1?Hyper-Textbook: Optimization Models and Applications??—? Fall 2014?

https://inst.eecs.berkeley.edu/~ee127a/book/login/index.html

二?openfabmap

https://code.google.com/p/openfabmap/

openFABMAP

This is an open and modifiable code-source which implements the Fast Appearance-based Mapping algorithm (FAB-MAP) originally developed by Mark Cummins and Paul Newman. OpenFABMAP was designed from published FAB-MAP theory and is for personal and research use.

FAB-MAP is a Simultaneous Localisation and Mapping algorithm which operates in appearance space only. FAB-MAP performs location matching between places that have been visited within the world as well as providing a measure of the probability of being at a new, previously unvisited location. Camera images form the sole input to the system, from which bag-of-words models are formed through the extraction of appearance-based (e.g. SURF) features.

The code has implementations of

• Feature Detection and Extraction and Bag-of-words models using OpenCV
• Chow-Liu tree implementation
• FAB-MAP v1.0 (Cummins & Newman 2008)
• FAB-MAP v1.0 using a Look-up-table for improved computation speed
• FAB-MAP with Fast-Bailout (Cummins & Newman 2010)
• FAB-MAP v2.0 (Cummins & Newman 2010)

For an overview of OpenFABMAP see (Glover et al. 2012). OpenFABMAP was first used in (Glover et al. 2010).

As of the latest version, openFABMAP is dependent solely on?OpenCV 2.3?or higher. The project is designed to integrate with OpenCV2.3 2D feature-based methods and storage methods. The project has a?CMake?build environment for general use on both Linux and Windows systems. See the README file for more information on compiling the code.

OpenFABMAP is also designed to integrate with?Robot Operating System?(ROS). See the?CyPhy-ROS?page for a package that has implemented openFABMAP as a ROS node.

Check out the?wiki?(under construction) for some instructions and tips on running openFABMAP.

For questions on how to modify the source to your specific implementation, bug reporting, comments and suggestions, or if you would like to become involved in developing the openFABMAP project beyond the current implementation contact via the?google group.

Citations?Endnote?BibTex

三

吳立德 《深度學習課程》

http://www.youku.com/playlist_show/id_21508721.html

四

1. ?火光搖曳團隊分享文檔

? ? ?Docs

• 神奇的伽瑪函數
• LDA 數學八卦
• 正態分布的前世今生

? ?Paper

• Towards Topic Modeling for Big Data

• Peacock: Learning Long-Tail Topic Features for Industrial Applications，Yi Wang,?Xuemin Zhao,?Zhenlong Sun,?Hao Yan,?Lifeng Wang,?Zhihui Jin,?Liubin Wang,?Yang Gao,?Ching Law,?Jia Zeng，ACM TIST 2014

? ?Slides

• 正態分布(1)
• 正態分布(2)
• Dirichlet 函數介紹
• Gibbs Sampling 介紹
• LDA Introduction
• 假設檢驗
• 假設檢驗及其應用
• 統計參數估計
• 語言模型和序列標注
• NLP&統計語言模型
• 廣告定向中的用戶分析
• 精準定向的廣告系統
• Learning One Million Latent Topics from Billions of Queries
• 大規模主題模型建模及其在騰訊業務中的應用
• SWIG Introduction
• Code Review
• SVN Introduction
• LDA Training System
• Introduction to Peacock
• 人臉對齊技術
• M6D Targeting Model
• Multi-Layer Perceptron
• 一起學習 Spark
  • Scala入門簡介
  • Akka介紹
  • MLLib: LR & NB
  • MLLib: LinearSVM
  • MLLib: Regression

2. ?其他資料整理

本文鏈接：資料分享 本站文章若無特別說明，皆為原創，轉載請注明來源：火光搖曳，謝謝！^^

五 http://yimiwawa.github.io/六 ffmpeg SDLhttp://dranger.com/ffmpeg/ 6? http://visioncompute.readthedocs.org/en/latest/FaceDetect.html 七 http://weibo.com/cvrobot?is_hot=1&noscale_head=1#_0 八 http://bigwww.epfl.ch/ http://imagingbook.com/source/ http://imagingbook.com/9 https://www.behance.net/gallery/29828109/Deep-Learning-Automatic-Parking-Lot-Classification 10 http://blog.csdn.net/huneng1991/article/details/48085601

《新程序員》：云原生和全面數字化實踐50位技術專家共同創作，文字、視頻、音頻交互閱讀

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