Novel registration algorithm for 3-D images captured from multiple views of object surface

A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.

A hybrid genetic algorithm for multi-modal image registration

This paper describes a new method for three-dimensional medical image registration. In the interactive image-guided HIFU （ High Intensity Focused Ultrasound） therapy system, a fast and precise localization of the tumor is very important. An automatic system is developed for registering pre-operative MR images with intra-operative ultrasound images based on the vessels visible in both of the modalities. When the MR and the ultrasound images are aligned, the eenterline points of the vessels in the MR image will align with bright intensities in the ultrasound image. The method applies an optimization strategy combining the genetic algorithm with the conjugated gradients algorithm to minimize the objective function. It provides a feasible way of determining the global solution and makes the method robust to local maximum and insensitive to initial position. Two experiments were designed to evaluate the method, and the results show that our method has better registration accuracy and convergence rate than the other two classic algorithms.

Accurate Registration of Remote Sensing Images Based on Optimized ORB Algorithms

As a branch of digital image processing, image registration technology has gradually become the basic key technology of image understanding and deep processing of computer vision after decades of development. In recent years, image mosaic technology has been widely used in medical image processing, computer vision, remote sensing image processing, virtual reality technology and other fields. Therefore, based on the optimized ORB algorithm, the author studies the precise registration technology of remote sensing images. The use of ORB algorithm for remote sensing image registration can effectively remove mismatch points and achieve accurate matching, thus achieving correct splicing. Moreover, the problem caused by the registration difference is greatly overcome to the registration.

Research on Detection Technology of Micro-Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm.The device detection model and data set are established based on Faster RCNN.Finally,the number of training was continuously optimized,and when the loss function of Faster RCNN converged,the identification result of the device was obtained.

A Mosaic Technique Based on Image Registration

osaic technique and image registration process are studied in this paper. By image registration, the correspondence between images of the same scene can be established. The aim of mosaic is to build up the scene panorama of a real image sequence of the same scene. So mosaic can be integrated by way of image registration. Here, some image registration method is given, and then affine transformation is taken. Finally, the image sequence is built into the panorama.

Image Registration Based on Improved Mutual Information with Hybrid Optimizer

An improved image registration method is proposed based on mutual infor- mation with hybrid optimizer. Firstly, mutual information measure is combined with morphological gradient information. The essence of the gradient information is that locations a large gradient magnitude should be aligned, but also the orientation of the gradients at those locations should be similar. Secondly, a hybrid optimizer combined PSO with Powell algorithm is proposed to restrain local maxima of mutual information function and improve the registration accuracy to sub-pixel level. Lastly, muhlresolution data structure based on Mallat decomposition can not only improve the behavior of registration function, but also improve the speed of the algorithm. Experimental results demonstrate that the new method can yield good registration result, superior to traditional optimizer with respect to smoothness and attraction basin as well as convergence speed.

Automatic registration of aerophotos based on SUSAN operator

The technique of image matching is the basis of image fusion,image mosaic and scene 3D reconstruction.In the paper a novel image registration method based on SUSAN operator is proposed.Firstly,Corner points are extracted by using SUSAN(Smallest Univalue Segment Assimilating Nucleus) operator.Then matched corner points are selected through coarse matching and fine matching.Based on such corner pairs,aerophotos are registered automatically.

基于特征点配准的真伪卷烟商标纸鉴别

为提高真伪卷烟商标纸鉴别的准确性和效率,降低鉴别的经验要求和主观性,提出一种基于特征点配准的真伪卷烟商标纸鉴别方法。使用一致的标准扫描采集卷烟样品图像,基于尺度不变特征转换算法提取图像特征点,通过特征匹配和基于单应性变换的图像配准获取判别预测变量。采用逻辑回归、梯度提升分类决策树算法构建二元分类模型对图像样本进行训练和评估。在64个卷烟规格、2 918个样本数据集上进行实验,该方法准确率高于95%。通过对比实验验证了该方法的稳定性和有效性。

