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.     

Interactive texture design and synthesis from mesh sketches

Geometry mesh introduces user control into texture synthesis and editing, and brings more variations in the synthesized results. But still two problems related remain in need of better solutions. One problem is generating the meshes with desired size and pattern efficiently from easier user inputs. The other problem is improving the quality of synthesized results with mesh information. We present a new two-step texture design and synthesis method that addresses these two problems. Besides example texture, a small piece of mesh sketch drawn by hand or detected from example texture is input to our algorithm. And then a mesh synthesis method of geometry space is provided to avoid optimizations cell by cell. Distance and orientation features are introduced to improve the quality of mesh rasterization. Results show that with our method, users can design and synthesize textures from mesh sketches easily and interactively.     

Perspective-aware texture analysis and synthesis

This paper presents a novel texture synthesis scheme for anisotropic 2D textures based on perspective feature analysis and energy optimization. Given an example texture, the synthesis process starts with analyzing the texel (TEXture ELement) scale variations to obtain the perspective map (scale map). Feature mask and simple user-assisted scale extraction operations including slant and tilt angles assignment and scale value editing are applied. The scale map represents the global variations of the texel scales in the sample texture. Then, we extend 2D texture optimization techniques to synthesize these kinds of perspectively featured textures. The non-parametric texture optimization approach is integrated with histogram matching, which forces the global statics of the texel scale variations of the synthesized texture to match those of the example. We also demonstrate that our method is well-suited for image completion of a perspectively featured texture region in a digital photo.     

An evolutionary system for near-regular texture synthesis

Near-regular texture is probably among the most difficult to handle in the texture synthesis area, because the synthesis must preserve the holistic structural property and the local randomness simultaneously. In this paper, motivated by the relationship between a near-regular texture image and an evolutionary system, we propose a novel texture synthesis algorithm. By defining individuals with appropriate attributes and behaviors, we convert the texture synthesis problem to an evolution process of an evolutionary system. It can achieve high-quality synthesized results on a large variety of near-regular textures without any extra overhead for memory and pretreatment, and the speed approaches real-time. Moreover, it can be easily generalized to deal with other kinds of textures.     

一种基于GPU实现的自适应八叉树纹理绘画算法

当前,虽然基于二维图像映射定义的传统二维纹理已得到广泛应用。但是它有很多局限性。这是因为很多三维模型在纹理空间中进行参数化是非常困难的,例如隐式表面、细分表面和高密度或高细节的多边形网格。基于八叉树纹理定义,提出了一种新型的自适应八叉树纹理绘画算法。和传统的八叉树纹理映射算法相比,不但占用更少的存储空间,而且实现了基于GPU的纹理查询,有更快的查找速度。     

Radon transform orientation estimation for rotation invariant texture analysis

This paper presents a new approach to rotation invariant texture classification. The proposed approach benefits from the fact that most of the texture patterns either have directionality (anisotropic textures) or are not with a specific direction (isotropic textures). The wavelet energy features of the directional textures change significantly when the image is rotated. However, for the isotropic images, the wavelet features are not sensitive to rotation. Therefore, for the directional textures, it is essential to calculate the wavelet features along a specific direction. In the proposed approach, the Radon transform is first employed to detect the principal direction of the texture. Then, the texture is rotated to place its principal direction at 0 degrees. A wavelet transform is applied to the rotated image to extract texture features. This approach provides a features space with small intraclass variability and, therefore, good separation between different classes. The performance of the method is evaluated using three texture sets. Experimental results show the superiority of the proposed approach compared with some existing methods.     

