高斯颜色模型在瓷片图像分类中的应用

由于RGB颜色空间不能很好贴近人的视觉感知,同时也缺少对空间结构的描述,因此采用兼顾颜色信息和空间信息的高斯颜色模型以获取更全面的特征,提出了一种基于高斯颜色模型和多尺度滤波器组的彩色纹理图像分类法,用于瓷器碎片图像的分类。首先将原始图像的RGB颜色空间转换到高斯颜色模型;再用正规化多尺度LM滤波器组对高斯颜色模型的3个通道构造滤波图像,并借助主成分分析寻找主特征图,接着选取各通道的最大高斯拉普拉斯和最大高斯响应图像,与特征图联合构成特征图像组用以进行参数提取;最后以支持向量机作为分类器进行学习和分类。实验结果表明,与基于灰度的、基于RGB模型的和基于RGB_bior 4.4小波的方法相比,本文方法具有更好的分类结果,其中在Outex纹理图像库上获得的分类准确率为96.7%,在瓷片图像集上获得的分类准确率为94.2%。此方法可推广应用到其他彩色纹理分类任务。     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.     

MDS-based segmentation model for the fusion of contour and texture cues in natural images

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spatially adaptive) non-local constraints based on edge and contour cues allows us to efficiently regularize the reduced data (or the resulting de-textured color image) and to efficiently combine inhomogeneous region and edge based features in a data fusion/reduction model used as pre-processing step for a final segmentation task. In addition, a set of color/texture and edge-based adaptive spatial continuity constraints is imposed during the segmentation step. These improvements lead to an appealing and powerful two-step adaptive segmentation model, integrating contour and texture cues. Extensive experimental evaluation on the Berkeley image segmentation database demonstrates the efficiency of this hybrid segmentation model in terms of classification accuracy of pairwise pixels in the resulting segmentation map and in the precision–recall framework widespread used for evaluating contour detectors.     

基于滤波器阵列与图割的彩色纹理分割

图割法因无法体现像素点的纹理区域特性而难以应用于纹理分割。针对该问题,提出一种基于滤波器阵列与图割的彩色纹理分割算法。利用构建的滤波器阵列提取图像的纹理特征,并加入图像的H、S、I分量值组成纹理-色彩特征向量,采用texton直方图作为彩色纹理的统计模型对纹理-色彩特征向量进行统计计算,通过直方图差计算像素点间的纹理相似度,再应用图割法中的规范割准则对彩色纹理进行分割。实验结果证明,该算法具有较高的分割准确性。     

EWA splatting

We present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's (1989) EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.     

基于概念的自然纹理分类

纹理是图像的重要视觉特征,纹理分类是图像分析、计算机视觉等领域一个重要的研究课题。文章不同于以往的纹理分类方法,提出了一种基于概念的纹理分类方法。该方法以中文自然语言中常用的纹理描述词作为纹理概念,给出了10个基本概念的纹理分类,然后利用Gabor滤波参数和SVM对自然纹理图像进行分类,实现了图像的纹理视觉特征到纹理概念的转换,部分解决了纹理概念与纹理参数之间的“语义鸿沟”问题。     

基于自适应滤波和MBVC的矢量误差扩散法

针对彩色图像最小亮度分布处理方法中存在的由于使用固定误差扩散系数带来的方向性纹理缺陷问题,提出了一种基于自适应滤波的最小亮度分布处理改进算法。该算法通过自适应的方法,在处理过程中随图像特性动态调节扩散系数,使得处理结果更加逼近原始图像。使用最小亮度分布的办法,减小因各颜色之间亮度不同所带来的彩色噪声。实验结果表明,该算法处理输出图像更加柔和,在图像渐变区域没有明显纹理,效果更好。     

Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand–Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods.     

局部自适应的灰度图像彩色化

目的 现有的灰度图像彩色化方法为了保证彩色化结果在颜色空间上的一致性,往往采用全局优化的算法,使得图像边界区域易产生过渡平滑现象。为此提出一种局部自适应的灰度图像彩色化方法,在迁移过程中考虑局部邻域像素信息,同时自动调节邻域像素权重,在颜色正确迁移的同时保证清晰的边界信息。方法 首先结合SVM（support vector machine）和ISLIC（improved simple linear iterative clustering）算法获取彩色图像和灰度图像分类结果图;然后在分类基础上,确定灰度图像高置信度像素点,并根据图像纹理特征,在彩色图像中寻找灰度图像的像素匹配点;最后利用自适应权重均值滤波实现高置信度匹配像素点的颜色迁移,并利用迁移结果对低置信度像素点进行颜色扩散,以完成灰度图像彩色化。结果 实验结果显示,本文方法获得的彩色化迁移结果评分均高于3.5分,特别是局部放大区域评价结果均接近或高于4.0分,高于其他现有彩色化方法评价分数。表明本文方法不仅能够保证颜色迁移的准确性和颜色空间的一致性,同时也能获取颜色区分度高的边界细节信息。与现有的典型灰度图像彩色化方法相比,彩色化结果图在颜色迁移的正确性和抑制边界区域颜色的过渡平滑上都有更优的表现。结论 本文算法为灰度图像彩色化过程中抑制颜色越界问题提供了新的指导方法,能有效地应用于遥感、黑白图像/视频处理、医学图像着色等领域。     

