The Promise and Perils of Near-Regular Texture

Motivated by the low structural fidelity for near-regular textures in current texture synthesis algorithms, we propose and implement an alternative texture synthesis method for near-regular texture. We view such textures as statistical departures from regular patterns and argue that a thorough understanding of their structures in terms of their translation symmetries can enhance existing methods of texture synthesis. We demonstrate the perils of texture synthesis for near-regular texture and the promise of faithfully preserving the regularity as well as the randomness in a near-regular texture sample.     

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.     

Using perceptual relation of regularity and anisotropy in the texture with independent component model for defect detection

This paper addresses the raw textile defect detection problem using independent components approach with insights from human vision system. Human vision system is known to have specialized receptive fields that respond to certain type of input signals. Orientation-selective bar cells and grating cells are examples of receptive fields in the primary visual cortex that are selective to periodic- and aperiodic-patterns, respectively. Regularity and anisotropy are two high-level features of texture perception, and we can say that disruption in regularity and/or orientation field of the texture pattern causes structural defects. In our research, we observed that independent components extracted from texture images give bar or grating cell like results depending on the structure of the texture. For those textures having lower regularity and dominant local anisotropy (orientation or directionality), independent components look similar to bar cells whereas textures with high regularity and lower anisotropy have independent components acting like grating cells. Thus, we will expect different bar or grating cell like independent components to respond to defective and defect-free regions. With this motivation, statistical analysis of the structure of the texture by means of independent components and then extraction of the disturbance in the structure can be a promising approach to understand perception of local disorder of texture in human vision system. In this paper, we will show how to detect regions of structural defects in raw textile data that have certain regularity and local orientation characteristics with the application of independent component analysis (ICA), and we will present results on real textile images with detailed discussions.     

Periodic pattern of texture analysis and synthesis based on texels distribution

Recently, sample-based texture synthesis techniques have drawn significant attention from researchers. These existing approaches mainly use the Markov Random Field (MRF) or texture features as texture model to analyze the local properties of sample textures. Indeed, human perception is sensitive to structure and periodicity. In this paper, we perform texture synthesis by taking into account the distribution of texels. Given a sample texture, the analysis procedure consists in segmenting texture into individual texels, and detecting each texel in order to analyze their neighborhood relationships by constructing connectivity. Then the synthesis process consists in reproducing a new large texture directly on a user-specified canvas by recomposing segmented texels, which synthesizes two-dimensional texel arrangements based on the previously constructed neighborhood relationships of texels. Results show that the proposed method is successful in generating textures visually indistinguishable to the sample textures. Moreover, the method especially deals with the near-regular textures, which well preserves underlying structural regularity.     

基于分块的高效图像可逆认证方法

图像可逆认证是一项将可逆信息隐藏和脆弱水印相结合的新技术,其既能实现对图像的脆弱认证,还能在提取认证信息的同时无失真地恢复出原始载体,对图像的原始性和完整性认证具有非常重要的意义.针对现有可逆认证方法认证精度低、对具有复杂纹理的图像或图像中部分纹理复杂区域无法实现有效保护的问题,提出一种新的图像可逆认证方法.首先对待认证图像进行分块,根据每个子块可嵌入容量将其分为差分块和平移块,并采用不同的可逆嵌入方法对不同类型的块进行认证码嵌入操作.为了增大嵌入容量以提高对每个子块的认证效果,还采取了分层嵌入的方式.在认证方,可以通过从每个子块中提取认证码实现子块的篡改检测和定位.此外,所提方法还可与形态学中的膨胀和腐蚀操作结合以细化篡改检测标记,进一步提高检测效果.实验结果表明,所提方法能够在同样的认证精度下对纹理平滑和纹理复杂的图像进行保护,同时还能够实现对几乎所有子块的独立认证和恢复,具有广泛的适用性.     

Multichannel texture analysis using localized spatial filters

A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures     

Document image characterization using a multiresolution analysis of the texture: application to old documents

In this article, we propose a method of characterization of images of old documents based on a texture approach. This characterization is carried out with the help of a multi-resolution study of the textures contained in the images of the document. Thus, by extracting five features linked to the frequencies and to the orientations in the different areas of a page, it is possible to extract and compare elements of high semantic level without expressing any hypothesis about the physical or logical structure of the analyzed documents. Experimentation based on segmentation, data analysis and document image retrieval tools demonstrate the performance of our propositions and the advances that they represent in terms of characterization of content of a deeply heterogeneous corpus.     

Capture and fusion of 3d surface texture

Image fusion is a process that multiple images of a scene are combined to form a single image. The aim of image fusion is to preserve the full content and retain important features of each original image. In this paper, we propose a novel approach based on wavelet transform to capture and fusion of real-world rough surface textures, which are commonly used in multimedia applications and referred to as3D surface texture. These textures are different from 2D textures as their appearances can vary dramatically with different illumination conditions due to complex surface geometry and reflectance properties. In our approach, we first extract gradient/height and albedo maps from sample 3D surface texture images as their representation. Then we measure saliency of wavelet coefficients of these 3D surface texture representations. The saliency values reflect the meaningful content of the wavelet coefficients and are consistent with human visual perception. Finally we fuse the gradient/height and albedo maps based on the measured saliency values. This novel scheme aims to preserve the original texture patterns together with geometry and reflectance characteristics from input images. Experimental results show that the proposed approach can not only capture and fuse 3D surface texture under arbitrary illumination directions, but also has the ability to retain the surface geometry properties and preserve perceptual features in the original images.     

