A reliable algorithm for image matching based on SIFT

A novel algorithm is presented to make the results of image matching more reliable and accurate based on SIFT (Scale Invariant Feature Transform). SIFT algorithm has been identified as the most resistant matching algorithm to common image deformations; however, if there are similar regions in images, SIFT algorithm still generates some analogical descriptors and provides many mismatches. This paper examines the local image descriptor used by SIFT and presents a new algorithm by integrating SIFT with two-dimensional moment invariants and disparity gradient to improve the matching results. In the new algorithm, decision tree is used, and the whole matching process is divided into three levels with different primitives. Matching points are considered as correct ones only when they satisfy all the three similarity measurements. Experiment results demonstrate that the new approach is more reliable and accurate.

Fast image matching algorithm based on affine invariants

Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of extended centroid （EC） to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications.

Image matching algorithm based on SIFT using color and exposure information

Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.

Modified SIFT descriptor and key-point matching for fast and robust image mosaic

To improve the performance of the scale invariant feature transform （ SIFT）, a modified SIFT （M-SIFT） descriptor is proposed to realize fast and robust key-point extraction and matching. In descriptor generation, 3 rotation-invariant concentric-ring grids around the key-point location are used instead of 16 square grids used in the original SIFT. Then, 10 orientations are accumulated for each grid, which results in a 30-dimension descriptor. In descriptor matching, rough rejection mismatches is proposed based on the difference of grey information between matching points. The per- formance of the proposed method is tested for image mosaic on simulated and real-worid images. Experimental results show that the M-SIFT descriptor inherits the SIFT＇ s ability of being invariant to image scale and rotation, illumination change and affine distortion. Besides the time cost of feature extraction is reduced by 50% compared with the original SIFT. And the rough rejection mismatches can reject at least 70% of mismatches. The results also demonstrate that the performance of the pro- posed M-SIFT method is superior to other improved SIFT methods in speed and robustness.

基于图像声呐的里程计研究

为了探究基于图像声呐的里程计实现方法,文章借鉴了基于光学图像的里程计方法,将光学的线性投影模型改进成为更精确的非线性“投影”模型,使其更适合应用于声呐图像的情况。文章提出了从声呐图像到特征检测、特征匹配、运动估计并最终得到声呐里程计的实现思路。针对尺度不变特征匹配问题,结合声呐图像分辨力低且信噪比低的特点,提出了更稳健的匹配方法,有效地降低了特征点异常匹配出现的频率,通过半物理仿真实验验证了使用文中方法可以有效地得到声呐运动轨迹,为基于图像声呐的里程计研究提供了参考。

一种基于谱图SIFT的同源频谱监测数据判定方法

随着各类无线电应用的普及,在一定空间范围内,超短波监测过程中的监测数据易受到非同源的同频或邻频信号的影响,仅依靠常规监测中的频谱数据是无法判定信号是否同源的,因而不同监测站点获得的数据缺乏关联性,数据分析结果可能产生误导,降低工作效率。依据人工监测的经验,尝试用计算机视觉等技术分析监测数据的频谱图和时频谱图,结合谱图特性引入角度阈值改进SIFT算法的特征点匹配模式,以适应无线电监测数据分析的需要,并提出以图像特征点检测匹配率为前提,利用卡帕值综合评价两种谱图同源判定结果一致性的方法。通过实验模拟和实例验证,两种谱图同源判定结果的卡帕值为0.7605,达到高度一致;同时,所提方法在实践过程中有提高工作效率的作用,具备操作可行性和实际意义。

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。

球形全景核线约束的颜色不变量综合特征快速匹配方法

针对目前立体全景模型量测应用中特征匹配自动化程度偏低的问题,提出一种基于立体球形全景约束的颜色不变量综合特征快速匹配方法.首先,通过核线约束,使全景影像匹配的搜索范围从二维限制到一维带状缓冲区域;然后利用颜色不变量相关系数进一步确定精细搜索范围;最后,基于颜色不变量及旋转不变纹理特征构建综合匹配测度模型,以实现全景特征匹配.通过实地拍摄的全景图像,与灰度匹配法、SIFT,SURF以及CSIFT进行比较分析.实验结果表明,该方法匹配准确率提高了近10%,可消减球形全景特征的误匹配,有效地解决影像匹配时同名像点自动寻找与几何信息快速解算,为全景量测模型二次采样、量测及深度图的生成奠定基础.

