利用小波分解和分形维数进行声纳图像识别

以分形维数和多重分形的概念为基础，通过计算原图像和灰度差分图像的分形维数和多重分形维数，形成了纹理特征集的第一部分；然后对声纳图像采用树式结构小波变换，将小波变换各个频带输出的熵作为纹理分类的特征，并根据特征本身的离散程度对其进行规范化处理，构成了纹理特征集的第二部分；最后将这两部分组合，对不同信噪比的声纳图像进行分类识别．识别结果表明，文中方法的识别率可达到90％以上．     

一种基于数学形态学的分形维数估计方法

对于分形维数的估计是基于分形理论的纹理图像分割算法中最重要的环节。由于使用固定划分的规则网格，常用的基于盒计数的分形维数估计算法及其各种改进方法的误差较大；而传统的形态学维数估计算法虽然在准确性上有一定提高．但其时间复杂度偏高。为此提出了一种基于可变结构元的数学形态学分形维数估计方法(VSEM)。该方法将灰度图像视为一个三维空间中的曲面，使用一组不同尺度的结构元分别度量该曲面．根据度量结果与尺度之间满足的指数率来估计图像表面的分形维数。通过恰当的选择结构元和使用递推技术得到不同尺度下的膨胀结果，新方法成功地弥补了现有算法的不足。本文使用了一组合成纹理和一组自然纹理来评估几种常见的分形维数估计算法。结果显示，本文提出的新方法能够在较小的时间复杂度下，得到更为精确的估计结果。最后，将该方法应用于遥感图像的分割。与其他常用的分形分割算法相比，使用该方法估计的分形维数和图像的临域均值作为特征能够得到更好的分割结果。在对比分析和分割实验中表现出的良好性能说明本文提出的分形维数估计算法可以有效地应用于纹理图像分割。     

苹果分类识别中的分形问题研究

提出一种新颖的基于分形理论的苹果属性分类识别方法。通过机器视觉获取苹果彩色图像,进行处理,得到反映苹果色度属性的灰度图象;运用分形理论,通过苹果灰度图象创建灰度特性曲线,对该曲线的分形问题进行研究,得到曲线的分形维数;根据特征维数,建立起苹果属性类别和分形维数间的关系,实现了苹果属性的准确检测和分类。实验结果表明,用分形理论方法进行苹果分类识别,具有良好的抗噪、容错能力,并能有效地克服传统图像分割识别方法适应性差的缺点,提高了分类判别准确率和分类精度。     

Segmentation of natural scenes

A new simple and computationally efficient approach to image segmentation via recursive region splitting and merging is presented. Unlike other techniques the criterion for splitting is based on a generalization of a two-class gradient relaxation method and merging uses a test for mean gray level equivalency for adjacent regions. The technique is illustrated by providing results for both synthetic and natural scenes.     

Secure semi-blind watermarking based on iteration mapping and image features

This paper presents a secure semi-blind watermarking technique based on iteration mapping and image features. An image (gray or color) is divided into blocks of fixed size that are analyzed using fractal dimension to determine their properties. The feature blocks containing edges and textures are used to form a feature label. The watermark is the fusion of image feature label and a binary copyright symbol. Arnold iteration transform is employed for constructing the watermark to increase the security. The secure image adaptive watermark is then embedded in the feature blocks by modifying DCT middle-frequency coefficients. The detection and extraction procedure is a semi-blind, i.e., it does not need the original image. Only those who have the original watermark and the key can detect and extract the right watermark, which makes the approach have high security level. Experimental results demonstrate that this algorithm can achieve good perceptual invisibility, adaptability and security. It is also robust against spatial attacks including discarding, scribbling, luminance and contrast changing, and other attacks including low or high-pass filtering, adding noise and JPEG processing.     

