Analysis and Controlled Synthesis of Inhomogeneous Textures

Many interesting real‐world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture's spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An anisotropic texture is one where the local patterns exhibit a preferred orientation, which also may vary across the spatial domain. While many example‐based texture synthesis methods can be highly effective when synthesizing uniform (stationary) isotropic textures, synthesizing highly non‐uniform textures, or ones with spatially varying orientation, is a considerably more challenging task, which so far has remained underexplored. In this paper, we propose a new method for automatic analysis and controlled synthesis of such textures. Given an input texture exemplar, our method generates a source guidance map comprising: (i) a scalar progression channel that attempts to capture the low frequency spatial changes in color, lighting, and local pattern combined, and (ii) a direction field that captures the local dominant orientation of the texture. Having augmented the texture exemplar with this guidance map, users can exercise better control over the synthesized result by providing easily specified target guidance maps, which are used to constrain the synthesis process.     

一种方向Gabor滤波纹理分割算法

结合人眼视觉特性，设计了一种方向Gabor滤波器，该滤波器顾及了纹理图像的方向特性；利用Gabor滤波器的带通技术，抑制次要纹理图像的主频率分量，增强目标纹理图像主频率分量，使滤波输出图像具有较大的类间离散度和较小的类内离散度，将纹理图像的分割转化为传统的图像分割，使图像的分割质量和算法效率都得到了提高。     

An experiment on texture segmentation using modulated wavelets

A textured image is considered to be produced by a smooth signal, representing in part the illumination environment, modulated by some frequencies determined by surface characteristics of the textures. Through expansion of the smooth signal into wavelets, a textured image may be decomposed into modulated “wavelets” providing multiresolution information. It is shown that the corresponding wavelet coefficients can be obtained efficiently by using the standard wavelet transform while the associated h and g filters are modulated accordingly. A set of multichannel filters is designed through the use of a modulated “wavelet' multiresolution decomposition, providing both spatial frequency and orientation selectivity. Based on these multichannel amplitude responses as discriminating features, an image containing multiple textures can be effectively segmented. The potential of this approach is shown by experimental results     

A human vision based computational model for chromatic texturesegregation

We have developed a computational model for texture perception which has physiological relevance and correlates well with human performance. The model attempts to simulate the visual processing characteristics by incorporating mechanisms tuned to detect luminance-polarity, orientation, spatial frequency and color, which are characteristic features of any textural image. We obtained a very good correlation between the model's simulation results and data from psychophysical experiments with a systematically selected set of visual stimuli with texture patterns defined by spatial variations in color, luminance, and orientation. In addition, the model predicts correctly texture segregation performance with key benchmarks and natural textures. This represents a first effort to incorporate chromatic signals in texture segregation models of psychophysical relevance, most of which have treated grey-level images so far. Another novel feature of the model is the extension or the concept of spatial double opponency to domains beyond color, such as orientation and spatial frequency. The model has potential applications in the areas of image processing, machine vision and pattern recognition, and scientific visualization.     

织物纹理的简单视神经细胞感受野的选择特性

研究了织物纹理的简单视神经细胞感受野的选择特性。分别选择具有明显方向性和周期性以及周期性和方向性不显著的两类代表织物纹理为研究对象,采用基于独立分量分析的视觉模型估计出简单视觉细胞的感受野。通过对感受野兴奋区域重心、方向角和面积的分析,以有效表示感受野在位置、方向和空间频率的选择特性。基于织物纹理与自然纹理两类纹理的简单细胞感受野选择性比较实验显示,相比于自然纹理,织物纹理的简单细胞感受野单元中的兴奋区域在方向、位置和空间频率上具有更加显著的选择特性。实验结果表明,提出的简单视觉细胞感受野选择特性研究方法在一定程度上揭示了初级视皮层对织物纹理图像处理的神经机制,从视觉感知层面反映了织物纹理的视觉特性。     

Reconstruction of surface texture based on spatial information measured with a pen-type texture sensor

Surface texture is one of the important properties for the human to identify objects by touch. Effective reconstructions of textures are necessary for realistic interactions between the human and environment via human–computer interfaces. This paper presents a systematic approach for sensing and reconstructing periodic surface textures. Three significant issues are discussed: a pen-type texture sensor that measures the spatial information based on the measurements of contact forces; an algorithm for the reconstruction of periodic textures based on the obtained spatial information; and the method of incremental scanning to identify the polar spectrum of a surface by limited number of scans. The concept of polar spectrum is introduced to describe the spatial properties of the surface, that is, the relation between spatial frequencies and the direction of measurement. The pattern of polar spectrum is used to facilitate surface reconstructions. Experimental results based on the spatial information obtained with a laser displacement sensor and the pen-type texture sensor demonstrate the effectiveness of the proposed methods for the measurement and reconstruction of periodic textures.     