A new approach for rotation-invariant and noise-resistant texture analysis and classification

The analysis and classification of images, such as texture images, is one of the substantial and important fields in image processing. Due to destructive effects of image rotation and noise, the stability and efficiency of texture analysis and classification methods are an important research area. In this paper, a new method for texture analysis and classification has been proposed which is based on a particular combination of wavelet, ridgelet and Fourier transforms as well as support vector machine. The proposed method has been evaluated for 13 texture datasets produced by three original datasets containing 25 and 111 original textures from Brodatz database and 24 original textures from OUTEX database. These datasets comprise 415584 and 93600 rotated noise-free and noisy texture images for Brodatz database and also 49920 noisy and 4320 noise-free texture images for OUTEX database, respectively. Simulation results demonstrate the capability, efficiency and also stability of the proposed method especially for real-time rotation-invariant and noise-resistant texture analysis and classification.     

Volumetric texture synthesis for non-photorealistic volume rendering of medical data

This paper presents a novel technique, called volumetric texture synthesis, for non-photorealistic volume rendering. It extends texture synthesis from 2D areas/3D surfaces to volumes. By selecting different texture samples, it allows for a wide variety of stylized rendering for the target volume. As a preprocessing step, volume data analysis is used to identify texture orientations for the volume. This is followed by volumetric texture synthesis, which generates 3D non-photorealistic textures along the identified texture orientations. Finally, standard volume rendering is applied to display the volume data decorated by the texture. Experimental results are provided in the paper.     

结合分水岭的纹理梯度各向异性图像分割

目的 目前,许多图像分割算法对含有丰富纹理信息的图像的分割效果并不理想,尤其是在不同纹理的边缘信息的保持方面。为了解决这一问题,提出一种基于连续纹理梯度信息的各向异性图像分割算法。方法 在分水岭算法的基础上,引入纹理梯度各向异性算法,能够在避免纹理信息影响分割效果的前提下,最大限度地保证纹理边缘信息的完整。针对纹理特征数据敏感的特性,本文将离散的图像高度信息映射到连续的纹理梯度空间,能够有效减少由细小差异造成的过分割现象。结果 本文方法在BSD500 Dataset和Stanford Background Dataset中选择了大量的纹理信息丰富的图片与最新的分割算法进行了实验与对比。本文方法在分割效果（降低过分割现象）、保持边缘信息和分割准确率等方面均获得明显改进,并在图像分割的平均准确率方面与最新算法进行比较发现,本文算法的平均分割准确率达到90.9%,明显超过了其他最新算法,验证了本文方法的有效性。结论 本文提出的基于分水岭的纹理梯度各向异性算法对纹理图像的分割具有保边和准确的特点,采用连续梯度空间的方法能够有效地减少传统分水岭算法的过分割现象。本文方法主要适用于纹理信息丰富（自然纹理和人工纹理）的图片。     

Texture boundary detection based on the long correlation model

The problem of detecting texture boundaries without assuming any knowledge on the number of regions or the types of textures is considered. Texture boundaries are often regarded as better features than intensity edges, because a large class of images can be considered a composite of several different texture regions. An algorithm is developed that detects texture boundaries at reasonably high resolution without assuming any prior knowledge on the texture composition of the image. The algorithm utilizes the long correlation texture model with a small number of parameters to characterize textures. The parameters of the model are estimated by a least-squares method in the frequency domain. The existence and the location of texture boundary is estimated by the maximum-likelihood method. The algorithm is applied to several different images, and its performance is shown by examples. Experimental results show that the algorithm successfully detects texture boundaries without knowing the number of types of textures in the image     

A Wavelet-Based Approach for Analyzing Industrial Stochastic Textures With Applications

Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among industrial stochastic images or to discriminate between texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes to model this specific class of textures as colored noise and presents a new approach for multiresolution stochastic texture representation and discrimination in industry (e.g., nonwoven textiles and paper). The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local orientation and density variability as features. Based on this representation, a multiresolution distance measure for stochastic textures is proposed, and industrial applications of the method and experimental results are reported. The conclusions include ideas for future work     

An Optimal Control Approach for Texture Metamorphosis

In this paper, we introduce a new texture metamorphosis approach for interpolating texture samples from a source texture into a target texture. We use a new energy optimization scheme derived from optimal control principles which exploits the structure of the metamorphosis optimality conditions. Our approach considers the change in pixel position and pixel appearance in a single framework. In contrast to previous techniques that compute a global warping based on feature masks of textures, our approach allows to transform one texture into another by considering both intensity values and structural features of textures simultaneously. We demonstrate the usefulness of our approach for different textures, such as stochastic, semi‐structural and regular textures, with different levels of complexities. Our method produces visually appealing transformation sequences with no user interaction.     

