Unknown Transformation Research Articles

Multiple Moving Object Detection From UAV Videos Using Trajectories of Matched Regional Adjacency Graphs

Image registration has been long used as a basis for the detection of moving objects. Registration techniques attempt to discover correspondences between consecutive frame pairs based on image appearances under rigid and affine transformations. However, spatial information is often ignored, and different motions from multiple moving objects cannot be efficiently modeled. Moreover, image registration is not well suited to handle occlusion that can result in potential object misses. This paper proposes a novel approach to address these problems. First, segmented video frames from unmanned aerial vehicle captured video sequences are represented using region adjacency graphs of visual appearance and geometric properties. Correspondence matching (for visible and occluded regions) is then performed between graph sequences by using multigraph matching. After matching, region labeling is achieved by a proposed graph coloring algorithm which assigns a background or foreground label to the respective region. The intuition of the algorithm is that background scene and foreground moving objects exhibit different motion characteristics in a sequence, and hence, their spatial distances are expected to be varying with time. Experiments conducted on several DARPA VIVID video sequences as well as self-captured videos show that the proposed method is robust to unknown transformations, with significant improvements in overall precision and recall compared to existing works.

Evaluating the photocatalytic treatment of stevioside by TiO2 in different aqueous matrices and identification of transformation products

The present study reports the photocatalytic transformation of stevioside, under simulated solar irradiation using TiO2 as a photocatalyst. As a tool of investigating the effect of various aqueous matrices, as well as, the initial stevioside concentration on the variation of the photocatalytic efficiency, a fully nested experimental design was employed. A significant impact on the degradation rate of the sweetener was observed: degradation rate decreases in the order distilled water>river water>lake water, attributed to the increased natural organic matter content of the respective natural water samples. Moreover, the investigation has involved the identification of intermediate compounds, as well as the assessment of mineralization and toxicity evaluation. More than one hundred unknown transformation products, most of them in the form of several isobaric species, were identified. By employing accurate mass determination, we were able to attribute an empirical formula to each species and through MSn analyses we were capable to distinguish several isobaric species. The overall transformation mechanism was assessed and involved the hydroxylation/oxidation of the molecule and the subsequent loss of the glucose units bound to the parent compound.

Simultaneous anaerobic transformation of carbon tetrachloride to carbon dioxide and tetrachloroethene to ethene in a continuous flow column

The simultaneous anaerobic transformation of tetrachloroethene (PCE) and carbon tetrachloride (CT) was evaluated in a continuous flow column. The column was packed with quartz sand and bioaugmented with the Evanite culture (EV) that is capable of transforming PCE to ethene. Azizian and Semprini (2016) reported that PCE and CT could be simultaneously transformed in the column, with PCE (0.1mM) transformed mainly to ethene and CT (0.015mM) to chloroform (CF) (20%) and an unknown transformation product, likely carbon dioxide (CO2). The fermentation of propionate, formed from lactate fermentation, was inhibited after the transformation of CT, likely from the exposure to CF. Reported here is the second phase of that study where a second bioaugmentation of the EV culture was made to reintroduce a lactate and propionate fermenting population to the column. Effective lactate and propionate fermentation were restored with a H2 concentration of ~25nM maintained in the column effluent. PCE (0.1mM) was effectively transformed to ethene (~98%) and vinyl chloride (VC) (~2%). Unlabeled CT (0.015 to 0.03mM) was completely transformed with a transient build-up of CF and chloromethane (CM), which were subsequently removed below their detection limits. A series of transient tests were initiated through the addition of carbon-13 labeled CT (13CT), with concentrations gradually increased from 0.03 to 0.10mM. GC–MS analysis of the column effluent showed that 13C labeled CO2 (13CO2) was formed, ranging from 82 to 93% of the 13CT transformed, with the transient increases in 13CO2 associated with the increased concentration of 13CT. A modified COD analysis indicated a lesser amount of 13CT (18%) was transformed to soluble products, while 13CO2 represented 82% the 13CT transformed. In a final transient test, the influent lactate concentration was decreased from 1.1 to 0.67mM. The transformation of both CT and PCE changed dramatically. Only 59% of the 13CT was transformed, primarily to CF. 13CO2 concentrations gradually decreased to background levels, indicating CO2 was no longer a transformation product. PCE transformation resulted in the following percentage of products formed: cDCE (60%), VC (36%), and ethene (4%). Incomplete propionate fermentation was also observed, consistent with the build-up of CF and the decrease in H2 concentrations to approximately 2nM. The results clearly demonstrate that high concentrations of CT were transformed to CO2, and effective PCE dehalogenation to ethene was maintained when excess lactate was fed and propionate was effectively fermented. However, when the lactate concentration was reduced, both PCE and CT transformation and propionate fermentation were negatively impacted.