基于SURF的遥感图像配准优化算法

针对遥感图像配准中易出现的信息量巨大和误匹配问题,通过改进SURF算法,提出了一种用于遥感图像配准的优化算法。优化算法引入特征点图像熵,通过去除携带信息量不足的误选取特征点,提高了特征点选取以及后续特征点匹配的准确性。在特征点描述阶段,算法实现了描述子降维,并将降维后的特征点描述子与FLANN算法相结合,降低了算法特征点误匹配率,同时减少了因描述子降维而带来的影响。实验表明,该优化算法相较于传统SURF算法,待配准图像特征点选取率下降10.9%,特征点误匹配率下降17.5%,并得到精确的遥感图像配准效果。

Rapid and robust medical image elastic registration using mean shift algorithm

In landmark-based image registration, estimating the landmark correspondence plays an important role. In this letter, a novel landmark correspondence estimation technique using mean shift algorithm is proposed. Image corner points are detected as landmarks and mean shift iterations are adopted to find the most probable corresponding point positions in two images. Mutual information between intensity of two local regions is computed to eliminate mis-matching points to improve the stability of corresponding estimation correspondence landmarks is exact. The proposed experiments of various mono-modal medical images. Multi-level estimation （MLE） technique is proposed Experiments show that the precision in location of technique is shown to be feasible and rapid in the

基于轮廓点相似性测度的2D-3D医学图像配准算法

针对传统配准算法无法适用于成像模糊、对比度低的X光医学图像的问题,本文提出一种基于轮廓点相似性测度的配准技术.首先引入分块双阈值增强策略来提取DRR图像和X光图像的边缘轮廓信息;其次,采用高斯加权欧氏距离计算图像轮廓的相似度;最后通过平衡优化器算法进行迭代优化,得到最优的位姿参数.实验结果表明:本文算法能够精确提取模糊X光图像的边缘轮廓信息,而且可以准确评估其与CT数据的相似度,平均配准成功率超过94%,算法效率和鲁棒性优于传统算法,可用于医疗诊断、放射疗法、图像引导手术等医学活动.

基于自适应变异的变步长天牛须算法及其在图像配准中的应用

针对现有遥感技术缺乏光谱信息而导致图像失真度高、成像模糊等问题,文章提出基于倾斜摄影技术提高遥感图像分辨率的方法。传统的天牛须算法(beetle antennae search algorithm,BAS)在处理图像匹配时,虽然具有参数少、收敛速度快、易于实现等优点,但是精度有限。因此以步长指数衰减的方式进行变步长搜索,同时借鉴粒子群算法寻优策略,引入天牛左右须历史最佳位置作为下一步解的搜索参考位置,以此提高解的搜索速度。与传统的天牛须智能优化算法相比,该文提出的基于自适应变异的变步长天牛须算法求解精度更高,通过实验验证了该算法的有效性。

基于融合式PC-ORB的异源图像配准算法

异源图像配准中,由于图像的成像机理差异,图像像素强度关联和旋转畸变是不可避免的两大问题,针对图像像素强度关联问题,提出了基于辐射不变特征变换(radiation-variation insensitive feature transform,RIFT)的图像配准算法,对图像间像素关联差异小的图像对配准有良好的精度,但对旋转畸变图像会产生较多错误匹配。对于旋转畸变问题,传统的ORB(oriented fast and rotated brief)算法,对旋转图像的配准有一定的稳定性,但对于强度变化不明显的图像对,特征点检测质量较低,配准精度不理想。因此本文将相位一致性(phase consistency,PC)融合进ORB算法,利用相位信息代替传统的图像强度信息,再构造旋转不变性BRIEF特征描述子,对图像像素强度变化和旋转畸变均具有鲁棒性。用图像像素强度关联不明显的红外图像和可见光图像进行配准实验,本文算法针对不同旋转幅度的图像的配准精度较高,RMSE稳定在1.7~2.1,优于RIFT算法,在特征点检测数量、配准精度和效率等性能上均有良好性能。

基于BBF优化算法的无人机遥感图像配准研究

针对无人机遥感图像中的亮度、尺度、图像重叠等问题,利用BBF优化算法设计无人机遥感图像配准方法;根据无人机遥感成像原理,获取无人机遥感图像作为配准对象,通过图像融合、校正等步骤,完成初始遥感图像的预处理;提取无人机遥感图像的SIFT特征点和轮廓特征,利用BBF优化算法搜索当前图像与参考图像之间的特征匹配对,完成图像的特征匹配;最终通过粗匹配、精匹配和消除错误匹配点3个步骤,得出无人机遥感图像的配准结果;通过效果测试实验得出结论:优化设计方法的特征匹配点准确率为5.15%,漏配率为3.75%,显著优于传统配准算法。