图像的纹理标准性度量及其应用

通过分析纹理合成中子块参数对合成速度及质量的影响,发现对于一类纹理图像,不依赖于参数的选择即可快速高效地进行纹理合成。选择子块灰度平均值作为度量指标,根据纹理标准性强的图像应具有的特征,提出了一种新的计算图像纹理标准性系数的算法。结合大量计算结果,界定了一般图像、强标准性纹理以及弱标准性纹理的分类标准。并将其应用到纹理合成当中,对强标准性纹理图像的合成采用大尺度子块及零搜索的合成方法,提高了合成速度的同时保持合成质量不变。     

带约束的基于样图地表纹理合成技术研究

在传统的视景仿真开发过程中,研究人员需要将海量的航空影像照片经过数字化等一系列处理变成可使用的地表纹理,映射在地形表面,生成地景数据库.此法耗费大量人力,而且地景库一经生成就不可再改变,真实性降低.提出一种新的地表纹理的生成方法,以随机生成的高度信息作为约束,不同的地表纹理单元作为样图,采用带约束的基于样图纹理合成算法合成出真实地表纹理图像.实验中,通过改变高程约束和对多种地表纹理进行组合,可以方便快速地合成高质量的二维地表纹理图像.提出的纹理合成的方法,为今后与数字地图相结合开发地景数据库,积累一定的经验.对提高视景数据库的真实性具有实际意义.     

多种子快速纹理合成

为避免纹理映射引起的接缝和扭曲,近几年人们提出了一些纹理合成方法。但大多数只能处理部分纹理,而且相当费时,为此,提出一种新的纹理合成算法,该算法在样图中选取多个种子,并将其预置到合成图中作为实始匹配点,然后通过螺旋状路径,在初始匹配点领域搜索寻找新匹配点进行合成,以此循环,直至合成图充满为止,该方法大大加快了合成速度,与穷尽搜索法相比,合成速度平均可以提高360倍,特别是在亮度空间合成时,在合成图象质量与RGB空间结果基本相同的情况下,可以实现进一步加速,另外,该算法还可合成沿方向变化的纹理,对不同纹理进行合成实验,其结果令人满意。     

基于约束的多样图纹理合成方法

基于已有的约束的多样图纹理合成算法,提出采用图像金字塔的方法来实现,并在其中利用了纹理的相关性原理,不仅可以合成自然纹理,而且对结构性较强的纹理也取得了较好的效果,合成速度也得到了提高。实验结果表明,该算法在合成质量和合成速度上都有较大的提高,对于控制某种纹理在合成结果图中的位置的纹理合成有广泛的适用性。     

Efficient example-based painting and synthesis of 2D directional texture

We present a new method for converting a photo or image to a synthesized painting following the painting style of an example painting. Treating painting styles of brush strokes as sample textures, we reduce the problem of learning an example painting to a texture synthesis problem. The proposed method uses a hierarchical patch-based approach to the synthesis of directional textures. The key features of our method are: 1) Painting styles are represented as one or more blocks of sample textures selected by the user from the example painting; 2) image segmentation and brush stroke directions defined by the medial axis are used to better represent and communicate shapes and objects present in the synthesized painting; 3) image masks and a hierarchy of texture patches are used to efficiently synthesize high-quality directional textures. The synthesis process is further accelerated through texture direction quantization and the use of Gaussian pyramids. Our method has the following advantages: First, the synthesized stroke textures can follow a direction field determined by the shapes of regions to be painted. Second, the method is very efficient; the generation time of a synthesized painting ranges from a few seconds to about one minute, rather than hours, as required by other existing methods, on a commodity PC. Furthermore, the technique presented here provides a new and efficient solution to the problem of synthesizing a 2D directional texture. We use a number of test examples to demonstrate the efficiency of the proposed method and the high quality of results produced by the method.     

