基于多分辨率的厄米高斯矩的LBP纹理分类

针对LBP（局部二值模式)纹理描述子局限于在单一分辨率下捕获纹理图像的纹理信息的问题,提出一种基于多分辨率的厄米高斯矩的LBP纹理分类方法。首先结合图像纹理的多分辨率特性,采用厄米高斯矩对图像进行多分辨率重构,然后利用LBP纹理描述子对重构图像进行特征提取,最后采用K近邻特征空间距离的分类方法进行纹理分类。选取KTH-TIPS纹理数据库的纹理图像进行测试实验,实验结果表明,与传统LBP纹理分类方法相比,使用多分辨率的厄米高斯矩的LBP纹理分类方法进行纹理分类,可以更加全面地描述图像的纹理信息,使纹理分类准确率更高。     

The Structure of Locally Orderless Images

We propose a representation of images in which a global, but not a local topology is defined. The topology is restricted to resolutions up to the extent of the local region of interest (ROI). Although the ROI's may contain many pixels, there is no spatial order on the pixels within the ROI, the only information preserved is the histogram of pixel values within the ROI's. This can be considered as an extreme case of a textel (texture element) image: The histogram is the limit of texture where the spatial order has been completely disregarded. We argue that locally orderless images are ubiquitous in perception and the visual arts. Formally, the orderless images are most aptly described by three mutually intertwined scale spaces. The scale parameters correspond to the pixellation (inner scale), the extent of the ROI's (outer scale) and the resolution in the histogram (tonal scale). We describe how to construct locally orderless images, how to render them, and how to use them in a variety of local and global image processing operations.     

View-based recognition of real-world textures

A new method for recognizing 3D textured surfaces is proposed. Textures are modeled with multiple histograms of micro-textons, instead of more macroscopic textons used in earlier studies. The micro-textons are extracted with the recently proposed multiresolution local binary pattern operator. Our approach has many advantages compared to the earlier approaches and provides the leading performance in the classification of Columbia-Utrecht database textures imaged under different viewpoints and illumination directions. It also provides very promising results in the classification of outdoor scene images. An approach for learning appearance models for view-based texture recognition using self-organization of feature distributions is also proposed. The method performs well in experiments. It can be used for quickly selecting model histograms and rejecting outliers, thus providing an efficient tool for vision system training even when the feature data has a large variability.     

利用相位一致性实现纹理分类的方法

提出了一种利用相位一致性(Phase Congruency,PC)实现纹理分类的方法。首先计算图像的PC值,然后将连续的PC值离散化,接着统计离散化PC值的直方图,最后将该直方图作为特征来实现对纹理图像的分类。PC值的直方图反映的是一种全局特征,因此可以将该方法与局部二元模式方法(Local Binary Pattern,LBP)相结合来提高纹理分类性能。在Outex、Brodatz以及CUReT纹理数据库上的实验表明,提出的方法与LBP结合后可以得到更好的纹理分类结果。PC值对噪声具有良好的抗干扰能力,实验表明,提出的方法在噪声情形下对纹理分类也具有较高的鲁棒性。     

An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

Nowadays, texture classification becomes more important, as the computational power increases. The most important hardness of texture image analysis in the past was the deficiency of enough tools to characterize variety scales of texture images effectively. Recently, multi-resolution analysis such as Gabor filters, wavelet decompositions provide very good multi-resolution analytical tools for different scales of texture analysis and classification. In this paper, a Wavelet Neural Network based on Adaptive Norm Entropy (WNN-ANE) expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm (Best Wavelet Statistical Features (WSF)–Wavelet Co-occurrence Features (WCF)). Efficiently of proposed method was proved using exhaustive experiments conducted with Brodatz texture images.     

Applying wavelets transform,rough set theory and support vector machine for copper clad laminate defects classification

In this paper, we present a multi-resolution approach for the inspection of local defects embedded in homogeneous copper clad laminate (CCL) surfaces. The proposed method does not just rely on the extraction of local textural features in a spatial basis. It is based mainly on reconstructed images using the wavelet transform and inverse wavelet transform on the smooth subimage and detail subimages by properly selecting the adequate wavelet bases as well as the number of decomposition levels. The restored image will remove regular, repetitive texture patterns and enhance only local anomalies. Based on these local anomalies, feature extraction methods can then be used to discriminate between the defective regions and homogeneous regions in the restored image. Rough set feature selection algorithms are employed to select the feature. Rough set theory can deal with vagueness and uncertainties in image analysis, and can efficiently reduce the dimensionality of the feature space. Real samples with four classes of defects have been classified using the novel multi-classifier, namely, support vector machine. Effects of different sampling approach, kernel functions, and parameter settings used for SVM classification are thoroughly evaluated and discussed. The experimental results were also compared with the error back-propagation neural network classifier to demonstrate the efficacy of the proposed method.     

基于局部连接度和差异度算子的水平集纹理图像分割

目的 复杂纹理的图像分割一直是图像分割的难题,现有的一些纹理图像分割方法主要通过提取图像确定方向的灰度变化特征或者提取图像的局部灰度相似性特征得到特征图像,从而进行纹理图像的分割,然而,自然纹理中普遍存在局部形态相似和方向不确定的现象,导致现有方法不能准确地分割纹理图像。方法 本文提出局部连接算子和局部差异算子来描述局部纹理的形态相似性和局部纹理的差异度。一方面,通过设定一定阈值,将局部区域的灰度差异分为两类,分析两类差异的分布特征,从而提取图像的形态特性及局部连接度算子;另一方面,设置一种无方向性的灰度差异分析算子,提取图像局部的灰度差异值从而得到局部差异度算子。两个算子结合以更好地提取纹理图像的局部特征,然后通过融合局部相似度特征、局部差异度特征和灰度信息,构造水平集能量泛函,进而通过最小化能量泛函实现纹理图像分割。结果 相比基于Gabor变换、结构张量、局部相似度因子的纹理分割方法,提出的局部算子能够更好地区分自然图像的不同纹理区域,且对实验图像的平均分割准确率高达97%,远高于其他方法。因此,提出的模型对于自然纹理图像具有更好的分割效果。结论 本文提出了两种新颖的纹理特征局部描述子：局部连接度算子和局部差异度算子,能够有效地提取纹理特征,且有一定的互补性。实验表明,提出的方法对于复杂自然纹理图像具有良好的分割效果。     