用于显微细胞图象的二维自适应阈值分割算法的优化

为了改善细胞图象的分割效果 ,考虑将二维自适应阈值分割算法应用于显微细胞图象的分割 .针对细胞图象的二维直方图特点和分割要求 ,在对传统二维阈值分割算法进行优化和简化的基础上 ,通过改变阈值取值范围、优化阈值搜索方法等措施 ,提出了一种快速实现细胞图象二维自适应阈值分割算法 .仿真结果表明 ,新算法与传统算法相比 ,不仅大大减少了计算复杂性 ,同时还使分割效果得到了一定程度的改善     

Patch‐based Texture Interpolation

In this paper, we present a novel exemplar‐based technique for the interpolation between two textures that combines patch‐based and statistical approaches. Motivated by the notion of texture as a largely local phenomenon, we warp and blend small image neighborhoods prior to patch‐based texture synthesis. In addition, interpolating and enforcing characteristic image statistics faithfully handles high frequency detail. We are able to create both intermediate textures as well as continuous transitions. In contrast to previous techniques computing a global morphing transformation on the entire input exemplar images, our localized and patch‐based approach allows us to successfully interpolate between textures with considerable differences in feature topology for which no smooth global warping field exists.     

Dominant Texture and Diffusion Distance Manifolds

Texture synthesis techniques require nearly uniform texture samples, however identifying suitable texture samples in an image requires significant data preprocessing. To eliminate this work, we introduce a fully automatic pipeline to detect dominant texture samples based on a manifold generated using the diffusion distance. We define the characteristics of dominant texture and three different types of outliers that allow us to efficiently identify dominant texture in feature space. We demonstrate how this method enables the analysis/synthesis of a wide range of natural textures. We compare textures synthesized from a sample image, with and without dominant texture detection. We also compare our approach to that of using a texture segmentation technique alone, and to using Euclidean, rather than diffusion, distances between texture features.     

X射线光栅相衬成像拼接缝消除算法

针对X射线光栅相衬成像拼接过程中出现的拼接缝问题,提出一种基于改进的中值滤波结合全变分平滑模型的拼接缝消除方法。首先利用方差的全局亮度调整方法消除待拼接图像间亮度不均,获取亮度均匀图像。然后通过改进的中值滤波对拼接缝两侧灰度值进行修正,以抑制灰度突变的干扰;再利用全变分平滑模型对修正后的图像进行平滑,使拼接缝过渡光滑。最后对平滑后图像进行增强,获得增强后的大视场图像。实验结果表明,所提方法在保持图像边缘细节的同时,能够有效消除拼接缝隙,提高了拼接图像质量。     

Analysing roughness of surface through fractal dimension: A review

In last three decades, fractal geometry (FG) has been the focus of attention by several researchers owing to it exhibiting excellent properties and robust application with respect to current research scenario. Fractal Dimension (FD) plays a vital role in order to analyse complex objects that are found in nature which was failed to be analysed by Euclidian geometry. FD is an imperative aspect of FG to provide indicative application in different areas of research including image processing, pattern recognition, computer graphics and many more. Analysis of an image is an important technique of image processing to describe image features like texture, roughness, smoothness etc., and is only possible through FG. Due to this reason many more technique were evolved to estimate the fractal dimension. The main aim of this article is to give a comprehensive review, which summarizes recent research progress on analysis of surface roughness and an overview of different concepts, and the way they work and their benefits and their limitations, and also we deliver how the different concepts taken into consideration to estimate FD depend upon different algorithms. This article also discusses several factors affecting FD estimation; types of similarity property, spatial resolution, sampling process, region of interest, spectral band and box-height criteria are discussed. Furthermore, we have tried to present the application area oriented versus core area of FG. There are several contradictory results found in many kinds of literature on the influence of different parameters while conducting FD analysis. Mainly it has been observed that the FD estimation will be affected by texture property, gray scale range, color property, color distance and the other parameters which are already mentioned. Hence this article will be beneficial for researchers in order to select precise FD estimation. However different algorithms lead to different results even with the use of the same kind of database images, so selection of appropriate technique is a major challenge for accurate estimation. Therefore an in-depth and proper understanding is required in order to choose the appropriate algorithm and also a robust algorithm for analysing roughness in better and precise way needs to be developed.     