一种新的图像纹理表示方法

该文基于纹理元直方图导出了纹理的新表示方法,并根据正交镜像滤波器的小波变换能量的转换和量化来定义纹理元。此种,给出了数种小波纹理特征集的实验评价,此方法具有将Brodatz纹理分类的极好性能。同时还研究了数种考虑近似旋转不变性或比例不变性的变换。最后,引入了生成纹理直方图和形成二进制纹理集的特征空间的过程,而且纹理直方图和二进制纹理集的特征空间与颜色直方图是对称的。获得纹理的这些表示旨在为度量纹理的相似性和从图像中抽取纹理区域。     

Texture models and image measures for texture discrimination

The task of texture segmentation is to identify image curves that separate different textures. To segment textured images, one must first be able to discriminate textures. A segmentation algorithm performs texture-discrimination tests at densely spaced image positions, then interprets the results to localize edges. This article focuses on the first stage, texture discrimination.We distinguish between perceptual and physical texture differences: the former differences are those perceived by humans, while the latter, on which we concentrate, are those defined by differences in the processes that create the texture in the scene. Physical texture discrimination requires computing image texture measures that allow the inference of physical differences in texture processes, which in turn requires modeling texture in the scene. We use a simple texture model that describes textures by distributions of shape, position, and color of substructures. From this model, a set of image texture measures is derived that allows reliable texture discrimination. These measures are distributions of overall substructure length, width, and orientation; edge length and orientation; and differences in averaged color. Distributions are estimated without explicitly isolating image substructures. Tests of statistical significance are used to compare texture measures.A forced-choice method for evaluating texture measures is described. The proposed measures provide empirical discrimination accuracy of 84 to 100% on a large set of natural textures. By comparison, Laws' texture measures provide less than 50% accuracy when used with the same texture-edge detector. Finally, the measures can distinguish textures differing in second-order statistics, although those statistics are not explicitly measured.The author was with the Robotics Laboratory, Computer Science Department, Stanford University, Stanford, California 94305. He is now with the Institut National de Recherche en Informatique et en Automatique (INRIA), Sophia-Antipolis, 2004 Route des Lucioles, 06565 Valbonne Cedex, France.     

Unsupervised detection and localization of structural textures using projection profiles

The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.     

利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法*

目前基于2D-Gabor滤波器的虹膜识别算法主要是使用虹膜的相位信息或能量信息作为特征,但在虹膜可用区域减少时这些算法的识别效果明显下降。虹膜纹理除具有上述特征外,还有很强的方向性。在分析了2D-Gabor滤波器的方向和频率选择性后,提出了一种利用2D-Gabor滤波器提取纹理方向特征的虹膜识别方法。实验表明该方法提取的虹膜纹理方向特征可以在较小区域内提取出足够丰富的可区分性特征,实现高准确性的虹膜识别,说明方向特征是一种有效的虹膜识别特征。     

Deformation-based interactive texture design using energy optimization

In this paper, we present a novel interactive texture design scheme based on deformation and energy optimization. Given a small sample texture, the design process starts with applying a set of deformation operations to the sample texture to obtain a set of deformed textures. Then local changes to those deformed textures are further made by replacing their local regions with the texture elements interactively selected from other textures. Such a deform–select–replace process is iterated many times until the desired deformed textures are obtained. Finally the deformed textures are composed to form a large texture with graph-cut optimization. By combining the graph-cut algorithm with an energy optimization process, interactive selections of local texture elements are done simply through indicating the positions of texture elements very roughly with a brush tool. Our experimental results demonstrate that the proposed technique can be used for designing a large variety of versatile textures from a single small sample texture, increasing or decreasing the density of texture elements, as well as for synthesizing textures from multiple sources.     

Using multiscale texture information from ALOS PALSAR to map tropical forest

This research investigated the ability of the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) to map tropical forest in central Sumatra, Indonesia. The study used PALSAR 50 m resolution orthorectified HH and HV data. As land-cover discrimination is difficult with only two bands (HH and HV), we added textures as additional information for classification. We calculated both first- and second-order texture features and studied the effects of texture window size, quantization scale and displacement length on discrimination capability. We found that rescaling to a lower number of grey levels (8 or 16) improved discrimination capability and that equal probability quantization was more effective than uniform quantization. Increasing displacement tended to reduce the discrimination capability. Low spatial resolution increased the discrimination capability because low spatial resolution features reduce the effects of noise. A larger number of features also improved discrimination capability. However, the amount of improvement depended on the window size. We used the optimum combination of backscatter amplitude and textures as input data into a supervised multi-resolution maximum likelihood classification. We found that including texture information improved the overall classification accuracy by 10%. However, there was significant confusion between natural forest and acacia plantations, as well as between oil palm and clear cuts, presumably because the backscatter and texture of these class pairs are very similar.     

