-
基于背景差分的运动目标检测方法
：针对静止摄像机下的运动目标检测问题，提出了一种基于背景减法的运动目标检测算法( 通过对一组连续视频进行处理，从中得到不含运动目标的背景图像( 再利用背景差分的方法提取出运动目标( 在确定比较阈值的过程中，一改以往通过实验不断调整的做法，提出了动态阈值的概念，从而增强了检测效果，提高了算法的可实施性( 融入了高斯模型关于背景更新的算法，克服了由于背景突然改变而造成的误检测( 实验结果表明，通过背景差分与高斯模型相结合的方法，在有诸多不确定性因素的序列视频中构建背景有较好的自适应性，能迅速响应实际场景的变化，为准确地检测出运动目标提供了必要的基础(
- 2020-12-01下载
- 积分：1
-
数据库课程设计 医院药品进销存系统
耗费差不多60小时以上写的，每天写到凌晨2点，每个细节和用户体验都考虑到，绝对不是你们下载所看到的那些粗糙的界面和功能，我手动添加数据超过100多，测试记录上200多次，每个细节和功能体验都做到最完美，用VS2010编译，可获得win7下的透明界面，压缩包里面有数据库文件，数据库配置方法，课程设计说明书，还有源代码，完全由自己独立完成。绝无雷同版，仅供参考
- 2020-06-27下载
- 积分：1
-
DSP设计FIR带通滤波器（报告&源代码）
代码经过调试有效，实验报告详细清晰易懂，格式正确。① 滤波器的阶数≥5，截止频率自行选定，滤波系数用MATLAB确定。② 编制C54XDSP实现FIR滤波器的汇编源程序。③ 用软件仿真器完成上述程序的模拟调试。④ 以数据文件形式自行设定滤波器输入数据，以数据文件形式输出滤波结果，并与输入数据进行比较分析。用软件仿真器有关工具显示FIR滤波器的输入输出波形，以证明滤波器滤波性能。
- 2020-12-04下载
- 积分：1
-
纹理检测代码及论文
多个vc，matlab纹理检测，纹理分割代码数篇IEEE纹理分割，纹理缺陷检测论文合集
- 2020-12-10下载
- 积分：1
-
基于hmm的数字语音识别_matlab版
提供一个matlab版本的基于hmm的数字语音识别程序，经过调试，有注释；并且提供一个有40人的数字语音语料库；很实用。
- 2020-12-03下载
- 积分：1
-
多传感器异步数据融合算法
【实例简介】多传感器数据融合在数据融合领域是一个不错的
- 2021-11-05 00:33:43下载
- 积分：1
-
【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138
- 2020-12-10下载
- 积分：1
-
photoshopCC dds插件
photoshop CC 2018 64位可用dds64位插件，兼容所有PS64位使用
- 2020-11-28下载
- 积分：1
-
最大流，最大流最小费用算法
最大流，最大流最小费用算法，包括代码，测试数据，结果，已测试，完全正确。
- 2021-05-07下载
- 积分：1
-
Realtek-RTD2660源代码源程序-适用于7至19寸
Realtek-RTD2660源代码源程序-适用于7至19寸
- 2020-12-11下载
- 积分：1
|
联系客服（QQ：3324461878）
