多序列比对软件T-Coffee的并行化设计与实现

T-Coffee是广泛用于核酸或氨基酸的多序列比对工具.它通过生成基本信息库,扩展库,生成指导树,渐近式比对四个阶段来完成多序列的比对.分析了T-Coffee串行算法及其复杂度,并提出了基于SMP机的并行化版本.目标是使其充分并行化,实验结果表明它明显的提高了性能,并得到了很好的相对加速比.     

基于全局拼接的航拍图像拼接算法研究

研究航拍图像的拼接问题,提高图像拼接的准确度。由于当通过航拍获取的图像中部分图像相互存在重叠区域的比例不大时,造成拼接不准确。传统的区域的图像拼接算法无法将具有较小重叠区域的图像准确拼接。为了提高航拍图像拼接的准确率,提出一种全局的图像拼接算法,通过使用SIFT算法提取图像的SIFT特征点,根据位置误差最小的原则完成两幅图像的SIFT特征点匹配,最后利用整体最优化方法对拼接结果进行优化,采用全局特征点的拼接方法,可避免传统方法只利用重叠区域灰度特征而不能准确拼接低重叠度图像的问题。实验证明,改进方法利用图像的全局信息,准确地实现图像的拼接,取得了满意的结果。     

A two level real-time path planning method inspired by cognitive map and predictive optimization in human brain

A biologically inspired two level method is proposed for real-time path planning in a complex and dynamic environment, employable in ground vehicles. This method takes the advantage of both global and local path finding procedures. In the first level, i.e., global level, the planner utilizes a neural network architecture as a sensory-motor map, similar to the cognitive map used by humans, and an optimization algorithm to produce a coarse path. In the second level, i.e., local level, the global path is improved by employing a model-based prediction method with a finite prediction horizon in a way that future information about the environment is involved in the planner's decision making. In the suggested method, the prediction horizon is variable and is adjusted in each step of the planning in agreement with the kinematic features of the closest obstacle in the visual field of the planner. We considered four different path planning tasks in a virtual dynamic environment to evaluate the performance of the proposed method against the human path planning strategy. The results demonstrate the ability of the method to plan a strategy comparable to the driving scenarios chosen by most subjects and to generate a real-time collision-free path in a dynamic environment with obstacles.     

基于多直线特征的电子图象稳定算法

提出了一种基于多直线特征的全局运动估计算法,同时也介绍了它在电子图象稳定（EIS）系统中的应用,首先,用全局运动估计技术来估计摄像机的运动,即通过小波变换法提取图象中的边缘,并利用Hough变换法提取图象中的直线段特征,然后按照直线段的特征参数和位置选择原则选择具有分布特性的多个直线段,并通过对应的直线段特征比较计算局部运动参数和全局运动参数;最后根据全局运动估计结果,利用滤波法进行抖动判别和补偿,实现对图象序列的稳定处理,实验结果表明,多直线特征可以准确且稳定地估计摄像机的运动参数,通过滤波法可以消除视频序列的抖动。     

Computing global visibility maps for regions on the boundaries of polyhedra using Minkowski sums

A global visibility map is a spherical image built to describe the complete set of global visible view directions for a surface. In this paper, we consider the computation of global visibility maps for regions on the boundary of a polyhedron. Both the self-occlusions introduced by a region and the global occlusions introduced by the rest of the surfaces on the boundary of the polyhedron are considered for computing a global visibility map. We show that the occluded view directions introduced between a pair of polyhedral surfaces can be computed from the spherical projection of the Minkowski sum of one surface and the reflection of the other. A suitable subset of the Minkowski sum, which shares the identical spherical projection with the complete Minkowski sum, is constructed to obtain the spherical images representing global occlusions. Our method has been successfully tested on many CAD models. It extends the previous methods for computing global visibility maps using convex decomposition, and it exhibits a better performance.     

An efficient alignment algorithm for masked sequences

We consider the alignment problem where sequences may have masked regions. The bases in masked regions are either unspecified or unknown, and they will be denoted by N. We present an efficient algorithm that finds an optimal local alignment by skipping such masked regions of sequences. Our algorithm works for both the affine gap penalty model and the linear gap penalty model. The time complexity of our algorithm is O((n−T)(m−S)+vm+wn) time, where n and m are the lengths of given sequences A and B, T and S are the numbers of base N in A and B, and v and w are the numbers of masked regions in A and B, respectively.     

