Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Active Shape Models Using Scale Invariant Feature Transform

A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.

Mosaic of the Curved Human Retinal Images Based on the Scale-Invariant Feature Transform

To meet the needs in the fundus examination,including outlook widening,pathology tracking,etc.,this paper describes a robust feature-based method for fully-automatic mosaic of the curved human retinal images photographed by a fundus microscope. The kernel of this new algorithm is the scale-,rotation-and illumination-invariant interest point detector & feature descriptor-Scale-Invariant Feature Transform. When matched interest points according to second-nearest-neighbor strategy,the parameters of the model are estimated using the correct matches of the interest points,extracted by a new inlier identification scheme based on Sampson distance from putative sets. In order to preserve image features,bilinear warping and multi-band blending techniques are used to create panoramic retinal images. Experiments show that the proposed method works well with rejection error in 0.3 pixels,even for those cases where the retinal images without discernable vascular structure in contrast to the state-of-the-art algorithms.

Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.

Face recognition using SIFT features under 3D meshes

Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform(SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis(PCA).Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.

Target classification using SIFT sequence scale invariants

On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.

基于SIFT特征点提取的ICP配准算法

为解决传统迭代最近点(ICP)算法对点云配准的起始点对选择不佳而导致配准时间长、效率低的问题,提出一种基于尺度不变特征变换(SIFT)特征点提取的ICP点云配准算法(ST-ICP)。首先使用SIFT算法进行原始点云与目标点云的SIFT特征点提取,根据提取特征点完成快速点特征直方图(FPFH)特征运算,通过采样一致性初始配准算法(SAC-IA)搜索对应点对、求解变换矩阵,再进一步运用ICP算法进行点云精细配准。实验结果表明:与ICP算法相比较,ST-ICP算法的配准误差在迭代次数为5次时减小了1.019 cm,迭代次数为10次时减小了0.443 cm;在配准误差达到10^(-2) cm级别时,ST-ICP算法所用时间比传统ICP算法减少了12.829 s。ST-ICP算法优化了对应点对的选择,提升了配准精度和配准效率。

基于FPDE-SIFT的声呐干涉图像配准方法

图像配准是声呐进行高精度干涉测量的保障,该文针对水下目标的声呐图像配准,提出了一种基于4阶偏微分方程尺度不变特征变换的声呐干涉图像配准方法。该方法聚焦声呐图像配准的难点,首先基于4阶偏微分方程构建尺度空间,在保持图像细节的前提下滤除噪声,提高特征提取的准确度;对于残余噪声造成的特征点误检,借助特征点的相位一致性信息加以筛选,精简特征点样本集;最后对特征点匹配策略进行优化,提出改进的快速样本一致性匹配策略剔除特征点的误匹配。算法增加了匹配点对的数量,提高了匹配点对的准确度,实现了声呐干涉图像的精确配准。水池实验和外场试验表明,该文所提出的算法相较现有算法对声呐图像有着更好的适用性,配准后的均方根误差与留一法均方根均小于1像素,达到了亚像素配准精度。

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。

Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

基于内容的卫星图像配准是在遥感和图像处理领域的一大难题。受照度、旋转、来源差异的影响,该问题在多源遥感图像匹配中更为突出。尺度不变特征变换(scale-invariant feature transform,SIFT)算法是一种成功应用于卫星图像配准的算法。本地SIFT描述符被许多研究者应用于改进图像检索流程。尽管SIFT算法具有良好的稳定性,它在提取多源遥感中本地特征点的质量和数量上仍然具有一定的劣势。另外,SIFT算法提取的本地特征具有较高维度,导致计算过程耗时过长以及对保存相关信息的储存空间要求过高,而这两点也是在基于内容图像检索(content-based image retrieval,CBIR)的相关应用中的重要因素。本文介绍了一种在多源遥感中将本地SIFT特征转变为全局特征的新方法。通过在预处理阶段对图像进行对比度均衡化来提升SIFT本地特征点质量和数量。将参考数据库中每副图像的本地特征单独分为一类后,采用线性判别分析(linear discriminant analysis,LDA)方法将本地SIFT特征转变为全局特征,同时不为降低特征空间的维度。该方法可以显著减少计算时间和所需存储空间。将核函数应用于检定数据并映射,所测试特征点的检索率高达91.67%。

Algorithm Based on Morphological Component Analysis and Scale-Invariant Feature Transform for Image Registration

In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance,we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.

SIFT算法在机器视觉轨道交通变形监测中的应用分析

针对传统人工测量方法不能满足轨道交通结构变形监测精度要求高、实时性要求强等问题,本文提出一种尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)算法,基于该算法可实现机器视觉对轨道交通结构变形实时监测,同时基于MATLAB语言构建机器视觉轨道交通变形监测系统,并对该系统监测结果进行精度评估。结果表明,基于SIFT算法构建的机器视觉轨道交通变形监测系统与传统测量结果有较好的一致性,总体精度优于1mm。总之,SIFT算法可较好地匹配特征点信息,能够用于实现机器视觉轨道交通结构变形监测,同时机器视觉轨道交通结构变形监测系统在自动化程度、实时性、成本方面具有显著优势,拥有广阔的应用前景。

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EOSHHO-SIFT的混合域鲁棒图像水印算法

针对含水印信息的图像对几何攻击鲁棒性不强的问题,提出一种基于改进的尺度不变特征变换(Elite opposition-learning strategy based Harris hawks optimization and Scale-invariant feature transform, EOSHHO-SIFT)的图像水印算法。首先,水印预处理:对水印进行基于奇异值分解的预处理,得到需要嵌入奇异矩阵的信息;其次,载体预处理:对载体图像进行HAAR多小波变换,并在变换后的载体图像中使用EOSHHO-SIFT算法进行特征点提取;再次,水印嵌入:上述特征点作为鲁棒水印的嵌入区域,而用EOSHHO-SIFT优化后的特征点进行鲁棒水印的嵌入;最后,水印提取:利用HAAR以及EOSHHO-SIFT提取出含有水印信息的特征点,并利用此特征点所含的奇异值信息对水印进行重组。仿真结果表明,与其它不具有旋转不变特性的图像水印算法相比,算法具有较强的抗噪声、抗滤波以及抗几何攻击的能力。

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.

