A novel constrained texture mapping method based on harmonic map

In this paper, we present a novel constrained texture mapping method based on the harmonic map. We first project the surface of a 3D model on a planar domain by an angle-based-flattening technique and perform a parametrization. The user then specifies interactively the constraints between the selected feature points on the parametric domain of the 3D model and the corresponding pixels on the texture image; the texture coordinates of other sample points on the 3D model are determined based on harmonic mapping between the parametric domain of the model and the texture image; finally we apply an adaptive local mapping refinement to improve the rendering result in real-time. Compared with other interactive methods, our method provides an analytically accurate solution to the problem, and the energy minimization characteristic of the harmonic map reduces the potential distortion that may result in the constrained texture mapping. Experimental data demonstrate good rendering effects generated by the presented algorithm.     

基于视觉约束能量最小化的特征点匹配算法

为了解决海上目标航空遥感图像重复特征较多导致的匹配不一致问题,并简化匹配过程,文中提出了基于SIFT视觉约束能量最小化的匹配算法(CEM-SIFT)。该算法将约束能量最小化模型应用于特征点的匹配,通过构造有限脉冲响应线性滤波器,采用视觉信息计算其能量值,使得待匹配的点集经过滤波之后的平均输出能量在一定约束下达到最小值,最终实现含重复信息的特征精确匹配。采用10组航空遥感海冰图像对算法进行测试,结果表明,相对于采用SIFT欧氏距离(ED-SIFT),在匹配重复特征比较多、点集规模比较大的图像时,CEM-SIFT算法的匹配精度更高,能够达到100%。     

Timetabling through Constrained Heuristic Search and Genetic Algorithms

Hybrid genetic algorithms are presented that use constrained heuristic search and genetic techniques for the timetabling problem (TP). The TP is an NP-hard problem for which a general polynomial time deterministic algorithm is not known. The paper describes the classification of constraints and the constraint ordering to obtain the minimization of backtracking and the maximization of parallelism. The school timetabling problem is discussed in detail as a case study. The genetic algorithm approach is particularly well suited to this kind of problem, since there exists an easy way to assess a good timetable, but not a well structured automatic technique for constructing it. So, a population of timetables is created that evolves toward the best solution. The evaluation function and the genetic operators are well separated from the domain-specific parts, such as the knowledge of the problem and the heuristics, i.e. from the timetable builder. The present paper illustrates an approach based on the hybridization of constrained heuristic search with novel genetic algorithm techniques. It compares favourably with known programs to solve decision problems under logic constraints. The cost of the new algorithm and the quality of the solutions obtained in significant experiments are reported.     

基于能量最小化的图像融合

图像融合是指联合两个或两个以上的图像通过某种算法得到一幅更高质量的新图像.提出了一种融合全色图像和光谱图像的方法即能量最小化方法,能量主要由两个部分组成.第1部分保证了相关细节信息的注入.第2部分保持了多光谱图像的低频信息.另外,能量还可以包含高分辨率光谱图像的先验知识和其它一些约束条件.     

基于梯度曲面面积与稀疏约束的图像平滑方法

针对纹理图像在平滑过程中低对比度边缘易丢失和纹理细节抑制不彻底等问题,提出基于梯度曲面面积与稀疏约束的图像平滑方法.首先,将图像视作三维空间中的二维嵌入曲面,再在此基础上分析图像的几何特征并提出梯度曲面面积约束正则化项,以提高纹理抑制性能;其次,根据图像的统计特性,建立L0梯度稀疏与自适应梯度曲面面积约束的混合正则化约...     

Multiscale Minimization of Global Energy Functions in Some Visual Recovery Problems

Many image analysis and computer vision problems have been expressed as the minimization of global energy functions describing the interactions between the observed data and the image representations to be extracted in a given task. In this note, we investigate a new comprehensive approach to minimize global energy functions using a multiscale relaxation algorithm. The energy function is minimized over nested subspaces of the original space of possible solutions. These subspaces consist of solutions which are constrained at different scales. The constrained relaxation is implemented via a coarse-to-fine multiresolution algorithm that yields fast convergence towards high quality estimates when compared to standard monoresolution or multigrid relaxation schemes. It also appears to be far less sensitive to local minima than standard relaxation algorithms. The efficiency of the approach is demonstrated on a highly nonlinear combinatorial problem which consists of estimating long-range motion in an image sequence on a discrete label space. The method is compared to standard relaxation algorithms on real world and synthetic image sequences.     

