A Scale-Invariant Fifth Order WCNS Scheme for Hyperbolic Conservation Laws

In this article,a robust,effective,and scale-invariant weighted compact nonlinear scheme(WCNS)is proposed by introducing descaling techniques to the nonlinear weights of the WCNS-Z/D schemes.The new scheme achieves an essentially non-oscillatory approximation of a discontinuous function(ENO-property),a scaleinvariant property with an arbitrary scale of a function(Si-property),and an optimal order of accuracy with smooth function regardless of the critical point(Cp-property).The classical WCNS-Z/D schemes do not satisfy Si-property intrinsically,which is caused by a loss of sub-stencils’adaptivity in the nonlinear interpolation of a discontinuous function when scaled by a small scale factor.A new nonlinear weight is devised by using an average of the function values and the descaling function,providing the new WCNS schemes(WCNS-Zm/Dm)with many attractive properties.The ENO-property,Si-property and Cp-property of the new WCNS schemes are validated numerically.Results show that the WCNS-Zm/Dm schemes satisfy the ENO-property and Si-property,while only the WCNS-Dm scheme satisfies the Cp-property.In addition,the Gaussian wave problem is solved by using successively refined grids to verify that the optimal order of accuracy of the new schemes can be achieved.Several one-dimensional shock tube problems,and two-dimensional double Mach reflection(DMR)problem and the Riemann IVP problem are simulated to illustrate the ENOproperty and Si-property of the scale-invariant WCNS-Zm/Dm schemes.

Scale-Invariant Occupancy of Phase Space and Additivity of Nonextensive Entropy S_q

<Abstract>Phase space can be constructed for N equal and distinguishable binary subsystems which are correlated in a scale-invariant manner.In the paper,correlation coefficient and reduced probability are introduced to characterize the scale-invariant correlated binary subsystems.Probabilistic sets for the correlated binary subsys-tems satisfy Leibnitz triangle rule in the sense that the marginal probabilities of N-system are equal to the joint probabilities of the(N-1)-system.For entropic index q=1,nonextensive entropy Sq is shown to be additive in the scale-invariant occupation of phase space.

Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars,stones,or leaves.Optical recognition systems can help in preserving,sharing,and accelerate the study of the ancient scripts,but lack of standard dataset for such scripts is a major constraint.Although many scholars and researchers have captured and uploaded inscription images on various websites,manual searching,downloading and extraction of these images is tedious and error prone.Web search queries return a vast number of irrelevant results,and manually extracting images for a specific script is not scalable.This paper proposes a novelmultistage system to identify the specific set of script images from a large set of images downloaded from web sources.The proposed system combines the two most important pattern matching techniques-Scale Invariant Feature Transform(SIFT)and Template matching,in a sequential pipeline,and by using the key strengths of each technique,the system can discard irrelevant images while retaining a specific type of images.

井下危险区域目标检测

建立适合煤矿井下特殊环境的危险区域目标检测系统结构和新的目标匹配算法。新算法基于SIFT(scale-invariant feature transform)多尺度变换,结合形态学技术用降维后的局部区域匹配方法提高系统实时性;交叉匹配粗筛选后将RANSAC(random sample consensus)算法和L-M(Lev-enberg-Marquardt)非线性优化算法结合估计优化参数,解决现有算法计算复杂,匹配时间长,复杂环境匹配精度低的问题。实验证明,新算法对煤矿井下模糊、低照度、遮挡、高噪声和尺度变化等情况均具有良好的鲁棒性,解决多摄像机不同视角目标匹配问题,适合实时处理的监控系统中井下危险区域目标检测。

像方与物方相互结合的无人机航空影像匹配研究

提出了将像方与物方相结合的两阶段的无人机航空影像匹配方法,该方法可兼顾多视影像匹配可靠性和高精度空三解算的要求,同时也顾及了匹配效率。该方法利用无人机影像航带内重叠度大,影像间的变形对匹配影响小的优势,借助循序渐进的思路,在'像方'使用缩小影像快速开展多视影像匹配,同时采用光束法平差解算影像较为精确的外方位元素和计算地形数据(DSM)并对影像正射纠正。接下来,在'物方'利用正射影像开展二次匹配,这样不仅可以减少影像旋转、尺度缩放、放射变换、投影变形等对匹配的影响(特别是减小投影变形对航带间影像匹配的影响),提高了多视影像间匹配同名点的数量及分布均匀度,增强匹配的可靠性;在'物方',还保证了SIFT算法在同一影像尺度下提取特征点,提高了空三解算精度。通过选取不同地形类别的无人机航空影像试验验证,表明在'物方'采用正射影像开展多视影像匹配,航带间影像匹配点的数量明显提高(丘陵等平坦区域,点的数量增加约6倍;高山区域,点的数量增加约4~6倍,其中重叠度较小的影像间增加约4倍,重叠度较大的影像间增加约6倍),较传统的SIFT算法,空三解算精度提高约30%~40%。

基于SIFT和Affinity Propagation的遥感图像配准算法

随着传感器和光学影像测量等各种技术的快速发展,航空遥感技术已经在电力巡检、森林防火、地理测绘等领域中发挥着越来越重要的作用,而图像配准作为遥感图像的预处理步骤为图像融合等后续处理提供了参考和依据,目前已经成为遥感图像处理领域的研究热点。文中提出了一种基于尺度不变特征变换(Scale-invariant Feature Transform,SIFT)和仿射传播聚类(Affinity Propagation,AP)的图像配准算法。该算法与原有算法相比优势在于无需预先设定参数,并且实验仿真表明该算法能有效地对多源图像进行高精度的配准,与随机抽样一致算法(Random Sample Consensus,RANSAC)相比提高了正确匹配点的数目。

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.