Wide-baseline stereo matching based on multiple views

To solve the problem of wide-baseline stereo image matching based on multiple cameras,the paper puts forward an image matching method of combining maximally stable extremal regions (MSER) with Scale Invariant Feature Transform (SIFT) . It uses MSER to detect feature regions instead of difference of Gaussian. After fitted into elliptical regions,those regions will be normalized into unity circles and represented with SIFT descriptors. The method estimates fundamental matrix and removes outliers by auto-maximum a posteriori sample consensus after initial matching feature points. The experimental results indicate that the method is robust to viewpoint changes,can reduce computational complexity effectively and improve matching accuracy.

Content-Based Image Retrieval with Feature Extraction and Rotation Invariance

Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.

基于方差约束与几何不变特性的图像匹配算法

为解决较多图像匹配算法主要通过测量关键点之间的距离来实现特征匹配,忽略了图像的结构信息,使其存在较多的误匹配的问题,本文设计了方差约束耦合几何不变特性的图像匹配算法。借助于Forstner算子计算像素点的兴趣值,以检测图像的特征;计算图像的梯度信息,获取图像的方向值,并切割图像特征的圆形邻域,从而获取扇形子域;以图像的方向值为基础,通过计算扇形子域中的灰度不变矩,输出对应的特征向量;引入区域方差函数,获取图像的结构信息,将其加入至图像特征的匹配过程,以约束欧式距离的测量结果,实现图像特征匹配;最后利用匹配点间的几何不变特性,对匹配特征去伪求真,优化匹配结果。测试数据表明:相对于已有的匹配技术,在对无变换图像、缩放图像以及旋转图像匹配时,所提算法拥有更高的匹配准确度,分别达到了96.56%、95.38%和93.52%。

三分支空间变换注意力机制的图像匹配算法

对于待匹配图像具有旋转、缩放、平移等空间几何变换的图像模板匹配任务,现有的算法耗时较长,且准确率不高。针对该问题提出一种高准确率、低运算成本的图像匹配算法,首先根据中心点与邻域点的像素差来寻找特征点,进行快速特征检测,然后以这些特征点为中心,并以快速特征检测所计算出来的旋转角截取出一定尺寸的图像块。再将这些图像块输入空间变换注意力模块的特征描述子提取网络,最后使用K最邻近算法计算两张待匹配图像特征描述子中匹配的特征。特征描述子提取网络中引入了空间变换注意力模块,网络在训练的时候着重对空间信息进行学习,故所提算法提高了具有较大空间变化图像匹配任务的准确率。在匹配时间方面,所提的匹配算法仅次于检测和匹配都使用快速特征检测算法的方法。在匹配准确率方面,所提算法匹配的准确率远远优于实验所比较的其他算法。

基于Hausdorff距离和变参差分进化的SAR图像配准

为了提高合成孔径雷达(SAR)图像配准性能,提出了一种基于变参差分进化算法的图像配准方法。首先,获取了原始图像和待配准图像样本。然后,建立了基于差分进化(DE)算法的SAR图像配准模型。通过交叉、变异和选择操作等进化方式获得最优匹配特征点对,并根据特征点对获得图像配准结果。为了进一步优化DE算法在SAR图像配准中的性能,对差分缩放因子引入自适应策略。采用变参DE算法进行图像配准,弥补了因差分缩放因子设置不当而导致配准精度降低的缺点。在DE算法的适应度函数选择上,选取图像特征的Hausdorff距离函数作为相似性度量方法。实验结果显示,相比尺寸不变特征变换(SIFT)算法、卷积神经网络(CNN)算法,该文算法能够获得更高的配准准确度,接近100%,且配准稳定性更高,均方根误差(RMSE)均值为0.506 7。

基于RFNA和改进LBD的镜像线特征匹配方法

针对物体和镜像之间的匹配问题,引入RNFA(Relative Number of False Alarms)边缘链检测方法获取更丰富的线段。文中提出一种改进的LBD(Line Band Descriptor)算法用于构建局部不变特征描述符,通过比较局部不变特征描述符获得初始匹配对。采用全局投影角度的筛选方式,并通过拟合投影中线的方式剔除初始匹配对中误匹配项。在完成全局投影角度的选取和投影中线的拟合后,放宽对局部不变特征描述符阈值的筛选以获得更多的匹配对,提升召回率。图像集仿真实验结果表明,文中所提算法在纹理较弱区域能够更好地识别线段,且能够在保证原算法性能的基础上获得更多的匹配对,提高5%左右的正确匹配率,并达到90%以上的召回率。