基于特征点和SAR-SIFT算法的林权测绘地形图像几何配准

目前常规的测绘地形图像几何配准主要通过对单应性矩阵进行求解,从而实现图像配准,由于对待配准图像的灰度化处理程度较低,导致配准精度较差。对此,本文研究基于特征点和合成孔径雷达(SAR)-尺度不变特征变换(SIFT)算法的林权测绘地形图像几何配准方法。首先对SAR图像成像机理进行分析,在此基础上采用滤波算法对图像进行处理,并采用加权平均法对红、绿、蓝三原色(RGB)通道分量进行灰度化处理。然后结合SAR-SIFT算法,构建多层次的尺度空间,并结合海森矩阵对像素梯度值进行计算,生成特征描述子。最后通过对特征描述子的相似度进行计算,结合匹配阈值,实现图像特征点的匹配。实验结果表明:采用基于特征点和SAR-SIFT算法的林权测绘地形图像几何配准方法对图像进行几何配准时,算法的均方根误差值较小,具备较为理想的几何配准精度。

基于SURF算法的实时拼接研究

视频拼接有助于人们获得更高分辨率、大视野的场景。文中主要对视频拼接过程中的图像配准及图像融合的SURF(Speeded Up Robust Features)算法进行研究,详细地阐述了基于SURF特征实现图像拼接的每一个步骤,从而快速提高特征点的提取速度,提高了图像配准过程中的精度与准确率,加快了图像配准的速度,并提升图像融合的效果。实验结果表明,该算法能够有效地抑制视差伪影,其拼接图像无明显接缝,并且可以实现实时拼接,拼接的图像更加自然,拼接的视觉效果较好。

新型布谷鸟搜索算法及其应用

为了解决通常布谷鸟搜索算法在寻优过程中存在收敛速度慢、计算精度差等缺陷,提出了一种新型的布谷鸟搜索算法,并应用于图像配准问题。首先分析了基本布谷鸟搜索算法中的不足,利用信息分享、局部增强算子和新方式建立鸟巢来提高种群多样性、局部勘探能力和收敛速度。通过15个基准函数实验和2个医学图像配准实例,所有实验结果表明,改进算法具有更好的收敛速度、计算精度和鲁棒性。

基于深度学习的紫外成像仪的图像配准与融合

由于在高灵敏紫外成像仪中紫外光与可见光成像原理存在一定差异,现有的方法配准精度不高,融合效果不理想。针对以上问题,提出了一种基于AlexNet网络与果蝇优化算法(Fruit fly optimization algorithm, FOA)小波融合的紫外光与可见光的图像的配准融合算法。首先,对预训练的AlexNet网络的权值阈值进行微调,利用AlexNet网络对图像进行各层次的特征提取,配合空间变换矩阵,实现紫外光与可见光的高精度配准。其次,将配准后的紫外光与可见光图像输入到FOA优化后的小波算法模型中实现融合。仿真结果显示,所提方法配准精度远远高于工业要求且收敛速度快,融合图像的信息熵较高。

基于改进ORB和匹配策略融合的图像配准方法

针对Oriented Fast and Rotated Brief(简称ORB)算法在图像配准中不具有尺度信息、BRIEF算法不具有旋转不变性以及特征匹配精度低等问题,本文提出一种基于改进ORB和匹配策略融合的无人机航拍图像配准方法。首先,通过简化多尺度空间检测特征点,采用稳健的RootSIFT描述符进行特征描述,用来提升特征描述的稳定性。然后,结合距离比值、双向匹配和余弦相似性测度(Cosine Similarity)匹配策略获得初始匹配。最后,利用随机抽样一致性算法(RANSAC)确定最终的特征点匹配关系,计算空间变换参数,实现图像的精确配准。实验结果表明,该方法在图像尺度、旋转和光照变化方面均表现出良好的性能,并提高了图像匹配准确率和配准精度。