Multiscale directional filter bank with applications to structured and random texture retrieval

In this paper, multiscale directional filter bank (MDFB) is investigated for texture characterization and retrieval. First, the problem of aliasing in decimated bandpass images on directional decomposition is addressed. MDFB is then designed to suppress the aliasing effect as well as to minimize the reduction in frequency resolution. Second, an entropy-based measure on energy signatures is proposed to classify structured and random textures. With the use of this measure for texture pre-classification, an optimized retrieval performance can be achieved by selecting the MDFB-based method for retrieving structured textures and a statistical or model-based method for retrieving random textures. In addition, a feature reduction scheme and a rotation-invariant conversion method are developed. The former is developed so as to find the most representative features while the latter is developed to provide a set of rotation-invariant features for texture characterization. Experimental works confirm that they are effective for texture retrieval.     

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.     

用于字体识别的Gabor滤波角度GA优化方法

Gabor滤波角度对字体识别结果有重要影响,由于字体纹理与自然纹理的不同,现有的Gabor滤波器角度参数不适于提取字体纹理的有效特征。基于字体纹理的多变性,该文提出使用遗传算法通过对字体纹理的学习优化滤波角度参数,使之能够适应字体纹理的特点,以提高识别率。通过对常用的4种字体899块字体纹理样本的测试表明:遗传算法能够找到适合字体识别的角度参数,使用新的角度参数减少了识别时间,提高了字体识别率。     

基于NSCT域滚动引导滤波与自适应PCNN的医学图像融合

针对传统CT和MRI医学图像融合后存在边缘轮廓模糊、纹理细节丢失等问题,提出基于NSCT域结合相位一致性滚动引导滤波与改进参数自适应双通道PCNN的图像融合方法。首先,采用相位一致性滚动引导滤波对CT源图像进行增强,提高骨骼轮廓结构清晰度。然后,通过NSCT变换分解增强后的CT和MRI源图像得到低频子带和高频子带。低频子带系数采用改进参数自适应双通道脉冲耦合神经网络融合策略,明显改善了软组织的纹理细节模糊效果;高频子带系数采用加权求和修正拉普拉算法融合,提升了融合后图像的细节、纹理等信息。最后,通过逆NSCT变换重构出融合图像。通过五组对比实验表明,所提方法的AG、CC、SF、MSE以及CEN客观评价指标分别平均提高了13.30%、6.71%、4.40%、40.23%、19.16%,说明该融合方法在处理纹理细节、边缘轮廓、结构相似性以及图像像素方面性能更好。     

Haptic texture generation using stochastic models and teleoperation

In this paper, a method is suggested to generate haptic textures for isotropic surfaces using stochastic models. A Master-Slave tele-haptic system was realized by using two PHANToMs. Moving the stylus of the Master PHANToM by a user caused the Slave to scratch the surface of objects such as sandpaper or printing paper. Force data, generated by using the error between the Master and the Slave positions, were recorded and served as measures of the texture of the object in the remote environment. An estimate of haptic texture was obtained after a simple dynamic compensation of the force data. The haptic texture was modeled using a filter that was designed by analyzing the power-spectrum of estimated texture data. By passing White Gaussian Noise through the filter, virtual texture data could be generated. The forces corresponding to the virtual texture and the real texture by teleoperation were individually delivered through the Master PHANToM to users who were asked to identify or discriminate the corresponding textures. The users?? responses show that the virtual modeled textures were quite similar to the real ones.     

Choice of a pertinent color space for color texture characterization using parametric spectral analysis

This article presents a comparison of different color spaces including RGB, IHLS and L?a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.     

Shape from texture: integrating texture-element extraction andsurface estimation

A method is presented for identifying texture elements while simultaneously recovering the orientation of textured surfaces. A multiscale region detector, based on measurements in a ∇² G (Laplacian-of-Gaussian) scale space, is used to construct a set of candidate texture elements. True elements are selected from the set of candidate elements by finding the planar surface that best predicts the observed areas of the latter. Results are shown for a variety of natural textures, including waves, flowers, rocks, clouds, and dirt clods