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.

Digital watermarking algorithm based on scale-invariant feature regions in non-subsampled contourlet transform domain

Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform （NSCT） domain is proposed. The largest energy sub-band after NSCT is selected to embed watermark. The watermark is embedded into scaleinvariant feature transform （SIFT） regions. During embedding, the initial region is divided into some cirque sub-regions with the same area, and each watermark bit is embedded into one sub-region. Extensive simulation results and comparisons show that the algorithm gets a good trade-off of invisibility, robustness and capacity, thus obtaining good quality of the image while being able to effectively resist common image processing, and geometric and combo attacks, and normalized similarity is almost all reached.

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.

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。

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.

Vision based terrain reconstruction for planet rover using a special binocular bundle adjustment

This paper presents a pure vision based technique for 3D reconstruction of planet terrain. The reconstruction accuracy depends ultimately on an optimization technique known as 'bundle adjustment'. In vision techniques, the translation is only known up to a scale factor, and a single scale factor is assumed for the whole sequence of images if only one camera is used. If an extra camera is available, stereo vision based reconstruction can be obtained by binocular views. If the baseline of the stereo setup is known, the scale factor problem is solved. We found that direct application of classical bundle adjustment on the constraints inherent between the binocular views has not been tested. Our method incorporated this constraint into the conventional bundle adjustment method. This special binocular bundle adjustment has been performed on image sequences similar to planet terrain circumstances. Experimental results show that our special method enhances not only the localization accuracy, but also the terrain mapping quality.

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。

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.

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.

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

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 subsystems 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.

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.

SCALE-INVARIANT SOLUTION FOR FRACTIONAL ANOMALOUS DIFFUSION EOUATION

Fractional diffusion equation for transport phenomena in fractal media is an integro-partial differential equation. For solving the problem of this kind of equation the invariants of the group of scaling transformations are given and then used for deriving the integro-ordinary differential equation for the scale-invariant solution. With the help of Mellin transform the scale-invariant solution is obtained in terms of Fox functions. And the series and asymptotic representations for the solution are considered.

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.

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.