基于正交矩模糊和仿射混合不变量的图像识别算法

为了识别含有模糊和仿射混合形变的图像,提出了一种新的基于正交矩模糊和仿射混合不变量的图像识别算法.该算法首先使用归一化方法构造了基于Legendre正交矩的仿射不变量,并结合Legendre正交矩的模糊不变量提出了Legendre正交矩的模糊和仿射混合不变量;然后将该混合不变量作为描述算子,将欧几里德范数作为分类尺度,以最近邻法则作为分类器,对图像进行识别.实验结果表明,与其他基于非正交矩的混合不变量相比,基于Legendre正交矩的模糊和仿射混合不变量在混合形变下能够获得更好的不变性,不会带来信息冗余问题,并且对噪声鲁棒性较好;此外,该图像识别算法比其他算法具有更高的识别率,特别是在图像含有较大噪声的情况下.

基于正交矩混合不变量的离焦模糊图像配准信号与信息处理

针对同时遭受离焦模糊退化和几何形变的图像,提出一种基于Legendre正交矩不变量的图像配准方法.采用Harris特征点探测算法提取图像特征点,然后构造Legendre正交矩的模糊和几何混合不变量作为衡量尺度以获得特征点的对应关系,通过该对应关系估计形变参数完成配准过程.实验结果表明该方法能有效解决该类形变图像的配准问题,与其他方法相比能获得更准确的结果.

基于Legendre正交矩的模糊形变图像的配准方法

针对同时含有模糊和几何形变的图像,本文提出一种新的基于Legendre正交矩模糊和几何混合不变量的图像配准方法.该方法首先利用Harri-sLaplace算子检测出图像的特征点,然后构造Legendre矩混合不变量,并将其作为特征点的描述子获取特征点的对应关系,接着通过该对应关系估计图像间的形变参数,最后利用插值方法实现图像的配准.实验结果表明:本文方法能有效地解决含有混合形变的图像配准问题,并且和其他配准方法相比能获得更加准确的结果.

Translation-invariant wavelet denoising of full-tensor gravity-gradiometer data

Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translation- invariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet.

Inequalities of Maximum of Partial Sums and Convergence Rates in the Strong Laws for ρ^-mixing Random Variables

In this paper,we establish a Rosenthal-type inequality of partial sums for ρ~mixing random variables.As its applications,we get the complete convergence rates in the strong laws for ρ^-mixing random variables.The result obtained extends the corresponding result.

Moment Bounds for Strong Mixing Sequences and Their Application

Some moment inequalities for the strong mixing random variable sequence are established, and applied to discuss the asymptotic normality of the general weight function estimate for the fixed design regression model.

Generating Semi-Algebraic Invariants for Non-Autonomous Polynomial Hybrid Systems

Hybrid systems are dynamical systems with interacting discrete computation and continuous physical processes, which have become more common, more indispensable, and more complicated in our modern life. Particularly, many of them are safety-critical, and therefore are required to meet a critical safety standard. Invariant generation plays a central role in the verification and synthesis of hybrid systems. In the previous work, the fourth author and his coauthors gave a necessary and sufficient condition for a semi-algebraic set being an invariant of a polynomial autonomous dynamical system, which gave a confirmative answer to the open problem. In addition, based on which a complete algorithm for generating all semi-algebraic invariants of a given polynomial autonomous hybrid system with the given shape was proposed. This paper considers how to extend their work to non-autonomous dynamical and hybrid systems. Non-autonomous dynamical and hybrid systems are with inputs, which are very common in practice; in contrast, autonomous ones are without inputs. Furthermore, the authors present a sound and complete algorithm to verify semi-algebraic invariants for non-autonomous polynomial hybrid systems. Based on which, the authors propose a sound and complete algorithm to generate all invariants with a pre-defined template.

A Survey on Mixed Spin P-Fields

The mixed spin P-fields （MSP for short） theory sets up a geometric platform to relate Gromov-Witten invariants of the quintic three-fold and Fan-Jarvis-Ruan-Witten invariants of the quintic polynomial in five variables. It starts with Wittens vision and the P-fields treatment of GW invariants and FJRW invaxiants. Then it briefly discusses the master space technique and its application to the set-up of the MSP moduli. Some key results in MSP theory are explained and some examples are provided.

Response-surface-based structural reliability analysis with random and interval mixed uncertainties

Traditional reliability analysis requires probability distributions of all the uncertain parameters.However,in many practical applications,the variation bounds can be only determined for the parameters with limited information.A complex hybrid reliability problem then will be caused when the random and interval variables coexist in a same structure.In this paper,by introducing the response surface technique,we develop a new hybrid reliability method to efficiently compute the interval of the failure probability of the structure due to the probability-interval hybrid uncertainty.The present method consists of a sequence of iterations.At each step,a response surface model is constructed for the limit-state function by using a quadratic polynomial and a modified axial experimental design method.An approximate hybrid reliability problem is created based on the response surface model,which is subsequently solved by an efficient decoupling approach.An updating strategy is suggested to improve the quality of the response surface and whereby ensure the reliability analysis precision.A computational procedure is then summarized for the whole iterations.Four numerical examples and also a practical application are provided to demonstrate the effectiveness of the present method.