Graphical Search for Images by <Emphasis Type="Italic">PictureFinder</Emphasis>

The text searching paradigm still prevails even when users are looking for image data for example in the Internet. Searching for images mostly means searching on basis of annotations that have been made manually. When annotations are left empty, which is usually the case, searches on image file names are performed. This may lead to surprising retrieval results. The graphical search paradigm, searching image data by querying graphically, either with an image or with a sketch, currently seems not to be the preferred method partly because of the complexity in designing the query.In this paper we present our PictureFinder system, which currently supports “full image retrieval” in analogy to full text retrieval. PictureFinder allows graphical queries for the image the user has in his mind by sketching colored and/or textured regions or by whole images (query by example). By adjusting the search tolerances for each region and image feature (i.e. hue, saturation, lightness, texture pattern and coverage) the user can tune his query either to find images matching his sketch or images which differing from the specified colors and/or textures to a certain degree. To compare colors we propose a color distance measure that takes into account the fact that different colors spread differently in the color space, and which take into account that the position of a region in an image may be important.Furthermore, we show our query by example approach. Based on the example image chosen by the user, a graphical query is generated automatically and presented to the user. One major advantage of this approach is the possibility to change and adjust a query by example in the same way as a query which was sketched by the user. By deleting unimportant regions and by adjusting the tolerances of the remaining regions the user may focus on image details which are important to him.     

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.     

面向纹理平滑的方向性滤波尺度预测模型

目的 传统图像处理的纹理滤波方法难以区分梯度较强的纹理与物体的结构,而深度学习方法使用的训练集生成方式不够合理,且模型表示方式比较粗糙,为此本文设计了一种面向纹理平滑的方向性滤波尺度预测模型,并生成了含有标签的新的纹理滤波数据集。方法 在现有结构图像中逐连通区域填充多种纹理图,生成有利于模型训练的纹理滤波数据集。设计了方向性滤波尺度预测模型,该模型包含尺度感知子网络和图像平滑子网络。前者预测得到的滤波尺度图不但体现了该像素与周围像素是否为同一纹理,而且还隐含了该像素是否为结构像素的信息。后者以滤波尺度图和原图的堆叠作为输入,凭借少量的卷积层快速得出纹理滤波的结果。结果 在本文的纹理滤波数据集上与7个算法进行比较,峰值信噪比(peak signal to noise ratio, PSNR)与结构相似度(structural similarity, SSIM)分别高于第2名2.79 dB、0.0133,均方误差(mean squared error, MSE)低于第2名6.863 8,运算速度快于第2名0.002 s。在其他数据集上的实验对比也显示出本文算法更好地保持结构与平滑纹理。通过...     

指纹图像的一种奇异性检测算法

分析了现有的指纹图像奇异性检测方法中存在的问题，提出了新方法。将指纹的方向图转换为类似灰度纹理，用所提出的改进的新纹理特征检测奇异性。实验表明新方法检测奇异性的正确率为90．2％。     

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.     

基于网格优化的图像纹理替换方法

提出一种新的图像纹理替换方法,该方法在原始图像精确几何及光照信息未知的情况下,用新的纹理有效替换原始图像目标区域的纹理并逼真地保持了原始纹理的扭曲变形和光照效果.该方法借助反求的法向信息,通过一种基于网格边长伸展的参数化方法优化求得原始图像目标区域上划分的网格在纹理空间中的对应网格,有效模拟了原始纹理由于隐藏几何和透视投影引起的纹理扭曲效果;进而通过YCbCr颜色空间转换和传递保持了原始纹理的光照阴影效果.由于求取纹理采样规则的优化方法仅仅与原始图像空间相关,而与替换纹理无关,因而对目标图像处理一次后,可以有效地应用于任意新纹理的替换.通过适当的交互,替换纹理的纹元尺度渐变及纹理自遮挡效果亦可有效模拟.实验展示了良好的纹理替换效果.     

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     

Feature-aware natural texture synthesis

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis quality can be affected adversely if the texture elements in an example display spatially varied patterns, such as perspective distortion, the composition of different sub-textures, and variations in global color pattern as a result of complex illumination. This issue is common in natural textures and is a fundamental challenge for previously developed methods. Thus, we address it from a feature point of view and propose a feature-aware approach to synthesize natural textures. The synthesis process is guided by a feature map that represents the visual characteristics of the input texture. Moreover, we present a novel adaptive initialization algorithm that can effectively avoid the repeat and verbatim copying artifacts. Our approach improves texture synthesis in many images that cannot be handled effectively with traditional technologies.     

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.