图像校准中的多元样条方法

图像校准是将两两具有公共部分的一组图像通过变换合成到同一个图像的过程.通常这组图像可以是从不同时间、不同传感器(成像设备)或不同条件(气候、亮度、摄像位置和角度等)下获取的.基于任意三角剖分上的多元样条函数,本文提出了图像配准的一种新方法.实验结果表明,与目前国际上流行的其他图像校准方法相比,本文方法计算简单并且得到的合成图像具有更为自然的边界过渡.     

利用B样条曲线设计指纹图像滤波模板和快速指纹特征匹配算法

在指纹图像识别过程中,指纹图像的滤波去噪音二值化和指纹特征的匹配是最关键的两个部分.本文针对这两部分的算法设计进行分析和改进,介绍利用B样条造型来设计指纹滤波模板的方法和基于基准点附近的拓扑结构进行快速特征匹配的算法.并且利用改进后的指纹特征匹配算法,在样本指纹库中进行实验,结果表明该算法大大提高了指纹特征匹配的效率和准确性.     

SIFT算法在图像拼接中的应用研究

SIFT算法在计算机视觉中具有广泛的应用,SIFT是一种稳定的图像局部'特征匹配算法,该算法具有非常高的唯一性和稳健性,对于图像的亮度变化、尺度缩放、旋转变化和噪声等具有很好的鲁棒性.通过研究SIFT算法的基本原理和步骤,利用pycharm-community-2019.3.2开发工具、python3.7.9对SIFT算法进行仿真实验,通过图片检测图像的关键点,并实现对图像进行拼接.     

碎纸片的拼接复原方法研究

本文研究了来自同一页印刷品的中、英文文字文件,在规则的横、纵切割方式下,碎纸片的拼接复原方法.文中利用碎纸片图片边缘灰度值数据的匹配度和行间距、字高等文字排版数据,建立了一系列拼接复原模型.首先对仅纵切方式下的碎纸片拼接问题,建立了基于碎片边缘匹配的图像拼接复原模型,根据碎片边缘灰度值向量间的距离来衡量两张图片的匹配度.然后对中、英文碎纸片分别提出基于像素点投影和灰度计数的分类方法,将处在同一行的碎片分为一类,使用拼接复原模型完成行内的拼接.最后通过行间距信息完成了行与行之间的匹配,从而完成了整份文件的拼接复原.     

一种基于时域和频域特征的图像拼接方法

1 引言图像拼接是指将具有部分景物重合的图像进行无缝拼合,生成一张具有较宽视角的高分辨率图像的技术．现有的图像拼接技术主要有两类:基于区域的方法和基于特征的方法．基于区域的方法[1]通常是取参考图像中某一窗口中的点作为特征模板,建立描述     

基于方向多尺度变换和统计建模的纹理分类方法

纹理是图像分析和识别中经常使用的关键特征, 而小波变换则是图像纹理表示和分类中的常用工具. 然而, 基于小波变换的纹理分类方法常常忽略了小波低频子带信息, 并且无法提取图像纹理的块状奇异信息. 本文提出小波子带系数的局部能量直方图建模方法、轮廓波特征的Poisson 混合模型建模方法和基于轮廓波子带系数聚类的特征提取方法, 并将其应用于图像纹理分类上. 基于局部能量直方图的纹理分类方法解决了小波低频子带的建模难题, 基于Poisson 混合模型的纹理分类方法则首次将Poisson 混合模型用于轮廓子带特征的建模, 而基于轮廓波域聚类的纹理分类方法是一种快速的分类方法. 实验结果显示, 本文所提出的三类方法都超过了当前典型的纹理分类方法.     

基于多尺度局部结构约束的特征点匹配算法

对于含有重复模式较多、低重叠区域的图像,特征匹配时会存在离群点较多及伪同构等问题,从而影响匹配的精度.为了提高匹配算法的精度和鲁棒性,本文提出了一种基于多尺度局部结构相似性的特征点匹配算法(MLSS).首先定义局部结构相似性度量方法,即多尺度近邻结构相似性及多尺度局部拓扑结构相似性.基于局部结构相似性,构造特征匹配模型,并采用确定性退火思想去除离群点,实现特征点集的精确和快速的匹配.使用Daisy数据集、VGG数据集和航空遥感数据集中的22组典型数据对算法的性能进行验证,匹配结果显示,该算法的精度和鲁棒性优于其他四种经典的特征匹配方法.     