Testing the adequacy of semiparametric transformation models

We consider a semiparametric model whereby the response variable following a transformation can be expressed by means of a regression model. In this model, the form of the transformation is specified analytically (up to an unknown transformation parameter), while the regression function is completely unknown. We develop testing procedures for the null hypothesis that this semiparametric model adequately describes the data at hand. In doing so, the test statistic is formulated on the basis of Fourier-type conditional expectations, an idea first put forward by Bierens (J Econom 20:105–134, 1982). The asymptotic distribution of the test statistic is obtained under the null as well as under alternative hypotheses. Since the limit null distribution is nonstandard, a bootstrap version is utilized in order to actually carry out the test procedure. Monte Carlo results are included that illustrate the finite-sample properties of the new method.

Fuchsia : A tool for reducing differential equations for Feynman master integrals to epsilon form

We present Fuchsia — an implementation of the Lee algorithm, which for a given system of ordinary differential equations with rational coefficients ∂xJ(x,ϵ)=A(x,ϵ)J(x,ϵ) finds a basis transformation T(x,ϵ), i.e., J(x,ϵ)=T(x,ϵ)J′(x,ϵ), such that the system turns into the epsilon form : ∂xJ′(x,ϵ)=ϵS(x)J′(x,ϵ), where S(x) is a Fuchsian matrix. A system of this form can be trivially solved in terms of polylogarithms as a Laurent series in the dimensional regulator ϵ. That makes the construction of the transformation T(x,ϵ) crucial for obtaining solutions of the initial system.In principle, Fuchsia can deal with any regular systems, however its primary task is to reduce differential equations for Feynman master integrals. It ensures that solutions contain only regular singularities due to the properties of Feynman integrals. Program summaryProgram Title:FuchsiaProgram Files doi:http://dx.doi.org/10.17632/zj6zn9vfkh.1Licensing provisions: MITProgramming language:Python 2.7Nature of problem: Feynman master integrals may be calculated from solutions of a linear system of differential equations with rational coefficients. Such a system can be easily solved as an ϵ-series when its epsilon form is known. Hence, a tool which is able to find the epsilon form transformations can be used to evaluate Feynman master integrals.Solution method: The solution method is based on the Lee algorithm (Lee, 2015) which consists of three main steps: fuchsification, normalization, and factorization. During the fuchsification step a given system of differential equations is transformed into the Fuchsian form with the help of the Moser method (Moser, 1959). Next, during the normalization step the system is transformed to the form where eigenvalues of all residues are proportional to the dimensional regulator ϵ. Finally, the system is factorized to the epsilon form by finding an unknown transformation which satisfies a system of linear equations.Additional comments including Restrictions and Unusual features: Systems of single-variable differential equations are considered. A system needs to be reducible to Fuchsian form and eigenvalues of its residues must be of the form n+mϵ, where n is integer. Performance depends upon the input matrix, its size, number of singular points and their degrees. It takes around an hour to reduce an example 74 × 74 matrix with 20 singular points on a PC with a 1.7 GHz Intel Core i5 CPU. An additional slowdown is to be expected for matrices with complex and/or irrational singular point locations, as these are particularly difficult for symbolic algebra software to handle.

Real-Time Video Stylization Using Object Flows.

We present a real-time video stylization system and demonstrate a variety of painterly styles rendered on real video inputs. The key technical contribution lies on the object flow, which is robust to inaccurate optical flow, unknown object transformation and partial occlusion as well. Since object flows relate regions of the same object across frames, shower-door effect can be effectively reduced where painterly strokes and textures are rendered on video objects. The construction of object flows is performed in real time and automatically after applying metric learning. To reduce temporal flickering, we extend the bilateral filtering into motion bilateral filtering. We propose quantitative metrics to measure the temporal coherence on structures and textures of our stylized videos, and perform extensive experiments to compare our stylized results with baseline systems and prior works specializing in watercolor and abstraction.