基于地物不变性的侧扫声纳条带图像匹配方法

为解决侧扫声纳条带图像拼接时共视目标的扭曲和错位问题,提出了基于地物不变性的特征点匹配方法。该方法对侧扫声纳图像重叠区域进行符合地物不变性的特征点提取、K-均值分区和薄板样条弹性变换等处理,实现匹配点对的精确配准。采用某海域实测侧扫声纳数据对算法进行了验证,结果表明,特征点匹配效率和准确率高于传统的RANSAC算法,配准精度达到0像素偏差,拼接后的图像能够准确反映海底地貌特征,对大面积、高质量的海底地形地貌图像获取有重要意义。

基于图论的激光图像误匹配点自动探测

为了有效提取特征点,提升误匹配点自动探测效果,提出一种基于图论的激光图像误匹配点自动探测方法。利用尺度不变特征变换检测激光图像极值点,完成特征点提取,归一化处理特征点,计算提取的特征点间的距离,按照距离为提取特征点构建激光图像完全图;建立激光图像导出图,通过迭代处理导出图,自动探测误匹配点。实验证明:该方法可有效提取激光图像特征点;在不同旋转角度时,该方法自动探测误匹配点的粗差误判率较低、内点比例较高,最高粗差误判率仅有6.8%,最低内点比例84.8%,说明该方法具备较优的误匹配点自动探测效果。

AGV机器人多角度静态识别图像智能拼接方法

传统多角度静态图像识别拼接方法存在图像拼接精准度低、识别准确率低和拼接时间、识别时间长的问题,对此,文章提出了AGV机器人多角度静态识别图像智能拼接方法。对中心矩进行了归一化处理,获得多角度不变矩,利用不变矩对静态图像进行特征提取;采用对数几率回归分类算法进行分类;采用最大似然法估算图像中的参数;利用梯度下降法判定参数最小值,得到图像识别结果;利用AGV机器人,将图像的识别结果投影到变换矩阵中,将矩阵中的重叠图像划分成一个缝合部分和两个过渡部分;通过多分辨率图像融合算法和加权平均图像融合算法对这三个部分进行融合,得到完整的图像,实现静态图像智能拼接。实验结果表明,该方法的拼接精准度较高、识别准确率始终在80%以上,拼接时间最高仅为36 s,识别时间始终低于20 s,该方法的准确性高、实时性强。

基于ASIFT与Harris改进的影像仿射不变性算法研究

通过ASIFT算法,提取出其算法中的仿射不变模型Affine算子,再利用Harris算子进行影像特征检测。通过两种算子的结合得出仿射不变模型的解算参数,使得影像的匹配算法具有仿射不变性。利用无人机光学影像对改进算法进行影像仿射不变性实验。实验结果表明:改进的Affine-Harris算法与Harris算法相比,对于影像的匹配,在仿射不变性上优于原算法。

图像特征点提取及匹配技术

针对图像特征提取与匹配的适应性和准确性问题,将尺度不变特征变换算法(SIFT)应用到图像的特征点提取与匹配中。SIFT算法可在尺度空间寻找极值点,提取对图像尺度和旋转变化具有不变性,对光照变化和图像变形具有较强适应性的特征点及其特征描述。首先,采用SIFT算法提取图像的特征点及其描述,然后,采用基于置信度的匹配算法进行特征点的匹配,以找到图像间准确的匹配点对。最后,对不同光照条件、焦距、拍摄角度获取的图像进行特征点的提取及匹配。实验结果显示,本文算法对图像的光照、平移、旋转变换具有很好的适应性和准确性,能够提升匹配的自动化水平和准确度,准确率超过90%。结果表明,提出的算法可以进一步应用到图像识别,图像重建等领域。

采用简化SIFT算法实现快速图像匹配

SIFT(Scale Invariant Feature Transform)算子因其良好的尺度、旋转、光照等不变特性而广泛应用于图像匹配中,但用128维向量来表征每个特征点降低了算法的实时性。为了提高匹配速度,介绍了一种基于SIFT的简化算法(SSIFT),采用基于圆形窗口的12维向量有效地表示一个特征点。实验结果显示,算法在保持较好匹配率的同时能降低时间复杂度,适合运用在对实时性要求较高的场合。