Automatic classification of legumes using leaf vein image features

In this paper, a procedure for segmenting and classifying scanned legume leaves based only on the analysis of their veins is proposed (leaf shape, size, texture and color are discarded). Three legume species are studied, namely soybean, red and white beans. The leaf images are acquired using a standard scanner. The segmentation is performed using the unconstrained hit-or-miss transform and adaptive thresholding. Several morphological features are computed on the segmented venation, and classified using four alternative classifiers, namely support vector machines (linear and Gaussian kernels), penalized discriminant analysis and random forests. The performance is compared to the one obtained with cleared leaves images, which require a more expensive, time consuming and delicate procedure of acquisition. The results are encouraging, showing that the proposed approach is an effective and more economic alternative solution which outperforms the manual expert's recognition.     

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.     

基于频率谱处理的瓷砖纹理分类方法

针对瓷砖在线分类检测中,一些瓷砖品种的纹理难以定量化描述,且种间色差、亮度差难以区分的问题,提出一种基于频率谱处理的瓷砖纹理分类方法。首先采用基于高斯滤波的方法增强纹理并消除低幅值噪声,从而加强了对低质量图像的适应性。然后通过离散傅里叶变换得到频率谱图像,去除直流分量和高频成分后既可以突出纹理信息又抑制了高频噪声的影响。最后与预设模板库比较,采用图像匹配法计算距离,利用最小距离分类法实现分类,满足对各种纹理瓷砖的适应性以及在线分类快速性要求。实验结果表明,该方法对不同的瓷砖样本区分度高,对同类样本鲁棒性好,分类准确率高,在瓷砖在线分类检测中具有较高的实用价值。     

敏感图片过滤中大块皮肤区域的检测方法

针对色情图片中大块皮肤区域的特点,提出一种简单有效的检测方法。首先通过RGB颜色空间上的一个分段肤色模型过滤掉非肤色像素,得到候选皮肤区域;然后利用候选区域内像素的纹理粗糙度,统计得到整个候选区域的纹理粗糙度进行纹理过滤;最后计算候选肤色块的分形维数,用以过滤最难区分的沙漠类照片中的伪皮肤区域。实验结果表明,算法保持了较高的检出率和较低的误检率。     

An efficient color and texture based iris image retrieval technique

This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes ( [Mehrotra et al., 2010] and [Puhan and Sudha, 2008]) on UPOL (Dobeš, Machala, Tichavský, & Posp?´šil, 2004) and UBIRIS (Proencca & Alexandre, 2005) iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system.     

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.     

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.     

Inference of segmented color and texture description by tensor voting

A robust synthesis method is proposed to automatically infer missing color and texture information from a damaged 2D image by (N)D tensor voting (N > 3). The same approach is generalized to range and 3D data in the presence of occlusion, missing data and noise. Our method translates texture information into an adaptive (N)D tensor, followed by a voting process that infers noniteratively the optimal color values in the (N)D texture space. A two-step method is proposed. First, we perform segmentation based on insufficient geometry, color, and texture information in the input, and extrapolate partitioning boundaries by either 2D or 3D tensor voting to generate a complete segmentation for the input. Missing colors are synthesized using (N)D tensor voting in each segment. Different feature scales in the input are automatically adapted by our tensor scale analysis. Results on a variety of difficult inputs demonstrate the effectiveness of our tensor voting approach.     

基于兴趣点综合特征的图像检索

针对环形区域能更好地表达像素空间分布的特点,将其引入到Gabor小波纹理特征中,提出了一种基于兴趣点环形区域颜色和纹理特征的图像检索算法。首先采用自适应平滑滤波器对图像进行滤波处理,消除噪声的影响并利用快速鲁棒特征(SURF)算子检测兴趣点;然后计算兴趣点周围局部区域内环形颜色直方图及纹理特征,将其作为图像的综合特征;最后根据图像综合特征相似度,输出相似图像。实验结果表明,该算法使平均检索准确率提高至少7％。