15693 hf reader 偉根26接口
上傳時(shí)間： 2015-12-31
上傳用戶：cursor
《Thinking in Patterns 》 -- Problem-Solving Techniques using Java Bruce Eckel ，President, MindView, Inc. Revision 0.9 翻譯：劉曉偉 本書(shū)由作者在過(guò)去幾年里開(kāi)辦的一個(gè)講座課程發(fā)展而來(lái)的。本書(shū)不是一本介紹性的書(shū)籍。我假定你在閱讀本書(shū)之前通讀過(guò)《Thinking in Java》或者與之相當(dāng)?shù)钠渌滩摹?此外，我還假定你對(duì)Java 的語(yǔ)法有一定程度的了解。如果你對(duì)“對(duì)象”只有一些初步的認(rèn)識(shí)，在理解本書(shū)就容易得多。
標(biāo)簽： Problem-Solving Techniques Thinking Patterns
上傳時(shí)間： 2013-12-27
上傳用戶：253189838
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments.
標(biāo)簽： relational filtering consider problem
上傳時(shí)間： 2016-01-02
上傳用戶：海陸空653
Barcode reader of all barcode reader mechine
標(biāo)簽： reader Barcode barcode mechine
上傳時(shí)間： 2016-01-03
上傳用戶：我干你啊
％ PURPOSE : Demonstrate the differences between the following filters on the same problem: ％ ％ 1) Extended Kalman Filter (EKF) ％ 2) Unscented Kalman Filter (UKF) ％ 3) Particle Filter (PF) ％ 4) PF with EKF proposal (PFEKF) ％ 5) PF with UKF proposal (PFUKF)
標(biāo)簽： the Demonstrate differences following
上傳時(shí)間： 2016-01-07
上傳用戶：yiwen213
it just to check a math problem
標(biāo)簽： problem check just math
上傳時(shí)間： 2016-01-16
上傳用戶：dragonhaixm
an example of programming philips pegoda reader
標(biāo)簽： programming example philips pegoda
上傳時(shí)間： 2016-01-16
上傳用戶：stewart·
二維樹(shù)狀數(shù)組 解北大2155 http://acm.pku.edu.cn/JudgeOnline/problem?id=21
標(biāo)簽： JudgeOnline problem 2155 http
上傳時(shí)間： 2016-01-17
上傳用戶：米卡
The problem of image registration subsumes a number of problems and techniques in multiframe image analysis, including the computation of optic flow (general pixel-based motion), stereo correspondence, structure from motion, and feature tracking. We present a new registration algorithm based on spline representations of the displacement field which can be specialized to solve all of the above mentioned problems. In particular, we show how to compute local flow, global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of the above problems. Using a spline-based description of the flow removes the need for overlapping correlation windows, and produces an explicit measure of the correlation between adjacent flow estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D projective scene geometry. We also provide results on a number of standard motion sequences.
標(biāo)簽： image registration multiframe techniques
上傳時(shí)間： 2016-01-20
上傳用戶：520
Data Abstraction & Problem Solving with C++源碼
標(biāo)簽： Abstraction Problem Solving Data
上傳時(shí)間： 2013-12-24
上傳用戶：離殤
蟲(chóng)蟲(chóng)下載站版權(quán)所有 京ICP備2021023401號(hào)-1