Parametric optimization of sequence alignment

Theoptimal alignment or theweighted minimum edit distance between two DNA or amino acid sequences for a given set of weights is computed by classical dynamic programming techniques, and is widely used in molecular biology. However, in DNA and amino acid sequences there is considerable disagreement about how to weight matches, mismatches, insertions/deletions (indels or spaces), and gaps.Parametric sequence alignment is the problem of computing the optimal-valued alignment between two sequences as afunction of variable weights for matches, mismatches, spaces, and gaps. The goal is to partition the parameter space into regions (which are necessarily convex) such that in each region one alignment is optimal throughout and such that the regions are maximal for this property. In this paper we are primarily concerned with the structure of this convex decomposition, and secondarily with the complexity of computing the decomposition. The most striking results are the following: For the special case where only matches, mismatches, and spaces are counted, and where spaces are counted throughout the alignment, we show that the decomposition is surprisingly simple: all regions are infinite; there are at most n^2/3 regions; the lines that bound the regions are all of the form =c + (c + 0.5); and the entire decomposition can be found inO(knm) time, wherek is the actual number of regions, andn相似文献   

基于深度学习的图像拼接算法研究综述

图像拼接是计算机视觉和计算机图形学中的一个重要分支,在三维成像等方面具有广泛的应用。相较于传统基于特征点检测的图像拼接框架,基于深度学习的图像拼接框架具有更强的场景泛化表现。目前虽然关于基于深度学习的图像拼接研究成果众多,但仍缺少相应研究的全面分析和总结。为了便于该领域后续工作的开展,梳理了该领域近10年的代表性成果。在对传统拼接方法与基于深度学习的图像拼接方法对比的基础上,从图像拼接研究领域中的单应性估计、图像拼接和图像矩形化三个子问题出发,进行了学习策略及模型架构设计、经典模型回顾、数据集等方面的整理与分析。总结了基于深度学习的图像拼接研究方法的一些特点和当前该领域的研究现状,并对未来研究前景进行了展望。     

异构数据的视图集成研究

视图集成是异构数据源集成问题的关键技术之一.论文引进XML技术,利用其作为一种数据格式描述的元语言标准的优势,改进了已有视图集成方法,采用XQuery语言建立了真正意义上的虚拟的全局视图,为异构数据集成访问奠定了基础.     

不确定时延环境的图像序列预测理论

该文提出的“不确定时延环境的图像序列预测理论”从图像通信系统目端的角度出发,可以将接收端的不连续序列图像构造成连续的视频数据。即使图像源端发送出帧速率小于24帧每秒的图像序列（如每秒15帧）,在复现端仍然能够看到连续的视频图像。     

一种新的局部阈值分割算法

针对传统局部阈值算法在分割低质量图像时存在分割时间长的问题,提出一种新的局部阈值分割算法。该算法首先去除传统阈值模型中局部标准差因式,再利用积分图像快速计算局部均值,然后利用求得的局部均值和像素点的灰度值来计算局部阈值。由于降低局部阈值计算的时间复杂度,从而减少了图像分割时间。实验结果表明,该算法不仅仅能克服非均匀光照的影响,有很好的抗噪效果,还大大减少了分割时间。     

海面图像单应的估算及数据归一化方法

根据海面图像的单应进行距离估算对于海洋工程具有重要意义。结合导管架入水参数观测问题,提出一种海面图像单应的估算方法。该方法以导管架上表面为参考平面,依据其单应矩阵及它与海平面之间的夹角计算出海面图像的单应。重点介绍了根据特征直线计算平面单应矩阵时的数据归一化方法。通过实验分析了这种归一化方法对平面远处距离估算的影响,并根据现场图片中的距离估算误差说明了在实际应用中的效果。该方法可以应用到多种海洋观测当中。     

基于水平集方法的航拍图像运动分割

针对航拍图像由于受噪声和光照影响导致分割精度差问题,将一种改进的C-V水平集方法引入到航拍图像运动分割中.首先对C-V模型作了改进,通过引入新的能量项使改进方法无需重新初始化符号距离函数,在保证分割精度的同时提高了分割速度;其次对水平集函数的偏微分方程进行改进,消除了原始C-V方法对远离实际边缘的轮廓曲线进化的抑制作用.实验结果显示,改进方法能有效地分割出航拍图像中的运动目标,在初始轮廓线远离目标实际轮廓时也能够正确分割,并能更好地满足实时性的要求.改进方法具有分割精度高,耗时少的优点,可用于航拍图像运动目标检测跟踪系统.     