联合SIFT-GPU和整体平差法的隧道影像拼接研究

针对当前隧道影像拼接过程中效率低、累积误差大的问题,研究提出一种联合SIFT-GPU(scale-invariant feature transformgraphics processing unit)协同处理算法和整体平差法的隧道影像拼接技术。首先,基于车载移动测量技术获取隧道内部的影像和点云数据,利用点云数据和共线方程对原始影像进行纠正处理,并先后使用MASK匀光算法和Wallis滤波匀光法对影像进行匀光匀色;然后,融合SIFT匹配算法和GPU-CPU(central processing unit)协同处理技术加速提取所有待拼接影像特征点,使用RANSAC(random sample consensus)算法剔除误配点;最后,使用平移旋转缩放变换模型对400张影像进行拼接,采用整体平差算法对拼接结果进行误差补偿,利用GDAL(geospatial data abstraction library)分块处理技术输出拼接成果。研究结果表明:1)相较于原始的SIFT匹配算法,研究使用的SIFT-GPU协同处理算法使影像匹配效率提高了3~25倍;2)整体平差算法较好地解决了随着待拼接影像的增加而引起的误差累积问题,相较于传统算法,误差降低至原来的1/3。

SIFT-based automatic tie-points extraction for airborne InSAR images

The scale-invariant feature transform (SIFT) is often applied to extract tie-points for airborne SAR images. When a pair of airborne SAR images differs with look angles obviously, shadow sizes and shapes of same objects will differ obviously. In main and slave SAR images, key-points around shadows often match as tie-points, although they are not homologous points. The phenomenon worsens the performance of SIFT on SAR images. On the basis of SIFT, a modified matching method is proposed to decrease the number of incorrect tie-points. High-resolution airborne SAR images are used in Experiments. Experiment results show that the proposed method is very effective to extract correct tie-points for SAR images.

多聚焦图像离焦模糊区域的SIFT特征提取

常规的尺度不变特征变换(SIFT)图像特征提取方法难以提取多聚焦图像离焦模糊区域的特征,使得图像间存在局部、少量的公共特征,导致多聚焦图像配准精度差,严重影响后续图像融合和三维重建质量。在分析图像离焦模糊区域特征提取不确定性的基础上,提出了一种多聚焦图像离焦模糊区域的SIFT特征提取方法。首先提取多聚焦图像聚焦清晰区域的SIFT特征,再利用光流跟踪提取对应离焦模糊区域的SIFT特征,避免了在离焦模糊区域直接提取SIFT特征的不确定性。实验结果表明:提出的方法在离焦模糊区域具有良好的SIFT特征提取能力和提取精度,能实现多聚焦图像SIFT特征匹配数量显著增长,SIFT特征提取的误差为0.03~0.39 pixels,优于现有方法的0.21~1.71 pixels。降低了离焦模糊区域SIFT特征提取的不确定性,为多聚焦图像精确配准奠定了基础。

基于高维SIFT改进隐马尔可夫模型的多目标跟踪

针对多目标跟踪领域中出现的遮挡、目标身份互换等问题,本文提出了一种基于尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)关键点和隐马尔可夫模型的多目标跟踪算法。首先,在视频序列中逐帧提取每个目标的关键点集,并对其进行条件约束;其次,以得到的关键点集作为目标的状态建立隐马尔可夫模型,根据模型在时序段内传递状态的规律求出模型对应的参数;最后,以当前帧的观测状态和参数求出下一帧的隐性状态,实现对目标位置的预测。为了提升模型的推理速度,建立了表征全部目标的高维观测状态模型。与其他先进的算法在MOT17、MOT20、KITTI数据集上进行了仿真实验对比,结果表明本算法在跟踪准确度等指标上表现较优,并对遮挡和身份互换问题有较好的鲁棒性。

融合SIFT和级联分类器的特种车辆自动检测识别

针对特定场景中特种车辆因多环境影响因素下数据不均衡、检测精度和识别准确率低的问题,提出一种融合尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)和级联分类器的特种车辆自动检测及识别预测方法。首先,图像预处理后运用SIFT特征提取图像主体区域特征点及特征描述子;其次,结合SIFT特征点的密度调整优化算法实现目标车辆检测;最后,运用KMeans聚类算法获得目标检测框中SIFT特征描述子的中心聚类点,生成表征目标主体图像的128维特征描述子,并最终输入RF-RBF(Random Forest-Radial Basis Function)级联分类器进行学习并识别预测。该文均采用K折交叉验证方法保证模型的稳定性和可靠性。实验结果表明,在特定场景下特种车辆目标检测获得了75.47%平均交并比,级联分类器在特种车辆识别的综合准确率、精确率、召回率、F1-Score值以及FPS值分别为87.35%、88.17%、97.27%、92.38%以及21。进一步验证融合SIFT和级联分类器模型具有较好的自动化检测准确性和识别分类能力。