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.

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.

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.

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.

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。

基于遥感技术的水利工程施工环境污染监测方法研究

为了有效监测水利工程施工环境污染,提出基于遥感技术的水利工程施工环境污染监测方法。采用黑塞矩阵方法计算环境遥感图像中端元数量,获取施工环境积分图像,并通过尺度不变特征转换算法建立积分图像尺度空间金字塔,在尺度空间金字塔中,使用Hessian矩阵获取污染区域的响应阈值,提取施工环境污染特征,建立WEB网络服务端,实现水利工程施工环境污染监测。通过实验结果表明,所提方法可以全面的反应河流水质的分布变化,悬浮物、油膜、工业废液对水质的污染清晰可见;基于遥感光谱指数取值范围,明显监测出土壤中石油污染异常区域。

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。

基于图像特征的汉字字形相似度计算

汉字作为一种数量众多,结构复杂的象形文字,其字形的相似度计算对汉语的错别字识别和纠正具有重要意义。将汉字视为图形,通过尺度不变特征变换算法提取汉字的特征点,并利用汉字间特征点的对应关系,提出了一种基于特征点的相似度计算方法。对《通用规范汉字表》的8105个汉字进行了相似度分析,结果表明,该方法和人的认知结果比较一致。

Color Correction for Multi-view Video Using Energy Minimization of View Networks

Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost, and one of the fields where they are expected to be used more actively in the near future is in image-based rendering （IBR）. Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed. Color correction usually involves two steps： the first is to adjust camera parameters such as gain, brightness, and aperture before capture, and the second is to modify captured videos through image processing. This paper deals with the latter, which does not need a color pattern board. The proposed method uses scale invariant feature transform （SIFT） to detect correspondences, treats RGB channels independently, calculates lookup tables with an energy-minimization approach, and corrects captured video with these tables. The experimental results reveal that this approach works well.

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.

基于关键点精确配对的点云曲面匹配方法

针对基于特征的点云曲面匹配方法在关键点匹配时匹配效率低和精度不够的问题,提出了一种基于关键点精确配对的点云曲面匹配方法。通过采用基于曲率信息的改进3D-SIFT(3D scaleinvariant feature transform)算法,提取点云数据的关键点;将关键点处的FPFH(fast point feature histograms)特征描述以及模型中心点到关键点的向量与模型主趋势的夹角作为约束条件,获取精确的关键点匹配点对集合;求解刚体变换参数实现模型曲面的初始匹配;使用ICP(iterative closest point)算法进行二次优化,实现模型曲面的精确匹配。实验表明:该方法既能解决关键点匹配精度问题,又能很好地解决匹配效率的问题。

基于开源GIS数据的广域小重叠卫星影像定位模型精化

设计了一种适用于大区域、小重叠卫星影像有理多项式模型(RPC)定位精度优化的技术流程,基于开源数字正射影像(DOM)、数字高程模型(DEM)数据和RPC几何模型约束,通过优化尺度不变特征变换(SIFT)特征提取与匹配算法,实现了无像幅约束的控制点和连接点的快速稳健匹配,应用DEM支持下的RPC模型二维区域网方法实现了多景影像RPC模型精度的联合精化。GF-1卫星WFV影像的实验结果表明:在DEM支持下经过区域网平差后,RPC模型的定位精度可达到像素级水平。

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。

多层次特征和粒子群优化的场景分类

针对遥感图像的场景分类精度问题,提出多层次特征和粒子群算法优化分类器的场景分类算法。利用聚集局部描述符编码算法对尺度不变特征变换算法提取的局部特征编码,获得中层特征,通过卷积神经网络提取高层特征,将提取的特征作为支持向量机的输入数据,引入粒子群算法优化该分类器的参数,进行场景分类。在RSC11和WHU-RS19两个公开的遥感图像数据集上进行实验,分类精度分别达到95.28和97.20。将WHU-RS19数据集的结果与其它方法比较,精度有明显提高。实验结果表明,在分类时对分类器参数进行优化,分类效果更佳。

基于图像特征的纱线条干均匀度实时检测

为能够及时地获取纺纱过程中纱线质量变化信息,为优化纱线工艺参数服务,提出一种实时检测纱线条干均匀度的测量方法。为提高测量效率,首先使用最小外接矩形对实时采集的连续纱线图像进行感兴趣区域(region of interest,ROI)获取,对获取纱线图像根据纱线方向进行自动裁剪。其次基于最大类间方差法(OTSU)阈值分割及形态学运算获得的纱线条干二值图像。最后利用尺度不变特征转换(scale-invariant feature transform,SIFT)算法检测特征点对重合部分数据去除,保证纱线数据的完整及连续性,提出加入纱线倾斜角计算纱线直径方法。基于此方法计算对3种线密度的紧密纺纯棉纱的纱线直径并对纱线条干及纱疵进行检测。实验测量直径平均误差为2.38%,变异系数(coefficient of variation,CV)值与Uster Classimat5条干测试仪测试结果基本一致,表明此方法可满足纱线运动速度为5~30 m/min的纱线条干均匀度实时检测。