二维空间对象空间关系的定性表示

主要研究了二维空间中空间对象间关系的定性表示。介绍了基于RCC的拓扑关系定性表示,讨论了方向关系的定性表示,给出了一个将拓扑关系定性表示和方向关系定性表示相结合的定性表示模型——SR表示模型。     

面向矢量路网的自适应紧凑二维图像表达方法

矢量路网的二维图像表达旨在建立道路到图像的转化关系,对于道路交通流预测等实际问题具有重要研究价值.针对目前研究存在的空间拓扑关系丢失和图像分辨率不易确定等问题,提出了一种矢量路网的自适应二维图像表达方法.该方法能够自适应不同路网结构,在最大维持矢量路网拓扑关系的前提下,将道路路段一一映射到像素单元上,从而生成矢量路网的...     

Spatial Patches - A Primitive for 3D Model Representation

The commonly used solution for real-life 3D model representation is polygonal spatially consistent geometry, with texture, and, optionally, bump or displacement maps attached. Although the idea of displacement mapping is well known, there are just a few approaches to its efficient implementation. In this paper we develop a technique that allows for efficient representation and rendering of 3D models by getting a new angle on the displacement mapping concept. We introduce a new primitive that is defined as the range image of a small part of the model's surface; therefore, it is called a spatial patch. The whole model is just a collection of patches with no connectivity information between them. Such a representation can be directly acquired by 3D scanning machinery, and stored in a compact uniform form. It also allows for efficient visualization, which is the major focus of this paper. Thus, we present the logical structure of a rendering unit based on conventional z-buffering, and discuss the involved algorithms in detail. These algorithms benefit from modern features of computing units for which we believe the proposed technique can be used in a wide range of applications dealing with real-life 3D data.     

A Taxonomy of Representation Strategies in Iconic Communication

Predicting whether the intended audience will be able to recognize the meaning of an icon or pictograph is not an easy task. Many icon recognition studies have been conducted in the past. However, their findings cannot be generalized to other icons that were not included in the study, which, we argue, is their main limitation. In this paper, we propose a comprehensive taxonomy of icons that is intended to enable the generalization of the findings of recognition studies. To accomplish this, we analyzed a sample of more than eight hundred icons according to three axes: lexical category, semantic category, and representation strategy. Three basic representation strategies were identified: visual similarity; semantic association; and arbitrary convention. These representation strategies are in agreement with the strategies identified in previous taxonomies. However, a greater number of subcategories of these strategies were identified. Our results also indicate that the lexical and semantic attributes of a concept influence the choice of representation strategy.     

Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition

Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real-world makeup are costly, and extracting makeup from 2D images often struggles with shading effects and occlusions. This paper presents the first method for extracting makeup for 3D facial models from a single makeup portrait. Our method consists of the following three steps. First, we exploit the strong prior of 3D morphable models via regression-based inverse rendering to extract coarse materials such as geometry and diffuse/specular albedos that are represented in the UV space. Second, we refine the coarse materials, which may have missing pixels due to occlusions. We apply inpainting and optimization. Finally, we extract the bare skin, makeup, and an alpha matte from the diffuse albedo. Our method offers various applications for not only 3D facial models but also 2D portrait images. The extracted makeup is well-aligned in the UV space, from which we build a large-scale makeup dataset and a parametric makeup model for 3D faces. Our disentangled materials also yield robust makeup transfer and illumination-aware makeup interpolation/removal without a reference image.     

一种视觉词软直方图的图像表示方法

基于视觉词的统计建模和判别学习,提出一种视觉词软直方图的图像表示方法.假设属于同一视觉词的图像局部特征服从高斯混合分布,利用最大-最小后验伪概率判别学习方法从样本中估计该分布,计算局部特征与视觉词的相似度.累加图像中每个视觉词与对应局部特征的相似度,在全部视觉词集合上进行结果的归一化,得到图像的视觉词软直方图.讨论了两种具体实现方法:一种是基于分类的软直方图方法,该方法根据相似度最大原则建立局部特征与视觉词的对应关系;另一种是完全软直方图方法,该方法将每个局部特征匹配到所有视觉词.在数据库Caltech-4和PASCAL VOC 2006上的实验结果表明,该方法是有效的.     

A Vector-based Representation for Image Warping

A method for image analysis, representation and re-synthesis is introduced. Unlike other schemes it is not pixel based but rather represents a picture as vector data, from which an altered version of the original image can be rendered. Representing an image as vector data allows performing operations such as zooming, retouching or colourising, avoiding common problems associated with pixel image manipulation. This paper brings together methods from the areas of computer vision, image compositing and image based rendering to prove that this type of image representation is a step towards accurate and efficient image manipulation.     