一种改进的自然纹理合成方法

自然纹理合成方法是一种适合自然景物的基于样图的快速纹理合成方法。 但是候选点超越样图边界的问题没有很好得到解决,成为导致合成后图像产生的纹理块间明 显变化的主要因素。论文提出了一种改进的自然纹理合成算法,将样图边缘易产生无效候选 点的区域用样图内部与之大小和形状相同的像素块来代替,像素块和被替代像素块沿一条不 规则的曲线相匹配。合成过程中在接近边缘时像素块的生长会转向纹理内部。该方法减少了 因随机产生候选点而形成的块间不连续,有效地改善了视觉效果。     

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。     

自适应Shearlet域约束的全变差图像去噪

采用传统非线性扩散图像去噪方法得到的图像边缘模糊,为此,提出一种有限自适应Shearlet域约束的极小化变分图像去噪算法。通过自适应阈值收缩Shearlet系数,保留图像纹理与边缘空间,利用全变差极小化平滑空间,建立全变差正则化的能量泛函去噪模型。实验结果表明,该算法能在减少图像噪声的同时,保留图像边缘信息,对含有丰富纹理结构的图像,去噪性能更佳。     

结合图像细节特征的全局优化纹理合成

提出了一种结合图像细节特征的全局优化纹理合成算法.作者利用非线性分解方法获取纹理细节作为特征图,将其作为纹理图像一个新的信息通道来引导纹理合成.联合图像细节信息通道和颜色通道,在图像上建立了一个全局的纹理合成能量方程,采用最大期望值算法迭代优化求解出一个全局的能量最小值.在全局优化算法中,构建样本纹理和待合成纹理之间的各个信息通道直方图匹配算法,进一步提高纹理合成的质量.文中同时给出了结合细节特征的图像和视频纹理合成算法,实验结果表明,文中的方法较好地解决了纹理合成中合成的纹理图像结构不连续或特征结构容易断裂等问题.     

基于纹理特征的自适应图像修复算法

为了解决基于样本图像修复算法对纹理部分的修复易产生误差累计的问题,提高图像修复的准确性,对优先值计算公式进行了修正,通过引入调节因子α调整填充边缘优先级顺序,使算法在修复过程中对图像纹理细节的部分较为敏感;利用图像的小波系数估计图像的平均细节能量值,自适应地调节α因子,从而实现对不同纹理程度的图像自适当地调整修复策略,并通过实验证明了算法的有效性。     

基于自适应分数阶全变分的超分辨率图像重建

超分辨率图像重建在各领域有重要的应用价值,具有广阔的应用前景。超分辨率图像重建是一个病态求逆问题,最有效的解决方法是添加正则化项进行处理。本文在传统的全变分的基础上,添加分数阶全变分作为正则化项约束解空间,并利用纹理检测函数判断图像中不同位置的局部特征,自适应地选择其合适的阶次。采用交替方向乘子算法(ADMM)将优化函数划分为多个子问题进行求解,降低运算的复杂程度。本文全变分和自适应分数阶全变分的双正则化约束,在去除噪声锐化边缘的同时,根据图像的特征,自适应地重建出了纹理细节信息。实验结果表明,与其他方法相比,本文方法提高了图像的重建质量,且峰值信噪比（PSNR）和结构相似度（SSIM）值都有一定提高。     

应用PSO的快速纹理合成算法

应用PSO的快速纹理合成算法是一种高效的纹理合成算法,应用粒子群优化(PSO)算法对基于块采样的纹理合成算法的搜索匹配过程进行了改进,改变了原算法的全遍历搜索过程,在不影响合成质量的前提下加快了合成速度．本算法对于按序和随机的各种应用都能在一台中等的PC机上几秒内合成高质量的纹理．并对算法执行中的粒子数、迭代次数对合成速度和合成效果的影响进行了详细的分析．     

A unified method for appearance and geometry completion of point set surfaces

This paper presents a novel approach for appearance and geometry completion over point-sampled geometry. Based on the result of surface clustering and a given texture sample, we define a global texture energy function on the point set surface for direct texture synthesis. The color texture completion is performed by minimizing a constrained global energy using the existing surface texture on the surface as the input texture sample. We convert the problem of context-based geometry completion into a task of texture completion on the surface. The geometric detail is then peeled and converted into a piece of signed gray-scale texture on the base surface of the point set surface. We fill the holes on the base surface by smoothed extrapolation and the geometric details over these patches are reconstructed by a process of gray-scale texture completion. Experiments show that our method is flexible, efficient and easy to implement. It provides a practical texture synthesis and geometry completion tool for 3D point set surfaces.     

