from chrominance information and texture features were extracted from luminance information of the color bands. The last approach used grey scale texture features computed on a quantized color image. Results showed that the joint color-texture method was 98% accurate for limestone and 98.4% for iron ore gangue classification. It was further observed that the features showed better accuracy with 64 grey levels quantization.     

Rotation invariant features for color texture classification and retrieval under varying illumination

This article proposes a new quaternion-based method for rotation invariant color texture classification under illumination variance with respect to direction and spectral band. The color of an object varies according to the spectral power distribution, object-illumination, and viewing geometry of the light source. The quaternion representation of color is shown to be effective, which treats color channels as single unit rather than separate components. New texture signatures are extracted by calculating the norm of the Quaternion fourier spectrum. These signatures are proved to be invariant under image rotation and illumination rotation. Moreover, these features are also invariant to the color spaces. The robustness of different color spaces against varying illumination in color Texture classification with 45 samples of 15 outex texture classes are examined. Comparative results show that the proposed method is efficient in rotation invariant texture classification.     

图模型在彩色纹理分类中的应用

纹理分析中往往将彩色图像转换为灰度图以降低计算复杂度,这样就忽略了颜色信息。而利用主成分分析 的方法来降维彩色纹理,则可以尽可能地保留颜色和纹理信息。高斯图模型(Uaussian Graphical Models, GGM)可以 很好地描述有交互作用的高维数据,因此可用来建立图像纹理模型。根据局部马尔可夫性和高斯变量的条件回归之 间的关系,可将复杂的模型选择转变为较简单的变量选择。通过惩罚正则化方法,其部域选择和参数佑计可同步进 行,然后提取纹理特征进行彩色纹理分类,实验显示其具有很好的效果。因此,结合主成分分析和高斯图模型来构建 彩色纹理模型有很好的发展前景。     

Optimal spatial filter selection for illumination-invariant colortexture discrimination

Color textures contain a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination-invariant spatial information from a color image. In this paper, we address the problem of deriving optimal filters for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Similar features have been used in previous studies. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pairwise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filter.     

基于HVS和交叉掩蔽的彩色图像编码算法

以层树分集（SPIHT）编码方案为基础,结合人类视觉系统（HVS）模型和人类视觉对彩色图像分量亮度和色度的不同敏感性,提出了一种基于非对称编码和交叉掩蔽的小波域彩色图像压缩编码算法。该算法首先将原始图像从RGB空间转换到YCbCr空间,然后对YCbCr空间的各分量进行离散小波变换;之后根据人类视觉对彩色图像的亮度分量的敏感性,用交叉掩蔽模型对亮度分量的小波系数进行加权处理;与此同时,利用非对称编码和SPIHT编码思想完成图像的压缩。仿真实验结果表明,文中算法是一种高效的图像压缩编码方法,其压缩效果明显优于SPIHT编码方案。