来自澳大利亚Qeensland大学的计算机视觉Matlab工具箱。 This Toolbox provides a number of functions that are useful in computer vision, machine vision and related areas. It is a somewhat eclectic collection reflecting the author s personal interest in areas of photometry, photogrammetry, colorimetry. It covers functions such as image file reading and writing, filtering, segmentation, feature extraction, camera calibration, camera exterior orientation, display, color space conversion and blackbody radiators. The Toolbox, combined with MATLAB and a modern workstation computer, is a useful and convenient environment for investigation of machine vision algorithms. It is possible to use MEX files to interface with image acquisition hardware ranging from simple framegrabbers to Datacube servers.
标签： Qeensland functions provides Toolbox
上传时间： 2015-09-30
上传用户：qb1993225
A general technique for the recovery of signi cant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric pro- cedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is dis- cussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or pro- vide, by extracting all the signi cant colors, a prepro- cessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate
标签： technique presented features recovery
上传时间： 2015-10-14
上传用户：410805624
基于libsvm，开发的支持向量机图形界面(初级水平)应用程序，并提供了关于C和sigma的新的参数选择方法，使得SVM的使用更加简单直观.参考文章 Fast and Efficient Strategies for Model Selection of Gaussian Support Vector Machine 可google之。
标签： libsvm
上传时间： 2015-10-16
上传用户：cuibaigao
统计学习本质论，支持向量机（svm）的经典书籍
标签：
上传时间： 2014-01-20
上传用户：lifangyuan12
Input The input contains blocks of 2 lines. The first line contains the number of sticks parts after cutting, there are at most 64 sticks. The second line contains the lengths of those parts separated by the space. The last line of the file contains zero. Output The output should contains the smallest possible length of original sticks, one per line. Sample Input 9 5 2 1 5 2 1 5 2 1 4 1 2 3 4 0 Sample Output 6 5
标签： contains The blocks number
上传时间： 2015-10-27
上传用户：lepoke
The Rayleigh Integral Method is useful in computing the acoustic properties of a flat panel radiating into a half space.
标签： properties computing Rayleigh Integral
上传时间： 2015-12-07
上传用户：youmo81
celestia源代码，Celestia, a real-time 3D space simulation featuring a database of over 100000 stars, nearly a hundred solar system, objects, and a complete catalog of extrasolar planets.
上传时间： 2013-12-26
上传用户：缥缈
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
标签： sequential simulation posterior overview
上传时间： 2015-12-31
上传用户：225588
To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm.
标签： input-output the generated following
上传时间： 2014-01-05
上传用户：royzhangsz
MSVM，svm的多分类问题实现，实现语言为c
标签： MSVM
上传时间： 2014-01-27
上传用户：evil