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.

Evolution of crescent-shaped sand dune under the influence of injected sand flux:scaling law and wind tunnel experiment

This paper studies the evolution of crescent-shaped dune under the influence of injected flux. A scaling law and a wind tunnel experiment are carried out for comparison. The experiment incorporates a novel image processing algorithm to recover the evolutionary process. The theoretical and experimental results agree well in the middle stage of dune evolution, but deviate from each other in the initial and final stages, suggesting that the crescent-shaped dune evolution is intrinsically scale-variant and that the crescent shape breaks down under unsaturated condition.

A modified Lin equation for the energy balance in isotropic turbulence

At sufficiently large Reynolds numbers,turbulence is expected to exhibit scale-invariance in an intermediate("inertial")range of wavenumbers,as shown by power law behavior of the energy spectrum and also by a constant rate of energy transfer through wavenumber.However,there is an apparent contradiction between the definition of the energy flux(i.e.,the integral of the transfer spectrum)and the observed behavior of the transfer spectrum itself.This is because the transfer spectrum T(k)is invariably found to have a zero-crossing at a single point(at k=k*),implying that the corresponding energy flux cannot have an extended plateau but must instead have a maximum value at k=k*.This behavior was formulated as a paradox and resolved by the introduction of filtered/partitioned transfer spectra,which exploited the symmetries of the triadic interactions(J.Phys.A:Math.Theor.,2008).In this paper we consider the more general implications of that procedure for the spectral energy balance equation,also known as the Lin equation.It is argued that the resulting modified Lin equations(and their corresponding Navier–Stokes equations)offer a new starting point for both numerical and theoretical methods,which may lead to a better understanding of the underlying energy transfer processes in turbulence.In particular the filtered partitioned transfer spectra could provide a basis for a hybrid approach to the statistical closure problem,with the different spectra being tackled using different methods.

Scale-Invariance Exists in the Series of Character Intervals in the Four Great Chinese Novels

The scale-invariance behavior has been widely observed in English or other phonetic language texts. In the present study, we examine whether the semantic language, Chinese can also show this behavior. Typically, the scale-invariance behavior is examined in the series of character intervals for the four great Chinese novels by a method of detrended fluctuation analysis. We observe that the scale-invariance behavior characterized by a scaling exponent around 0.60 exists in each novel. Moreover, we divide each novel into three parts with equal number of chapters, and we also observe the existence of scale-invariance in the interval series for each part. Interestingly, we find that there is evident difference in the scaling exponents between the first(or second) part and the third part in the novel of A dream of red mansions, and the difference between parts is not evident for the other three novels. Our observation suggests that there are two writing styles in A dream of red mansions, which are consistent with current prevailing view that the first 80 chapters and the last 40 chapters were accomplished by Xueqin Cao and E Gao, respectively. Our method may shed light on the identification of writing styles in written texts.

Periocular Biometric Recognition for Masked Faces

Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.

Study of Human Action Recognition Based on Improved Spatio-temporal Features

Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.

Improved Global Context Descriptor for Describing Interest Regions

The global context(GC) descriptor is improved for describing interest regions,uses gradient orientation for binning,and thus provides more robust invariance for geometric and photometric transformations.The performance of the improved GC(IGC) to image matching is studied through extensive experiments on the Oxford A?ne dataset.Empirical results indicate that the proposed IGC yields quite stable and robust results,signi?cantly outperforms the original GC,and also can outperform the classical scale-invariant feature transform(SIFT) in most of the test cases.By integrating the IGC to the SIFT,the resulting of hybrid SIFT+IGC performs best over all other single descriptors in these experimental evaluations with various geometric transformations.

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Road visual navigation relies on accurate road models.This study was aimed at proposing an improved scale-invariant feature transform(SIFT)algorithm for recovering depth information from farmland road images,which would provide a reliable path for visual navigation.The mean image of pixel value in five channels(R,G,B,S and V)were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm,for achieving precise location of the feature points and ensuring the uniformity and density of the feature points.The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45ºin eight directions was then treated as the feature vector,and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points.In order to achieve the depth information of farmland road images,the energy method of feature points was used for eliminating the mismatched points.Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48%and 5.6 s,respectively,with the accuracy for depth recovery of-7.17%-2.97%in a certain sight distance.The mean uniformity,time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%,5.0-6.5 s,and higher than 88%,respectively,indicating that performance for achieving the feature points(e.g.,uniformity,matching accuracy,and algorithm real-time)of the improved SIFT algorithm were superior to that of conventional SIFT algorithm.This study provides an important reference for navigation technology of agricultural equipment based on machine vision.

Detection of engineering vehicles in high-resolution monitoring images

This paper presents a novel formulation for detecting objects with articulated rigid bodies from highresolution monitoring images, particularly engineering vehicles. There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects ob ject patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain ob ject parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain ob jects. In this stage, we increase the size of the image patches so that they include the complete ob ject using models of the ob ject parts.Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive ob jects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.

Scale-aware shape manipulation

A novel representation of a triangular mesh surface using a set of scale-invariant measures is proposed.The measures consist of angles of the triangles(triangle angles) and dihedral angles along the edges(edge angles)which are scale and rigidity independent. The vertex coordinates for a mesh give its scale-invariant measures, unique up to scale, rotation, and translation. Based on the representation of mesh using scale-invariant measures, a two-step iterative deformation algorithm is proposed, which can arbitrarily edit the mesh through simple handles interaction.The algorithm can explicitly preserve the local geometric details as much as possible in different scales even under severe editing operations including rotation, scaling, and shearing. The efficiency and robustness of the proposed algorithm are demonstrated by examples.