Energy-Constrained Distortion Reduction Optimization for Wavelet-Based Coded Image Transmission in Wireless Sensor Networks

Image transmissions in Wireless Multimedia Sensor Networks (WMSNs) are often energy constrained. They also have requirement on distortion minimization, which may be achieved through Unequal Error Protection (UEP) based communication approaches. In related literature with regard to wireless multimedia transmissions, significantly different importance levels between image-pixel-position information and image-pixel-value information have not been fully exploited by existing UEP schemes. In this paper, we propose an innovative image-pixel-position information based resource allocation scheme to optimize image transmission quality with strict energy budget constraint for image applications in WMSNs, and it works by exploring these uniquely different importance levels among image data streams. Network resources are optimally allocated cross PHY, MAC and APP layers regarding inter-segment dependency, and energy efficiency is assured while the image transmission quality is optimized. Simulation results have demonstrated the effectiveness of the proposed approach in achieving the optimal image quality and energy efficiency. The performance gain in terms of distortion reduction is especially prominent with strict energy budget constraints and lower image compression ratios.      

k-着色问题及其均场退火求解算法

均场退火方法既可以看作是一种新的神经网络计算模型,又可视为是对模拟退火的重大改进.该文把具有相邻约束的多层通孔最小化问题转换为更具广泛意义的k-着色问题,并提出了k-着色问题的均场退火求解算法.算法在线段相交图模型的基础上,提出了相邻矩阵和交叠矩阵等概念,并利用换位矩阵,将问题映射为相应的神经网络,再构造了该问题的能量函数.能量函数中的目标项、违背交叠约束的惩罚项、违背相邻约束的惩罚项和神经元归一化处理保证了网络能够求解到一个合法解.实验结果表明,这是一个有效的算法.     

Utility-based scheduling for grid computing under constraints of energy budget and deadline

With recent advances in computing and communication technologies enabling mobile devices more powerful, the scope of Grid computing has been broadened to include mobile and pervasive devices. Energy has become a critical resource in such devices. So, battery energy limitation is the main challenge towards enabling persistent mobile grid computing. In this paper, we address the problem of energy constrained scheduling scheme for the grid environment. There is a limited energy budget for grid applications. The paper investigates both energy minimization for mobile devices and grid utility optimization problem. We formalize energy aware scheduling using nonlinear optimization theory under constraints of energy budget and deadline. The paper also proposes distributed pricing based algorithm that is used to tradeoff energy and deadline to achieve a system wide optimization based on the preference of the grid user. The simulations reveal that the proposed energy constrained scheduling algorithms can obtain better performance than the previous approach that considers both energy consumption and deadline.     

基于组合字典的图像复原约束优化算法

提出一种基于组合字典和约束优化的图像复原算法。建立表示图像复原问题的约束优化模型,其目标函数由l2保真项和双l1正则项的线性组合构成。利用交替优化技术将模型分解为多个子问题求解,并通过邻近算子解决降噪子问题。实验结果表明,与Oliverira算法和Beck算法相比,该算法的复原速度较快,所得图像质量较好,且复原图像与原始图像的均方误差较小。     

应用粒子群优化算法的混合纹理合成*

应用粒子群优化算法作为像素寻优策略,应用于多样图纹理合成算法中,将群智能中经典的粒子群优化算法引入到纹理合成领域。应用粒子群优化多样图纹理合成方法是传统的基于像素纹理合成方法和基于块的纹理合成方法的折中,除能大大提高多样图纹理合成的速度,样图的质量也得到了很大改善。实验证明,该算法有效解决了徐晓刚混合纹理合成中输出纹理出现的条痕问题。     

采用加权优化的图像修复

针对目前贪婪修复算法可能存在修复效果视觉不一致以及优化修复算法中存在的算法复杂度较高或者未考虑结构信息的情况,提出一种基于加权优化的图像修复算法,通过定义出新的能量函数,把图像破损修复问题转化为加权的离散优化问题,在保证结构信息强、信任度高的区域被优先修复的前提下,利用贪婪修复思想获取初值并计算权值,然后通过类EM算法迭代求解出破损区域中每一个像素的最佳值。与其他贪婪合成和最优化方法相比,优先考虑结构信息对修复效果的影响,更好地保持了纹理和结构的整体一致性。