A Multi-Resolution Content-Based Retrieval Approach for Geographic Images

Current retrieval methods in geographic image databases use only pixel-by-pixel spectral information. Texture is an important property of geographical images that can improve retrieval effectiveness and efficiency. In this paper, we present a content-based retrieval approach that utilizes the texture features of geographical images. Various texture features are extracted using wavelet transforms. Based on the texture features, we design a hierarchical approach to cluster geographical images for effective and efficient retrieval, measuring distances between feature vectors in the feature space. Using wavelet-based multi-resolution decomposition, two different sets of texture features are formulated for clustering. For each feature set, different distance measurement techniques are designed and experimented for clustering images in a database. The experimental results demonstrate that the retrieval efficiency and effectiveness improve when our clustering approach is used.     

一种鲁棒的多特征彩色图像检索新方法

为了提高彩色图像检索的准确性,以回归型支持向量机(SVR)理论为基础,结合重要的图像边缘信息,提出了一种鲁棒的多特征彩色图像检索新方法。该方法首先利用回归型支持向量机(SVR)理论,对原始图像进行去噪处理及彩色边缘提取;然后将整个彩色边缘划分成局部网格区域,并分别计算出每个网格区域的颜色直方图和纹理直方图;最后综合利用上述网格区域的颜色直方图和纹理直方图来计算图像间内容的相似度,并进行彩色图像检索。实验结果表明,该方法不仅能够准确、快速的检索出用户所需图像,而且对光照、锐化、模糊等噪声攻击均具有较好的鲁棒性。     

Detection of macular diseases in optical coherence tomography image

Vision decreasing and blindness are great threats to health, which can be caused by various kinds of macular diseases. Optical coherence tomography (OCT) is widely used in the diagnosis of macular disease. Ophthalmologists often need to check and analyse lots of OCT images and then give eye disease report for each image, which is time-consuming and inefficient. Thus, the automated classification method is necessary. In this paper, we proposed an approach to determine the status (normal or abnormal) of retina by OCT images. The proposed method can differentiate several kinds of macular diseases from normal image, including macular edema, macular hole and age-related macular degeneration. Besides the geometry features, our method takes advantage of two texture feature (local binary pattern histograms and histogram of oriented gradient) to improve the accuracy rate. The experimental results on a public available dataset demonstrate that our approach is effective and achieve higher accuracy (all accuracy >0.98, AUC can reach 1).

Flowchart of the proposed detection method.     

Color and texture image retrieval using chromaticity histograms and wavelet frames

In this paper, we explore image retrieval mechanisms based on a combination of texture and color features. Texture features are extracted using Discrete Wavelet Frames (DWF) analysis, an over-complete decomposition in scale and orientation. Two-dimensional (2-D) or one-dimensional (1-D) histograms of the CIE Lab chromaticity coordinates are used as color features. The 1-D histograms of the a, b coordinates were modeled according to the generalized Gaussian distribution. The similarity measure defined on the feature distribution is based on the Bhattacharya distance. Retrieval benchmarking is performed over the Brodatz album and on images from natural scenes, obtained from the VisTex database of MIT Media Laboratory and from the Corel Photo Gallery. As a performance indicator recall (relative number of correct images retrieved) is measured on both texture and color separately and in combination. Experiments show this approach to be as effective as other methods while computationally more tractable.     

用于遥感图像建筑物目标分类的层次匹配核

提出了一种利用图像特征空间信息的核函数——层次对数极坐标匹配核,用于遥感图像建筑物目标的分类。对图像进行特征提取,并将特征映射到已聚类好的"码本"中,量化为有限个类别。将图像由粗到细划分为多个层次的对数极坐标系下的"子区域(单元格)"。比对落入同一层次、同一"子区域(单元格)"的每类特征的直方图交集,建立加权的多尺度直方图,将多个特征多尺度直方图合并,得到最终的核函数,并利用"一对多"的支持向量机(supportvector machine,SVM)完成建筑物的分类。对标准数据库Caltech-256和自建遥感图像数据集进行实验,结果证明了该核函数的有效性。     

采用金字塔纹理和边缘特征的图像烟雾检测

目的 与传统点式感烟器相比,图像烟雾检测具有响应速度快、非接触等显著优势,但烟雾形状、色彩、纹理千差万别,造成现有算法推广性能不好,亟需提高特征推广性能.为此提出了一种采用图像金字塔纹理和边缘多尺度特征的烟雾检测算法.方法 首先,该算法将图像进行金字塔分解,然后在每层图像上提取局部二元模式(LBP)和边缘方向直方图(EOH),采用不同池化方法得到金字塔局部二元模式(PLBP)和金字塔边缘方向直方图(PEOH)序列特征,分别用于表征烟雾纹理和边缘信息,首尾相连这些直方图后,采用支持向量机(SVM)进行训练、识别烟雾.结果 这金字塔纹理和边缘特征具有很好的分类性能,能够在比较大的图像库上达到94%以上的检测率和3.0%以下的误报率.结论 本文算法提取的纹理、边缘特征,对光照、尺度具有一定不变性,实验结果也表明本文特征对烟雾检测具有较好的推广性能.