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.

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.

基于改进SIFT算法的城市航拍图像快速拼接方法

城市航拍图像在城市规划、土地管理、环境监测和基础设施建设等领域具有广泛应用。针对无人机航拍高度越高,图像捕获成本越高,图像质量或能见度越低的问题,使用低空无人机来大量捕获图像;针对经典的尺度不变特征转换(SIFT)图像拼接算法存在匹配稳定性差、拼接质量差的问题,提出一种改进SIFT算法,通过提取图像金字塔模型,提高了匹配的稳定性;采用RANSAC算法减少局外点的干扰,提高图像拼接质量;采用混合平均加权法消除重叠区域接缝,最终实现了图像快速精准拼接。仿真实验结果显示,改进后的SIFT算法在图像拼接稳定性和质量上均表现较好,能获得良好且完整的拼接图像。

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.

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.

基于SIFT-GMLBP的动态图像视觉信息提取研究

为了精确地提取动态图像特征,为动画设计师提供更全面、更准确的视觉信息,文中提出基于SIFT-GMLBP的动态图像视觉信息提取方法。以关键点为像素中心,采用局部二值模式(LBP),通过比较其与邻域的灰度值获取LBP码,实现动态图像局部纹理特征捕捉;根据网格化LBP(MLBP)进一步将动态图像中的像素邻域划分为多个网格,使每个网格产生一个LBP值,降低特征向量的维数;结合Gabor滤波器,通过多尺度和多方向的纹理分析,提取动态图像在不同频率和方向上的局部结构信息,整合所有Gabor滤波器响应图像的GMLBP特征,形成包含原始动态图像在不同尺度和方向上的丰富纹理信息的特征向量。实验结果表明:该方法提取的关键点数量和分布非常合理,具有较高的稳定性和动态信息捕获能力,且该方法每秒能够处理高达30帧的图像。

基于SIFT特征点提取算法的三维数字影像重建方法

针对在数字影像三维重建过程中,由于原始数据中存在噪声和失真等不足,导致特征匹配效率和精度较低的问题,提出基于SIFT(Scale-Invariant Feature Transform)特征点提取算法的三维数字影像重建方法。采用双边滤波算法对数字影像中的环境噪声实施消除处理,并保留数字影像的边缘信息,提高特征点提取精度;通过尺度不变特征转换(SIFT)算法对其提取特征点,得到数字影像的特征点对;将该特征点对作为初始面片,利用空间目标多视影像密集匹配方法,实现对数字影像的三维重建。实验结果表明,所提方法特征匹配效率和匹配精度高,且降噪能力强,生成的三维重建影像所需平均时间为26.74 ms。

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。

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。

SIFT与SURF在指纹识别系统中的性能对比

要实现指纹识别技术,必须使用图像特征的提取技术,尺度不变特征变换(ScaleInvariantFeature Transform,SIFT)和加速稳健特征(Speeded Up Robust Features,SURF)是目前运用比较广泛的两种图像特征提取算法。为验证哪种算法在指纹识别领域更适用,文章使用同一手指指纹的多张照片为图库,分别使用两种方法进行同一图自我匹配、指纹角度、范围不同图片匹配及较为模糊的图片匹配等实验。通过实验得出:虽然SIFT算法比SURF算法耗时更长,但是使用SIFT算法的运算量小于SURF算法,因此两种算法花费的时间近乎相等;在指纹对比库的创建方面,SIFT算法要优于SURF算法;在指纹匹配识别过程中,无论是同一张图片自我匹配,还是指纹角度和范围不同的图片进行匹配、指纹不清晰的图片进行匹配,SIFT算法的成功匹配点比SURF算法的成功匹配点分布更均匀,且数量相近或更多。可见,在指纹识别系统中,使用SIFT算法比SURF算法更合适。

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

Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.

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.