Visual Word Soft-Histogram for Image Representation

基于视觉词的统计建模和判别学习,提出一种视觉词软直方图的图像表示方法.假设属于同一视觉词的图像局部特征服从高斯混合分布,利用最大-最小后验伪概率判别学习方法从样本中估计该分布,计算局部特征与视觉词的相似度.累加图像中每个视觉词与对应局部特征的相似度,在全部视觉词集合上进行结果的归一化,得到图像的视觉词软直方图.讨论了两种具体实现方法:一种是基于分类的软直方图方法,该方法根据相似度最大原则建立局部特征与视觉词的对应关系;另一种是完全软直方图方法,该方法将每个局部特征匹配到所有视觉词,在数据库Caltech-4和PASCAL VOC 2006上的实验结果表明,该方法是有效的.     

基于位平面分解的三角形NAM图像表示

位平面分解是一种能够有效地降低图像的复杂性的方法,而三角形Packing 问题是一类特殊的Packing 问题,在许多领域里得到了广泛的应用,有着巨大理论价值和实际意义.因此,借助于位平面分解和三角形Packing问题的思想,以提高多值图像的表示效率为目标,提出了一种基于位平面分解的的三角形NAM(非对称逆布局模式表示模型)的图像表示方法.给出并实现了基于位平面分解的三角形NAM的图像表示算法,理论分析和实验结果表明:与流行的线性四元树表示方法相比,基于位平面分解的三角形NAM表示方法能更有效地减少数据存储空间,是多值图像模式的一种良好的表示方法.     

Fractional-order Sparse Representation for Image Denoising

Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation (FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster. Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.      

二维手绘草图的非均匀α-B插值样条曲线表示的方法研究*

根据非均匀α-B插值样条曲线的一般形式,结合非均匀α-B插值样条曲线的形状调配方法,产生了一种新方法来解决二维手绘草图的表示问题。在二维草图中,研究了基于非均匀αB插值样条曲线的草图表示、草图生成、草图修改以及草图缩放的算法。这种表示方法模型统一,生成插值点的算法简单,适合分布式网络环境下的实时协作。表示后的草图缩放具有向量图缩放的优点,设计人员可以在缩放后的草图上进行形状调配。     

图像中物体空间关系的表示及在检索中的应用

基于图像中物体之间的空间关系的图像检索往往受困于待处理的图像中物体种类和空间位置难以自动准确地获取。文中基于物体识别算法的输出,提出一种对物体空间关系的三元组表示法,给出基于这种表示方法对图像索引、相似度计算和检索排序的方法及允许用户使用查询词和空间关系表达查询需求的二维输入界面,并实现原型系统。这种表示法具有良好的鲁棒性,可容忍物体识别算法一定程度的误差,将物体识别得到的置信度加入三元组表示法置信度计算和排序算法中,减少物体识别结果误差对检索性能的影响。在原型系统上的实验表明,该系统在实验中对包含物体位置关系的检索给出更准确的结果,在NDCG@m、MAP、F@m上均优于现有系统。     

Iconic indexing by 2-d strings

In this paper, we describe a new way of representing a symbolic picture by a two-dimensional string. A picture query can also be specified as a 2-D string. The problem of pictorial information retrieval then becomes a problem of 2-D subsequence matching. We present algorithms for encoding a symbolic picture into its 2-D string representation, reconstructing a picture from its 2-D string representation, and matching a 2-D string with another 2-D string. We also prove the necessary and sufficient conditions to characterize ambiguous pictures for reduced 2-D strings as well as normal 2-D strings. This approach thus allows an efficient and natural way to construct iconic indexes for pictures.     

A Single Image Representation Model for Efficient Stereoscopic Image Creation

Computer graphics is one of the most efficient ways to create a stereoscopic image. The process of stereoscopic CG generation is, however, still very inefficient compared to that of monoscopic CG generation. Despite that stereo images are very similar to each other, they are rendered and manipulated independently. Additional requirements for disparity control specific to stereo images lead to even greater inefficiency. This paper proposes a method to reduce the inefficiency accompanied in the creation of a stereoscopic image. The system automatically generates an optimized single image representation of the entire visible area from both cameras. The single image can be easily manipulated with conventional techniques, as it is spatially smooth and maintains the original shapes of scene objects. In addition, a stereo image pair can be easily generated with an arbitrary disparity setting. These convenient and efficient features are achieved by the automatic generation of a stereo camera pair, robust occlusion detection with a pair of Z‐buffers, an optimization method for spatial smoothness, and stereo image pair generation with a non‐linear disparity adjustment. Experiments show that our technique dramatically improves the efficiency of stereoscopic image creation while preserving the quality of the results.     