Multiple resolution segmentation of textured images

A multiple resolution algorithm is presented for segmenting images into regions with differing statistical behavior. In addition, an algorithm is developed for determining the number of statistically distinct regions in an image and estimating the parameters of those regions. Both algorithms use a causal Gaussian autoregressive model to describe the mean, variance, and spatial correlation of the image textures. Together, the algorithms can be used to perform unsupervised texture segmentation. The multiple resolution segmentation algorithm first segments images at coarse resolution and then progresses to finer resolutions until individual pixels are classified. This method results in accurate segmentations and requires significantly less computation than some previously known methods. The field containing the classification of each pixel in the image is modeled as a Markov random field. Segmentation at each resolution is then performed by maximizing the a posteriori probability of this field subject to the resolution constraint. At each resolution, the a posteriori probability is maximized by a deterministic greedy algorithm which iteratively chooses the classification of individual pixels or pixel blocks. The unsupervised parameter estimation algorithm determines both the number of textures and their parameters by minimizing a global criterion based on the AIC information criterion. Clusters corresponding to the individual textures are formed by alternately estimating the cluster parameters and repartitioning the data into those clusters. Concurrently, the number of distinct textures is estimated by combining clusters until a minimum of the criterion is reached     

基于图形硬件的纹理图像编码与实时绘制算法

真实感绘制对于细节的要求越来越高,应用程序通常采用多幅或大幅分辨率很高的纹理图像,有限的内存空间就成了一个制约的瓶颈.针对纹理图像的特点和可编程图形硬件的特殊要求,该文提出了一种新的面向绘制的编码算法--增量式纹理编码算法及相应的解压绘制算法,有效地解决了纹理存储容量和真实感之间的矛盾,并利用可编程图形硬件实现了实时解压绘制.该算法在图像压缩编码过程中,动态添加码表内容,只有当已有码表内容不能表示当前图像区域时,才增加码表内容.这种方法不仅能够对于自相似性较强的纹理图像取得很高的压缩比,而且由于码表的动态更新特性,可以对图像序列进行流式编码.在绘制纹理时,该算法充分利用了现有可编程图像硬件的特性,实现了实时解压绘制.文中分别对于静态图像和动态图像序列进行了实验,结果显示,此方法能灵活有效地对各类纹理图像进行编码.     

虚平面映射:深度图像内部视点的绘制技术

首先推导与归纳了图像三维变换中像素深度场的变换规律,同时提出了基于深度场和极线原则的像素可见性别方法,根据上述理论和方法,提出一种基于深度图像的建模与绘制(image-based modeling and rendering,简称IBMR)技术,称为虚平面映射.该技术可以基于图像空间内任意视点对场景进行绘制.绘制时,先在场景中根据视线建立若干虚拟平面,将源深度图像中的像素转换到虚平面上,然后通过对虚平面上像素的中间变换,将虚平面转换成平面纹理,再利用虚平面的相互拼接,将视点的成像以平面纹理映射的方式完成.新方法还能在深度图像内侧,基于当前视点快速获得该视点的全景图,从而实现视点的实时漫游.新方法视点运动空间大、存储需求小,且可以发挥图形硬件的纹理映射功能,并能表现物体表面的三维凹凸细节和成像视差效果,克服了此前类似算法的局限和不足.     

傅氏变换的自配准性质及其在纹理识别和图象分割中的应用

图象按纹理一致性进行辨识和分割是图象分析中的一个重要问题 ,有着广泛的实际应用 .讨论了傅氏变换应用于纹理识别的机理 ,并基于此提出了一种图象分割算法 .图象中的纹理线条呈现出很多方向 ,并随机地分布在图象的各个位置 ,然而对于它的傅氏变换幅度谱来说 ,相同方向的线条无论其位置如何 ,它们的贡献会被叠加在一起 ,集中地反映在通过频谱中心垂直于原线条方向的条带上 .这一现象被称为傅氏变换幅度谱的自配准性质 .首先对这一性质进行实验个例的研究和理论分析 ,然后设计算法将其应用于图象的纹理辨识和基于纹理的图象分割实验 ,取得了较为满意的效果 .实验证明 ,得益于自配准性质 ,傅氏变换方法不失为一种有潜力的纹理分析和图象分割方法 ,值得进一步扩展更多的图象应用领域     

基于直方图比率特征的虹膜图像分类方法

针对虹膜纹理的模式分类问题，提出了一种直方图比率特征的虹膜纹理分类方法。该方法利用虹膜图像的直方图信息，提取虹膜灰度等级对，通过灰度等级对自相关策略。计算虹膜纹理的直方图比率特征，实现了虹膜纹理的最大化分类。在相同的实验条件下，对不同样本的虹膜图像进行了仿真实验，结果表明：直方图比率方法较传统的直方图方法平均提高了3．05％的识别率。