一种针对于描述子的SIFT简化方法

由于目前的SIFT(scale invariant feature transform)特征提取算法具有较高的时间复杂度,不利于大规模的数据存储和搜索,提出一种简化的SIFT局部图像特征提取算法。改进的SIFT算法针对于描述子生成部分进行简化,将原算法中特征点描述子的矩形区域改为圆形区域,并将RANSAC(random sample consensus)算法应用于SIFT特征匹配中,有效地剔除错误匹配点。采用K.Mikolajczyk的衡量方法,即查全率和错误率进行评估。实验结果显示,算法在旋转、光照、视角变化等情况下都有很好的匹配效果,并且降低了时间复杂度。

基于改进SIFT算法的图像匹配

为进一步提高SIFT匹配算法的鲁棒性和正确率,从以下几个方面改进SIFT算法。对图像进行多分辨率小波变换,重建图像近似成分——低频信息参与匹配;采用"回"字形双层方邻窗将特征点邻域区域划分成四部分,建立32维特征点描述符向量;运用欧式距离初步确定匹配点,再用积分图像进一步剔除由于特征点具有空间相似性而出现的误匹配点,从而提高匹配精度。实验表明,本文算法在匹配精度和匹配时间上有明显提高,特别是当图像具有较多局部相似特征时,匹配点数增加,匹配正确率提高。

一种简化的SIFT图像特征点提取算法

针对目前尺度不变的图像特征点提取算法计算量较大,算法较复杂的问题,提出一种简化的SIFT图像特征点提取算法。此算法通过改变金字塔尺度空间的结构实现对SIFT特征点提取过程的简化,通过改变特征点描述子的结构实现对特征向量计算的简化,从而在保证算法鲁棒性的同时减少了计算量并增强了实时性。实验证明了该算法的有效性。

改进的SIFT特征提取和匹配算法

针对月球影像和尺度不变特征变换(SIFT)算法的特点,在改进SIFT特征提取算法的基础上,提出了一种稳健的图像自动匹配策略。首先,自动调整SIFT算法中的对比度控制系数,提高关键点提取的均衡性;然后,用SIFT描述向量之间的欧氏距离最小值与次小值的比值作为阈值,进行粗匹配,并利用匹配对主方向角度差直方图剔除部分伪匹配;最后,自动计算随机采样次数、误差容忍度等参数,使用改进的随机选取一致性(RANSAC)方法确定透视变换模型初始参数,并用该模型和误差容忍度对匹配集合中的匹配对进行校验,选取出正确的匹配对。实验结果表明,在图像有一定程度的视点、光照、旋转、比例、模糊和对比度变化等情形下,该算法稳定、可靠。该方法能有效解决图像匹配门限的选择问题,真正实现了无人工干预的自动匹配。

改进SIFT算法在文字图像匹配中的应用

使用SIFT算法对文字图像进行特征提取时,产生的特征点数目较少,且不同文字产生的特征向量存在强干扰性,导致匹配准确率较低。为此,提出一种改进的SIFT算法。该算法利用二值化图像代替灰度图像,增加特征点数目,并取消SIFT的旋转不变性。实验结果证明,与标准SIFT算法相比,改进SIFT算法能有效提高文字图像匹配的准确率。

Contourlet-SIFT特征匹配算法

基于局部特征的匹配算法中SIFT(Scale Invariant Feature Transform)算法性能好,应用广泛,但其描述子的维度高、匹配耗时大,对局部相似区域的匹配鲁棒性差。为此,该文提出一种Contourlet-SIFT特征匹配算法。在尺度空间下提取旋转不变特征,对特征及其邻域进行Contourlet变换,由各方向子带分解系数的均值和标准差构建全局纹理描述向量,根据向量间欧氏距离的大小进行特征点排序,选取距离较小的前1%的特征再进行SIFT最近邻比值匹配。实验结果表明该算法对亮度差异大、相似区域多的图像的匹配性能优于SIFT,在保证尺度、旋转、视角等不变性与SIFT相当的同时,匹配速度大为提升。

基于旋转不变特征的SIFT描述子在图像配准中的应用

针对尺度不变特征变换(SIFT)算法中描述子维度高造成配准过程中计算量过大的问题,提出了一种改进的SIFT算法。该算法利用圆形的旋转不变性,以特征点为中心,在近似大小的圆形特征点邻域内构造特征描述子,以每个圆环作为一个子环,每个子环内只有像素位置发生了改变,像素之间其他相对信息是保持不变的。当图像发生旋转时,统计每个圆环内元素的梯度累加值进行排序,生成特征向量描述子,降低了算法的维度及复杂度,把特征描述子的维数从128维降低到48维。实验结果表明,改进算法旋转配准重复率在85%以上;在图像旋转、缩放和光照变化情况下,与SIFT算法相比,平均配准准确率提高5%,平均配准耗时降低30%左右,有效实现了对SIFT的改进。