基于两级内容表征的数字图像内容保护算法

文中首先通过动态加权平均得到图像的象素级内容表征(Pixel-level Image Content Map,PICM),在此基础上,通过图像分割技术得到图像的区域级内容表征(Regional Image Content Map,RICM),并将表征图像RICM的Hu不变矩编码为内容7F-gF45息嵌入到原图像中,从而实现对原始图像的内容保护。实验结果表明,由于该两级内容表征方法及其矩特征的良好性能,算法对图像的非内容级图像操作(特别是仿射变换)具有很强的鲁棒性,且能够敏感的检测出对所保护图像的内容篡改。     

Class-Driven Non-Negative Matrix Factorization for Image Representation

Non-negative matrix factorization (NMF) is a useful technique to learn a parts-based representation by decomposing the original data matrix into a basis set and coefficients with non-negative constraints. However, as an unsupervised method, the original NMF cannot utilize the discriminative class information. In this paper, we propose a semi-supervised class-driven NMF method to associate a class label with each basis vector by introducing an inhomogeneous representation cost constraint. This constraint forces the learned basis vectors to represent better for their own classes but worse for the others. Therefore, data samples in the same class will have similar representations, and consequently the discriminability in new representations could be boosted. Some experiments carried out on several standard databases validate the effectiveness of our method in comparison with the state-of-the-art approaches.     

Parametric Surface Representation with Bump Image for Dense 3D Modeling Using an RBG-D Camera

When constructing a dense 3D model of an indoor static scene from a sequence of RGB-D images, the choice of the 3D representation (e.g. 3D mesh, cloud of points or implicit function) is of crucial importance. In the last few years, the volumetric truncated signed distance function (TSDF) and its extensions have become popular in the community and largely used for the task of dense 3D modelling using RGB-D sensors. However, as this representation is voxel based, it offers few possibilities for manipulating and/or editing the constructed 3D model, which limits its applicability. In particular, the amount of data required to maintain the volumetric TSDF rapidly becomes huge which limits possibilities for portability. Moreover, simplifications (such as mesh extraction and surface simplification) significantly reduce the accuracy of the 3D model (especially in the color space), and editing the 3D model is difficult. We propose a novel compact, flexible and accurate 3D surface representation based on parametric surface patches augmented by geometric and color texture images. Simple parametric shapes such as planes are roughly fitted to the input depth images, and the deviations of the 3D measurements to the fitted parametric surfaces are fused into a geometric texture image (called the Bump image). A confidence and color texture image are also built. Our 3D scene representation is accurate yet memory efficient. Moreover, updating or editing the 3D model becomes trivial since it is reduced to manipulating 2D images. Our experimental results demonstrate the advantages of our proposed 3D representation through a concrete indoor scene reconstruction application.     

Patch2Vec: Globally Consistent Image Patch Representation

Many image editing applications rely on the analysis of image patches. In this paper, we present a method to analyze patches by embedding them to a vector space, in which the Euclidean distance reflects patch similarity. Inspired by Word2Vec, we term our approach Patch2Vec. However, there is a significant difference between words and patches. Words have a fairly small and well defined dictionary. Image patches, on the other hand, have no such dictionary and the number of different patch types is not well defined. The problem is aggravated by the fact that each patch might contain several objects and textures. Moreover, Patch2Vec should be universal because it must be able to map never‐seen‐before texture to the vector space. The mapping is learned by analyzing the distribution of all natural patches. We use Convolutional Neural Networks (CNN) to learn Patch2Vec. In particular, we train a CNN on labeled images with a triplet‐loss objective function. The trained network encodes a given patch to a 128D vector. Patch2Vec is evaluated visually, qualitatively, and quantitatively. We then use several variants of an interactive single‐click image segmentation algorithm to demonstrate the power of our method.     

一种改进的基于NAM的彩色图像表示算法

图像表示在机器人、图像处理、模式识别等领域里是一个非常重要的研究内容之一.尽管基于NAM的彩色图像表示方法是彩色图像模式的一种良好表示方法,但是该方法是建立在单类型子模式(矩形)基础之上,因而还有更进一步的优化空间.通过对多子模式类型的组合(矩形和三角形)逆布局作进一步的研究,提出了一种改进的基于NAM的彩色图像表示算法,并给出了该算法的存储结构和数据量分析.理论分析和实验结果表明:与基于NAM的彩色图像表示算法和流行的基于线性四元树的彩色图像表示方法相比,改进的基于NAM的彩色图像表示方法能更有效地减少子模式数(节点数)和数据存储空间,是彩色图像模式表示的一种更优的表示算法,为逐步逼近彩色图像模式的最优化表示提供了一种重要的研究途径.     

Context-Enhanced Representation Learning for Single Image Deraining

International Journal of Computer Vision - Perception of content and structure in images with rainstreaks or raindrops is challenging, and it often calls for robust deraining algorithms to remove...