neural network utility is a Neural Networks library for the C++ Programmer. It is entirely object oriented and focuses on reducing tedious and confusing problems of programming neural networks. By this I mean that network layers are easily defined. An entire multi-layer network can be created in a few lines, and trained with two functions. Layers can be connected to one another easily and painlessly.
標(biāo)簽： Programmer Networks entirely network
上傳時(shí)間： 2013-12-24
上傳用戶：liuchee
一本詳細(xì)介紹vc object model 的書籍，你書中的講解十分的詳細(xì)、全面
標(biāo)簽： object model 詳細(xì)介紹 書籍
上傳時(shí)間： 2016-12-19
上傳用戶：ynzfm
無需攝像機(jī)標(biāo)定的攝像機(jī)運(yùn)動(dòng)跟蹤算法 Moving Object Detection Video Surveillance and its Applicationinand Monitoring
標(biāo)簽： Applicationinand Surveillance Monitoring Detection
上傳時(shí)間： 2014-11-28
上傳用戶：tianjinfan
Video object matching across multiple independent views using local descriptors and adaptive learning文章描述了多攝像機(jī)系統(tǒng)下的目標(biāo)檢測與跟蹤，和自學(xué)習(xí)方法。很有參考價(jià)值。
標(biāo)簽： independent descriptors matching adaptive
上傳時(shí)間： 2014-01-18
上傳用戶：chongcongying
列出數(shù)據(jù)庫中所有的表名 獲取access庫中表的個(gè)數(shù)及表的名稱 用ado怎樣實(shí)現(xiàn) 工程--->引用--->Microsoft ActiveX Data Object 2.x（版本號(hào)）
標(biāo)簽： Microsoft ActiveX access Object
上傳時(shí)間： 2016-12-30
上傳用戶：bakdesec
Object-Oriented Programming with ANSI-C
標(biāo)簽： Object-Oriented Programming ANSI-C with
上傳時(shí)間： 2017-01-04
上傳用戶：3到15
（1）ICCV1999 Object Recognition from Local Scale-Invariant Features.pdf提出（2）IJCV2004 Distinctive image features from scale invariant keypoints.pdf總結(jié)（3）CVPR2004 PCA-SIFT：A More Distinctive Representation for Local Image Descriptors.pdf加PCA降維
標(biāo)簽： Scale-Invariant Distinctive Recognition Features
上傳時(shí)間： 2013-12-27
上傳用戶：yepeng139
PASCAL Visual Object Classes，視覺識(shí)別競賽的程序，共有20大類視覺目標(biāo)。用MATLAB代碼寫成。能生成precision/recall圖。
標(biāo)簽： Classes PASCAL Object Visual
上傳時(shí)間： 2014-01-11
上傳用戶：壞天使kk
Object-Oriented Programming Implementation Issues of C++ Manipulating Data Object Persistence and Encryption
標(biāo)簽： Object-Oriented Implementation Manipulating Programming
上傳時(shí)間： 2017-01-14
上傳用戶：杜瑩12345
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標(biāo)簽： processing ballistic the tracking
上傳時(shí)間： 2014-10-31
上傳用戶：yyyyyyyyyy
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