Solving the robot-world hand-eye(s) calibration problem with iterative methods

Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as $\mathbf{AX}=\mathbf{ZB}$, where $\mathbf{X}$ and $\mathbf{Z}$ are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and simulated datasets, and compare to seven other state-of-the-art methods. Our collection of methods return greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple-eye calibration, and results are shown with two and three cameras mounted on the same robot.

Transformation of carbon tetrachloride and chloroform by trichloroethene respiring anaerobic mixed cultures and supernatant

Carbon tetrachloride (CT) and chloroform (CF) were transformed in batch reactor experiments conducted with anaerobic dechlorinating cultures and supernatant (ADC + S) harvested from continuous flow reactors. The Evanite (EV) and Victoria/Stanford (VS) cultures, capable of respiring trichloroethene (TCE), 1,2-cis-dichloroethene (cDCE), and vinyl chloride (VC) to ethene (ETH), were grown in continuous flow reactors receiving an influent feed of saturated TCE (10 mM; 60 mEq) and formate (45 mM; 90 mEq) but no CT or CF. Cells and supernatant were harvested from the chemostats and inoculated into batch reactors at the onset of each experiment. CT transformation was complete following first order kinetics with CF, DCM and CS2 as the measurable transformation products, representing 20–40% of the original mass of CT, with CO2 likely the unknown transformation product. CF was transformed to DCM and likely CO2 at an order of magnitude rate lower than CT, while DCM was not further transformed. An analytical first order model including multiple key reactions effectively simulated CT transformation, product formation and transformation, and provided reasonable estimates of transformation rate coefficients. Biotic and abiotic treatments indicated that CT was mainly transformed via abiotic processes. However, the presence of live cells was associated with the transformation of CF to DCM. In biotic tests both TCE and CT were simultaneously transformed, with TCE transformed to ETH and approximately 15–53% less CF formed via CT transformation. A 14-day exposure to CF (CFmax = 1.4 μM) reduced all rates of chlorinated ethene respiration by a factor of 10 or greater.

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter.

The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

OUP accepted manuscript

SummarySemiparametric transformation models with random effects are useful in analysing recurrent and clustered data. With specified error and random effect distributions, Zeng & Lin (2007a) proved that nonparametric maximum likelihood estimators are semiparametric efficient. In this paper we consider a more general class of transformation models with random effects, under which an unknown monotonic transformation of the response is linearly related to the covariates and the random effects with unspecified error and random effect distributions. This includes many popular models. We propose an estimator based on the maximum rank correlation, which relies on symmetry of the random effect distribution, and establish its consistency and asymptotic normality. A random weighting resampling scheme is employed for inference. The proposed method can be extended to censored and clustered data. Numerical studies demonstrate that the proposed method performs well in practical situations. Application of the method is illustrated with the Framingham cholesterol data.

Inference for biased transformation models

Working regression models are often parsimonious for practical use and however may be biased. This is because either some strong signals to the response are not included in working models or too many weak signals are excluded in the modeling stage, which make cumulative bias. Thus, estimating consistently the parameters of interest in biased working models is then a challenge. This paper investigates the estimation problem for linear transformation models with three aims. First, to identify strong signals in the original full models, a sufficient dimension reduction approach is applied to transferring linear transformation models to pro forma linear models. This method can efficiently avoid high-dimensional nonparametric estimation for the unknown model transformation. Second, after identifying strong signals, a semiparametric re-modeling with some artificially constructed predictors is performed to correct model bias in working models. The construction procedure is introduced and a ridge ratio estimation is proposed to determine the number of these predictors. Third, root-n consistent estimators of the parameters in working models are defined and the asymptotic normality is proved. The performance of the new method is illustrated through simulation studies and a real data analysis.

Bayesian analysis of transformation latent variable models with multivariate censored data

Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.

