Many problems in engineering shape design involve eigenvalue optimizations.The relevant difficulty is that the eigenvalues are not continuously differentiable with respect to the density.In this paper,we are intereste...Many problems in engineering shape design involve eigenvalue optimizations.The relevant difficulty is that the eigenvalues are not continuously differentiable with respect to the density.In this paper,we are interested in the case of multi-density inhomogeneous materials which minimizes the least eigenvalue.With the finite element discretization,we propose a monotonically decreasing algorithm to solve the minimization problem.Some numerical examples are provided to illustrate the efficiency of the present algorithm as well as to demonstrate its availability for the case of more than two densities.As the computations are sensitive to the choice of the discretization mesh sizes,we adopt the refined mesh strategy,whose mesh grids are 25-times of the amount used in[S.Osher and F.Santosa,J.Comput.Phys.,171(2001),pp.272-288].We also show the significant reduction in computational cost with the fast convergence of this algorithm.展开更多
The development of document image databases is becoming a challenge for document image retrieval tech-niques.Traditional layout-reconstructed-based methods rely on high quality document images as well as an optical ch...The development of document image databases is becoming a challenge for document image retrieval tech-niques.Traditional layout-reconstructed-based methods rely on high quality document images as well as an optical char-acter recognition(OCR)precision,and can only deal with several widely used languages.The complexity of document layouts greatly hinders layout analysis-based approaches.This paper describes a multi-density feature based algorithm for binary document images,which is independent of OCR or layout analyses.The text area was extracted after prepro-cessing such as skew correction and marginal noise removal.Then the aspect ratio and multi-density features were extract-ed from the text area to select the best candidates from the document image database.Experimental results show that this approach is simple with loss rates less than 3%and can efficiently analyze images with different resolutions and dif-ferent input systems.The system is also robust to noise due to its notes and complex layouts,etc.展开更多
基金supported by the Chinese National Science Foundation(No.10871179)the National Basic Research Programme of China(No.2008CB717806)Specialized Research Fund for the Doctoral Program of Higher Education of China(SRFDP No.20070335201).
文摘Many problems in engineering shape design involve eigenvalue optimizations.The relevant difficulty is that the eigenvalues are not continuously differentiable with respect to the density.In this paper,we are interested in the case of multi-density inhomogeneous materials which minimizes the least eigenvalue.With the finite element discretization,we propose a monotonically decreasing algorithm to solve the minimization problem.Some numerical examples are provided to illustrate the efficiency of the present algorithm as well as to demonstrate its availability for the case of more than two densities.As the computations are sensitive to the choice of the discretization mesh sizes,we adopt the refined mesh strategy,whose mesh grids are 25-times of the amount used in[S.Osher and F.Santosa,J.Comput.Phys.,171(2001),pp.272-288].We also show the significant reduction in computational cost with the fast convergence of this algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.60472028)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20040003015).
文摘The development of document image databases is becoming a challenge for document image retrieval tech-niques.Traditional layout-reconstructed-based methods rely on high quality document images as well as an optical char-acter recognition(OCR)precision,and can only deal with several widely used languages.The complexity of document layouts greatly hinders layout analysis-based approaches.This paper describes a multi-density feature based algorithm for binary document images,which is independent of OCR or layout analyses.The text area was extracted after prepro-cessing such as skew correction and marginal noise removal.Then the aspect ratio and multi-density features were extract-ed from the text area to select the best candidates from the document image database.Experimental results show that this approach is simple with loss rates less than 3%and can efficiently analyze images with different resolutions and dif-ferent input systems.The system is also robust to noise due to its notes and complex layouts,etc.
