learning opencv you can use it to start you vision knowledge
标签: knowledge you learning opencv
上传时间: 2017-05-06
上传用户:蠢蠢66
Q. Zhou, J.K. Aggarwal. Tracking and Classifying Moving Objects from Video. 这篇文章另辟蹊径,利用“紧凑度值的变化、运动方向的变化”,区分人、人群、机动车。达到良好的分类效果。是运动目标分类领域的好文章。
标签: Q. J.K. Classifying Aggarwal
上传时间: 2013-12-17
上传用户:alan-ee
Zang, Q. and Klette, R. Object Classification and Tracking in Video Surveillance. 这篇文章是有关多运动目标分类的文章。使用常用的长宽比作为分类特征,结合角点特征。提高了人车的分类效果。
标签: Q. R. Classification Surveillance
上传时间: 2013-12-25
上传用户:王者A
a book for learning the bash
上传时间: 2013-12-21
上传用户: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.
标签: well-known algorithm AdaBoost Adaptive
上传时间: 2014-01-15
上传用户: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
标签: well-known algorithm AdaBoost Adaptive
上传时间: 2013-12-31
上传用户:jiahao131
embedded self learning and practice
标签: embedded learning practice self
上传时间: 2017-05-14
上传用户:坏坏的华仔
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.
标签: step-by-step programming learning Borland
上传时间: 2014-01-04
上传用户:gxf2016
Learning jQuery 1.3 Learning jQuery 1.3
上传时间: 2013-12-23
上传用户:chenjjer
Link Layer Model in MATLAB consisting files: inputFile.m, LinkLayerModel.m, Q.m, topologyFile.m
标签: LinkLayerModel topologyFile consisting inputFile
上传时间: 2017-05-23
上传用户:王楚楚