learning opencv you can use it to start you vision knowledge
標簽： knowledge you learning opencv
上傳時間： 2017-05-06
上傳用戶：蠢蠢66
Q. Zhou, J.K. Aggarwal. Tracking and Classifying Moving Objects from Video. 這篇文章另辟蹊徑，利用“緊湊度值的變化、運動方向的變化”，區分人、人群、機動車。達到良好的分類效果。是運動目標分類領域的好文章。
標簽： Q. J.K. Classifying Aggarwal
上傳時間： 2013-12-17
上傳用戶：alan-ee
Zang, Q. and Klette, R. Object Classification and Tracking in Video Surveillance. 這篇文章是有關多運動目標分類的文章。使用常用的長寬比作為分類特征，結合角點特征。提高了人車的分類效果。
標簽： Q. R. Classification Surveillance
上傳時間： 2013-12-25
上傳用戶：王者A
a book for learning the bash
上傳時間： 2013-12-21
上傳用戶：ggwz258
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.
標簽： well-known algorithm AdaBoost Adaptive
上傳時間： 2014-01-15
上傳用戶：qiaoyue
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files
標簽： well-known algorithm AdaBoost Adaptive
上傳時間： 2013-12-31
上傳用戶：jiahao131
embedded self learning and practice
標簽： embedded learning practice self
上傳時間： 2017-05-14
上傳用戶：壞壞的華仔
this step-by-step guide makes learning Borland C++Builder programming a breeze. It鈥檚 the perfect learning tool for beginning programmers who want to develop their own programming capabilities, and for developers who want to get up-to-speed with C++Builder quickly and easily.
標簽： step-by-step programming learning Borland
上傳時間： 2014-01-04
上傳用戶：gxf2016
Learning jQuery 1.3 Learning jQuery 1.3
上傳時間： 2013-12-23
上傳用戶：chenjjer
Link Layer Model in MATLAB consisting files: inputFile.m, LinkLayerModel.m, Q.m, topologyFile.m
標簽： LinkLayerModel topologyFile consisting inputFile
上傳時間： 2017-05-23
上傳用戶：王楚楚