A Bag of Strings Representation for Image Categorization

This paper presents an architecture well suited for natural images classification or visual object recognition applications. The method proposes to integrate a spatial representation into the well known “bag of local signatures” approach. For this purpose, it combines the power of a string representation which provides an ordered view of local features with the vectorial histogram representation allowing to recognize efficiently and quickly an image by using a machine learning classifier. To reach this goal, we propose to represent an image by a set of strings of local signatures obtained by tracking the detected salient points along image edges. We propose here to conjointly use the Hölder exponents and the direction of minimal regularity of the bidimensional signal singularities to compute a signature describing precisely a region of interest centered on an interest point. As we will see, an alphabet of strings is easily obtained by using a typical self organizing map architecture. As a consequence, a “bag of strings” representation is used, providing a compact representation encoding both local signatures and spatial information. This representation is particularly well suited to train a support vector machine classifier used for the last classification step. This architecture obtains good classification rates on different well known datasets.     

彩色图像的单应矩阵估计算法

图像间单应矩阵估计是图像配准与图像拼接中的核心问题,传统的估计方法是针对灰度图像的算法。本文以分层运动估计为基础提出了彩色图像的平面单应矩阵的估计算法。此算法采用色度与饱和度不变为约束条件得到彩色图像的光流方程,显著改善了亮度不变约束的不足之处;采用最优导数计算滤波器计算图像导数,提高了算法的精度与稳健性;采用尺度总体最小二乘方法代替最小二乘或总体最小二乘方法来估计模型参数,提高了算法对于图像噪声的适应性。实验结果表明,该算法稳健性好、精度高,而且可以得到稠密的匹配点。     

细粒度民族服饰图像检索的全局-局部特征提取方法

民族服饰图像具有不同民族风格的服装款式、配饰和图案,导致民族服饰图像细粒度检索准确率较低.因此,文中提出细粒度民族服饰图像检索的全局-局部特征提取方法.首先,基于自定义的民族服饰语义标注,对输入图像进行区域检测,分别获得前景、款式、图案和配饰图像.然后在全卷积网络结构的基础上构建多分支的全局-局部特征提取模型,对不同区...     

基于2D格状图的显微图像拼接

针对大规模显微图像的拼接,提出了一种显微图像拼接的全局对准模型。利用2D格状图表示图像的邻接关系,并根据局部对准的可靠性对格状图进行修正,以消除空白区的影响。根据图像投影坐标的误差建立全局对准模型,可以有效地消除误差积累。把系统偏斜校正作为约束条件加入全局对准模型,并利用非线性优化方法得到具有全局一致性的图像对准参数,成功地解决了大规模显微图像的拼接问题。     

Accurate M-hausdorff distance similarity combining distance orientation for matching multi-modal sensor images

Although Hausdorff distance (HD) has been widely used in an object identification between same modality images, the object identification between different modality images are challenging because of the poor edge correspondence coming from heterogeneous image characteristics. This paper proposes a robust Hausdorff distance similarity (accurate M-HD: AMHD) between multi-modal sensor data. To improve robustness against the outliers when comparing the pairs of multi-modal images, the AMHD utilizes the orientation information of each point in addition to the distance transform (DT) map as a similarity criterion. In the AMHD scheme, the DT map is generated by applying dead-reckoning signed DT, and the distance orientation (DO) map is constructed by employing the Kirsch compass kernel to the DT map, respectively. Using the additional information on the DO, the proposed similarity can precisely examine the outliers including non-correspondent edges and noises, and discard false correspondent distances efficiently. The computer simulations show that the proposed AMHD yields superior performance at aligning multi-modal sensor data (visible-thermal IR face images) over those achieved by the conventional robust schemes in terms of the position error between the ground truth and the computed position.     

RISNet:无监督真实场景图像拼接网络

图像拼接目的是获得一张高清无缝的全景图,现有方法依赖于特征匹配的准确性,会错误地对齐图像,产生伪影和失真等现象,为此提出一种新的无监督真实场景图像拼接网络,能够适应存在移动目标的真实场景拼接,保证全景图的精度无损失,包含配准和重建两个网络。在配准网络中引入内容感知分支,学习内容掩码,排除移动目标和误导性区域对于变换矩阵的负影响;在重建网络中添加边缘检测分支,构造边缘一致性感知损失,约束重建过程,优化图像细节,实现高清、无伪影的拼接效果。实验结果表明,该方法RMSE、PSNR、SSIM分别达到1.81、26.56、0.85,客观评价指标整体优于其他经典算法,用户调研结果也说明该方法获取的全景图清晰度更高。该方法有效地完成了真实场景下的无监督图像拼接,并能够泛化至其他场景的拼接任务中。     

视频图象序列的实时镶嵌技术研究

阐述了一种快速而高效的由视频图象或视频图象序列生成全景的配准方法,为了估计图象配准的校正参数,该方法计算伪运动矢量,这些伪运动矢量是光流在每一选定象素处的粗略估计,使用方法,实现了一个在低价PC上就能实时创建和显示全景图像的软件。