基于模糊控制的ASIFT图像特征优化算法

ASIFT算法是图像特征匹配的有效工具,具有很强的仿射不变性.在ASIFT算法的基础上,利用Nelder-Mead单纯形方法优化采样点,并通过模糊控制策略实现单纯形参数的自适应调整.针对一组低空遥感图像的实验结果表明,本文方法保持了ASIFT算法对仿射变换的鲁棒性,并且能获得比ASIFT和SIFT算法更多的特征匹配对.     

基于概率弹性匹配的多姿态人脸认证

为解决多姿态情况下的人脸认证问题,提出了基于概率弹性匹配的方法.首先从多尺度密集采样的图像中提取局部特征,并将局部区域的位置信息保存到特征向量中.然后用高斯混合模型在图像集中训练得到人脸特征的空间外观分布,并将人脸图像中的每一个局部区域表示成与其最相似的高斯分量,从而形成一个基于高斯混合模型表示的人脸特征.通过统计两个人脸特征向量的差向量来训练支撑向量机分类器从而实现多姿态人脸认证.在通用图像库中的实验验证了方法的有效性.     

基于PGH矩改进SURF算法的工业散料识别研究

针对工业散料识别过程中图像特征匹配率低的问题,提出一种基于PGH矩的改进SURF图像匹配算法.首先,研究Gaussian-Hermite矩,将其扩展到复数空间中,推导出Polar-Gaussian-Hermite矩;其次,利用升降算符法计算图像PolarGaussian-Hermite矩,获得新的特征向量;最后,将原始图像提取的特征点和模板图像进行准确匹配得到最优匹配结果.实验结果表明算法能够解决缩放、旋转和曝光情况下工件匹配问题,误匹配率8%左右,满足工业散料识别系统的准确性和实时性要求.     

基于Fourier-Mellin变换的全景图像拼接与曲线匹配

基于Fourier-Mellin变换的图像配准方法,对于图像拼接与曲线匹配进行新探讨.该方法在不需要准确控制相机运动,不需要知道相机的焦距等内部参数及检测图像特征,在配准精度要求不高的情况下,直接生成的全景图像可以满足很多实际需求;先将图像曲线转化为二值图像,然后应用。Fourier-Mellin变换对这些二值图像进行配准,从而达到两条曲线的匹配.     

On a New Diffeomorphic Multi-Modality Image Registration Model and Its Convergent Gauss-Newton Solver

In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. A key feature of the model is its ability of guaranteeing a diffeomorphic transformation which is achieved by a control term motivated by the quasi-conformal map and Beltrami coefficient. The existence of the solution of this model is established. To solve the model numerically, we design a Gauss-Newton method to solve the resulting discrete optimization problem and prove its convergence; a multilevel technique is employed to speed up the initialization and avoid likely local minima of the underlying functional. Finally, numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation.     

低分辨率医学图像的多图谱分割方法

基于多图谱的图像分割方法因其分割精度高和鲁棒性强,在医学图像分割领域被广泛研究,主要包含图像配准和标签融合两个步骤.目前对多图谱分割方法的研究通常都是在图谱图像和待分割目标图像具有相同分辨率的情况下展开的.然而,由于受图像采集时间,采集设备等影响,临床实践中采集的影像大多是低分辨率数据,使得目前在影像研究中广泛使用的方法无法有效应用于临床实践.因此,针对这一问题,我们结合图像超分辨率恢复方法,提出了精确鲁棒的低分辨率医学图像的多图谱分割方法,实验结果显示提出的方法显著地提高了多图谱分割方法的分割精度.     

A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method

This paper presents panoramic unmanned aerial vehicle (UAV) image stitching techniques based on an optimal Scale Invariant Feature Transform (SIFT) method. The image stitching representation associates a transformation matrix with each input image. In this study, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between the images. An improved Geometric Algebra (GA-SIFT) algorithm is proposed to realize fast feature extraction and feature matching work for the scanned images. The proposed GA-SIFT method can locate more feature points with greater accurately than the traditional SIFT method. The adaptive threshold value method proposed solves the limitation problem of high computation load and high cost of stitching time by greater feature points extraction and stitching work. The modified random sample consensus method is proposed to estimate the image transformation parameters and to determine the solution with the best consensus for the data. The experimental results demonstrate that the proposed image stitching method greatly increases the speed of the image alignment process and produces a satisfactory image stitching result. The proposed image stitching model for aerial images has good distinctiveness and robustness, and can save considerable time for large UAV image stitching.     