Orthogonal dual tensor method for solving the AX = XB sensor calibration problem

The AX=XB sensor calibration problem is very important in multiple fields such as robotics, computer vision or machine vision. In this type of problem, A and B are parameterizations of rigid body motions, while X is an unknown transformation. This research proposes a new approach for solving the AX=XB sensor calibration problem by mapping the SE3 classic formulation into an orthogonal dual tensors set SO̲3representation. Using an isomorphism between SE3 and SO̲3, together with a set of properties from SO̲3, we formulate closed-form simultaneous solutions for data recorded from any number of displacements. These solutions are free of coordinates and can easily be put into practice. Implementation algorithms are presented and numerical results are discussed in order to evaluate the performance of the proposed solutions.

Affine invariants of generalized polygons and matching under affine transformations

A generalized polygon is an ordered set of vertices. This notion generalizes the concept of the boundary of a polygonal shape because self-intersections are allowed. In this paper we study the problem of matching generalized polygons under affine transformations. Our approach is based on invariants. Firstly we associate an ordered set of complex numbers with each polygon and construct a collection of complex scalar functions on the space of plane polygons. These invariant functions are defined as quotients of the so-called Fourier descriptors, also known as discrete Fourier transforms.Each one of these functions is invariant under similarity transformations; that is, the function associates the same complex number to similar polygons. Moreover, if two polygons are affine related (one of them is the image of the other under an affine transformation), the pseudo-hyperbolic distance between their associated values is a constant that depends only on the affine transformation involved, but independent of the polygons.More formally, given a collection {Z1,Z2,…,Zm} of n-sided polygons in the plane and a query polygon W, we give algorithms to find all Zℓ such that f(Zℓ)=W+ΔW, where f is an unknown affine transformation and ΔW=(Δw1,…,Δwn) with |Δwk|≤ρ, where ρ is certain tolerance.

Weighted coordinate transformation formulated by standard least-squares theory

This paper presents a universal model of weighted coordinate transformation, i.e. transformation considering the errors of coordinates in both coordinate systems. It is intrinsically one of the typical examples of ‘error-in-variables’ (EIV) models. The proposed method of LS theory application on weighted coordinate transformation does not impose any constraints on the form of functional relationship among stochastic variables. Since the basic idea is to generalise Gauss–Markov model (GMM) by introduction of so-called ‘total residuals’, the proposed procedure is named ‘Generalised Gauss–Markov model’. Formulation of expressions for estimation of unknown transformation parameters is theoretically confirmed using the Gauss–Helmert model (GHM) and three different modifications of the GMM. The proposed procedure is in its essence a strict solution to total least-squares (unweighted) and weighted total least-squares problem in coordinate transformation. This thesis is experimentally confirmed by comparison of its results with those found in four characteristic examples from the literature.

An Alignment Method for the Integration of Underwater 3D Data Captured by a Stereovision System and an Acoustic Camera.

The integration of underwater 3D data captured by acoustic and optical systems is a promising technique in various applications such as mapping or vehicle navigation. It allows for compensating the drawbacks of the low resolution of acoustic sensors and the limitations of optical sensors in bad visibility conditions. Aligning these data is a challenging problem, as it is hard to make a point-to-point correspondence. This paper presents a multi-sensor registration for the automatic integration of 3D data acquired from a stereovision system and a 3D acoustic camera in close-range acquisition. An appropriate rig has been used in the laboratory tests to determine the relative position between the two sensor frames. The experimental results show that our alignment approach, based on the acquisition of a rig in several poses, can be adopted to estimate the rigid transformation between the two heterogeneous sensors. A first estimation of the unknown geometric transformation is obtained by a registration of the two 3D point clouds, but it ends up to be strongly affected by noise and data dispersion. A robust and optimal estimation is obtained by a statistical processing of the transformations computed for each pose. The effectiveness of the method has been demonstrated in this first experimentation of the proposed 3D opto-acoustic camera.

