【作 者】Per Christian Hansen 【出版社】Society for Industrial and Applied Mathematic 【出版日期】October 29, 2006 【ISBN】0898716187 9780898716184 【形态项】9.8 x 6.7 x 0.3 inches 【语 言】English 【价 格】$63.00 Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms) By Per Christian Hansen Publisher: Society for Industrial and Applied Mathematic Number Of Pages: 130 Publication Date: 2006-10-29 ISBN-10 / ASIN: 0898716187 ISBN-13 / EAN: 9780898716184 Binding: Paperback “The book’s focus on imaging problems is very unique among the competing books on inverse and ill-posed problems. …It gives a nice introduction into the MATLAB world of images and deblurring problems.” — Martin Hanke, Professor, Institut für Mathematik, Johannes-Gutenberg-Universität. When we use a camera, we want the recorded image to be a faithful representation of the scene that we see, but every image is more or less blurry. In image deblurring, the goal is to recover the original, sharp image by using a mathematical model of the blurring process. The key issue is that some information on the lost details is indeed present in the blurred image, but this “hidden” information can be recovered only if we know the details of the blurring process. Deblurring Images: Matrices, Spectra, and Filtering describes the deblurring algorithms and techniques collectively known as spectral filtering methods, in which the singular value decomposition—or a similar decomposition with spectral properties—is used to introduce the necessary regularization or filtering in the reconstructed image. The concise MATLAB® implementations described in the book provide a template of techniques that can be used to restore blurred images from many applications. This book’s treatment of image deblurring is unique in two ways: it includes algorithmic and implementation details; and by keeping the formulations in terms of matrices, vectors, and matrix computations, it makes the material access

This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than restricting attention to traditional nonlinear least squares.

内部有两个文件，.m文件为matlab代码，可以计算rice图片中的rice个数，直接可以运行。

矩阵函数计算的教材，英国皇家学会会员、曼彻斯特大学教授N. Higham所著。

This book gives a coherent and detailed description of analytical methods devised to study random matrices. These methods are critical to the understanding of various fields in in mathematics and mathematical physics, such as nuclear excitations, ultrasonic resonances of structural materials, chaotic systems, the zeros of the Riemann and other zeta functions. More generally they apply to the characteristic energies of any sufficiently complicated system and which have found, since the publication of the second edition, many new applications in active research areas such as quantum gravity, traffic and communications networks or stock movement in the financial markets. This revised and enlarged third edition reflects the latest developements in the field and convey a greater experience with results previously formulated. For example, the theory of skew-orthogoanl and bi-orthogonal polynomials, parallel to that of the widely known and used orthogonal polynomials, is explained here for the first time.

Direct Methods for Sparse Matrices (I. S. Duff, A. M. Erisman, J. K. Reid).pdf

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kaggle比赛HousePrices之数据预处理部分的完整代码，包含非常详细的注释，属于数据挖掘预处理的经典流程性代码。

ArcGIS 10.1 Mastering GIS Price Data

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