The Momentum Map Representation of Images

This paper discusses the mathematical framework for designing methods of Large Deformation Diffeomorphic Matching (LDM) for image registration in computational anatomy. After reviewing the geometrical framework of LDM image registration methods, we prove a theorem showing that these methods may be designed by using the actions of diffeomorphisms on the image data structure to define their associated momentum representations as (cotangent-lift) momentum maps. To illustrate its use, the momentum map theorem is shown to recover the known algorithms for matching landmarks, scalar images, and vector fields. After briefly discussing the use of this approach for diffusion tensor (DT) images, we explain how to use momentum maps in the design of registration algorithms for more general data structures. For example, we extend our methods to determine the corresponding momentum map for registration using semidirect product groups, for the purpose of matching images at two different length scales. Finally, we discuss the use of momentum maps in the design of image registration algorithms when the image data is defined on manifolds instead of vector spaces.     

A robust multigrid approach for variational image registration models

Variational registration models are non-rigid and deformable imaging techniques for accurate registration of two images. As with other models for inverse problems using the Tikhonov regularization, they must have a suitably chosen regularization term as well as a data fitting term. One distinct feature of registration models is that their fitting term is always highly nonlinear and this nonlinearity restricts the class of numerical methods that are applicable. This paper first reviews the current state-of-the-art numerical methods for such models and observes that the nonlinear fitting term is mostly ‘avoided’ in developing fast multigrid methods. It then proposes a unified approach for designing fixed point type smoothers for multigrid methods. The diffusion registration model (second-order equations) and a curvature model (fourth-order equations) are used to illustrate our robust methodology. Analysis of the proposed smoothers and comparisons to other methods are given. As expected of a multigrid method, being many orders of magnitude faster than the unilevel gradient descent approach, the proposed numerical approach delivers fast and accurate results for a range of synthetic and real test images.     

A robust multigrid approach for variational image registration models

Variational registration models are non-rigid and deformable imaging techniques for accurate registration of two images. As with other models for inverse problems using the Tikhonov regularization, they must have a suitably chosen regularization term as well as a data fitting term. One distinct feature of registration models is that their fitting term is always highly nonlinear and this nonlinearity restricts the class of numerical methods that are applicable. This paper first reviews the current state-of-the-art numerical methods for such models and observes that the nonlinear fitting term is mostly ‘avoided’ in developing fast multigrid methods. It then proposes a unified approach for designing fixed point type smoothers for multigrid methods. The diffusion registration model (second-order equations) and a curvature model (fourth-order equations) are used to illustrate our robust methodology. Analysis of the proposed smoothers and comparisons to other methods are given. As expected of a multigrid method, being many orders of magnitude faster than the unilevel gradient descent approach, the proposed numerical approach delivers fast and accurate results for a range of synthetic and real test images.     

GRASP and path relinking hybridizations for the point matching-based image registration problem

In the last decade, image registration has proven to be a very active research area when tackling computer vision problems, especially in medical applications. In general, image registration methods aim to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images, which involves a combinatorial optimization problem. From this matching, the registration transformation can be inferred by means of numerical methods.     

A two-phase decision support framework for the automatic screening of digital fundus images

In this paper we give a brief review on the present status of automated detection systems describe for the screening of diabetic retinopathy. We further detail an enhanced detection procedure that consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on the severity of abnormalities. If an image is found to be seriously abnormal, it will not be analysed further with robust lesion detector algorithms. As a further improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions of interest with possible lesions on the images that previously passed the pre-screening step. These regions will serve as input to the specific lesion detectors for detailed analysis. This procedure can increase the computational performance of a screening system. Experimental results show that both two steps of the proposed approach are capable to efficiently exclude a large amount of data from further processing, thus, to decrease the computational burden of the automatic screening system.     

A fast approach of nonparametric elastic image registration problem

In this paper, we present a fast algorithm of the nonparametric elastic image registration using a simple implementation of the Range Restricted GMRES (RRGMRES) method. This approach differs from the others in the fact that it is specified to the tridiagonal block matrix type to resolve a nonsymmetric linear system. In what follows, we prove existence and uniqueness of minimizer of the elastic registration problem and present the corresponding discrete problem by employing a finite difference scheme. The accuracy of the proposed method is demonstrated on different image registration examples; we also show the speedup of the proposed approach by calculating the corresponding CPU time and compared it with the classical elastic registration method.