A theoretical comparison of texture algorithms

An evaluation of the ability of four texture analysis algorithms to perform automatic texture discrimination will be described. The algorithms which will be examined are the spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), the gray level difference method (GLDM), and the power spectral method (PSM). The evaluation procedure employed does not depend on the set of features used with each algorithm or the pattern recognition scheme. Rather, what is examined is the amount of texturecontext information contained in the spatial gray level dependence matrices, the gray level run length matrices, the gray level difference density functions, and the power spectrum. The comparison will be performed in two steps. First, only Markov generated textures will be considered. The Markov textures employed are similar to the ones used by perceptual psychologist B. Julesz in his investigations of human texture perception. These Markov textures provide a convenient mechanism for generating certain example texture pairs which are important in the analysis process. In the second part of the analysis the results obtained by considering only Markov textures will be extended to all textures which can be represented by translation stationary random fields of order two. This generalization clearly includes a much broader class of textures than Markovian ones. The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.     

Hierarchical image analysis using irregular tessellations

A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes     

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。     

A model validation approach to texture recognition and inpainting

A new approach to texture recognition and inpainting problems is proposed. The approach is based on the robust model validation and state estimation techniques. The proposed solutions require the modeling of textures by using uncertain dynamical systems. We propose a new modeling method which is efficient in terms of computational and memory requirements. The main aspects of the modeling method include system identification and order reduction of marginally stable uncertain discrete-time systems. To demonstrate the results, both static-image textures and video textures (also known as dynamic textures) are considered.     

An improved box-counting method for image fractal dimension estimation

Fractal dimension (FD) is a useful feature for texture segmentation, shape classification, and graphic analysis in many fields. The box-counting approach is one of the frequently used techniques to estimate the FD of an image. This paper presents an efficient box-counting-based method for the improvement of FD estimation accuracy. A new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required, thereby yielding more accurate estimates. The experiments using synthesized fractional Brownian motion images, real texture images, and remote sensing images demonstrate this new method can outperform the well-known differential boxing-counting (DBC) method.     

基于中值-游程共生矩阵的纹理特征提取

提出了一种基于中值-游程共生矩阵模型的纹理特征提取方法.该方法利用了图像的灰度信息和等灰度游程长度信息,通过计算图像的中值矩阵和游程矩阵,从而计算出中值-游程共生矩阵,来提取图像的特征.仿真结果表明,该方法能有效地分割出纹理图像上区域特性不同的纹理,且分割效果优于等灰度游程矩阵和灰度共生矩阵.     

Texture description and segmentation through fractal geometry

Fractal geometry is receiving increased attention as a model for natural phenomena. In this paper we first present a new method for estimating the fractal dimension from image surfaces and show that it performs better at describing and segmenting generated fractal sets. Since the fractal dimension alone is not sufficient to characterize natural textures, we define a new class of texture measures based on the concept of lacunarity and use them, together with the fractal dimension, to describe and segment natural texture images.     

一种新的自然纹理分类方法

纹理分类一直是图像处理领域重要的研究课题之一。目前,用数学方法描述纹理特征从而进行纹理分类非常流行,但这些方法无法消除纹理视觉特征和人们理解的纹理概念之间的语义障碍。提出了一种新的基于中文自然语言纹理描述词的纹理方法,把常见的自然纹理分为10大类别,然后利用小波包分解和最小二乘支持向量机对自然纹理进行分类,实现了纹理的视觉特征到语义描述的转换。实验结果证明,该方法在图像理解和基于自然语言的图像检索中有助于缩小纹理特征的数学描述和人类理解之间的“语义鸿沟”。