Biosynthetic investigation of phomopsins reveals a widespread pathway for ribosomal natural products in Ascomycetes

Production of ribosomally synthesized and posttranslationally modified peptides (RiPPs) has rarely been reported in fungi, even though organisms of this kingdom have a long history as a prolific source of natural products. Here we report an investigation of the phomopsins, antimitotic mycotoxins. We show that phomopsin is a fungal RiPP and demonstrate the widespread presence of a pathway for the biosynthesis of a family of fungal cyclic RiPPs, which we term dikaritins. We characterize PhomM as an S-adenosylmethionine-dependent α-N-methyltransferase that converts phomopsin A to an N,N-dimethylated congener (phomopsin E), and show that the methyltransferases involved in dikaritin biosynthesis have evolved differently and likely have broad substrate specificities. Genome mining studies identified eight previously unknown dikaritins in different strains, highlighting the untapped capacity of RiPP biosynthesis in fungi and setting the stage for investigating the biological activities and unknown biosynthetic transformations of this family of fungal natural products.

Nonconvex plus quadratic penalized low-rank and sparse decomposition for noisy image alignment

This paper proposes a general method for dealing with the problem of recovering the low-rank structure, in which the data can be deformed by some unknown transformations and corrupted by sparse or nonsparse noises. Nonconvex penalization method is used to remedy the drawbacks of existing convex penalization method and a quadratic penalty is further used to better tackle the nonsparse noises in the data. We exploits the local linear approximation (LLA) method for turning the resulting nonconvex penalization problem into a series of weighted convex penalization problems and these subproblems are efficiently solved via the augmented Lagrange multiplier (ALM). Besides comparing with the method of robust alignment by sparse and low-rank decomposition for linearly correlated images (RASL), we also propose a nonconvex penalized lowrank and sparse decomposition (NLSD) model as comparison. Numerical experiments are conducted on both controlled and uncontrolled data to demonstrate the outperformance of the proposed method over RASL and NLSD.

Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework

This paper concerns the fixed-smoothing asymptotics for two commonly used estimators in the generalized empirical likelihood estimation framework for time series data, namely the continuous updating estimator and the maximum blockwise empirical likelihood estimator. For continuously updating generalized method of moments (GMM) estimator, we show that the results for the two-step GMM estimator in Sun (2014a) continue to hold under suitable assumptions. For continuous updating estimator obtained through solving a saddle point problem (Newey and Smith, 2004) and the maximum blockwise empirical likelihood estimator (Kitamura, 1997), we show that their fixed-smoothing asymptotic distributions (up to an unknown linear transformation) are mixed normal. Based on these results, we derive the asymptotic distributions of the specification tests (including the over-identification testing and testing on parameters) under the fixed-smoothing asymptotics, where the corresponding limiting distributions are nonstandard yet pivotal. Simulation studies show that (i) the fixed-smoothing asymptotics provides better approximation to the sampling distributions of the continuous updating estimator and the maximum blockwise empirical likelihood estimator as compared to the standard normal approximation. The testing procedures based on the fixed-smoothing critical values are more accurate in size than the conventional chi-square based tests; (ii) the continuously updating GMM estimator is asymptotically more efficient and the corresponding specification tests are generally more powerful than the other two competitors. Finite sample results from an empirical data analysis are also reported.

Heteroscedastic transformation cure regression models.

Cure models have been applied to analyze clinical trials with cures and age-at-onset studies with nonsusceptibility. Lu and Ying (On semiparametric transformation cure model. Biometrika 2004; 91:331?-343. DOI: 10.1093/biomet/91.2.331) developed a general class of semiparametric transformation cure models, which assumes that the failure times of uncured subjects, after an unknown monotone transformation, follow a regression model with homoscedastic residuals. However, it cannot deal with frequently encountered heteroscedasticity, which may result from dispersed ranges of failure time span among uncured subjects' strata. To tackle the phenomenon, this article presents semiparametric heteroscedastic transformation cure models. The cure status and the failure time of an uncured subject are fitted by a logistic regression model and a heteroscedastic transformation model, respectively. Unlike the approach of Lu and Ying, we derive score equations from the full likelihood for estimating the regression parameters in the proposed model. The similar martingale difference function to their proposal is used to estimate the infinite-dimensional transformation function. Our proposed estimating approach is intuitively applicable and can be conveniently extended to other complicated models when the maximization of the likelihood may be too tedious to be implemented. We conduct simulation studies to validate large-sample properties of the proposed estimators and to compare with the approach of Lu and Ying via the relative efficiency. The estimating method and the two relevant goodness-of-fit graphical procedures are illustrated by using breast cancer data and melanoma data. Copyright © 2016 John Wiley & Sons, Ltd.