The concept of Adaptive Memory coupled with advances in Neighborhood structures derived from dynamic and adaptive search constructions have been the source of numerous important developments in metaheuristic optimization throughout the last decade.
標簽： Neighborhood structures Adaptive advances
上傳時間： 2015-12-05
上傳用戶：784533221
功能為Neighborhood components analysis，a quick matlab implementation of NCA (see Goldberger et al, NIPS04).
標簽： Neighborhood components analysis
上傳時間： 2013-12-11
上傳用戶：tianjinfan
Neighborhood rough set based feature evaluation and reduction
標簽： Neighborhood evaluation reduction feature
上傳時間： 2017-05-13
上傳用戶：songnanhua
Neighborhood rough set based feature evaluation and reduction
標簽： Neighborhood evaluation reduction feature
上傳時間： 2013-12-19
上傳用戶：nanxia
Neighborhood rough set based feature evaluation and reduction
標簽： Neighborhood evaluation reduction feature
上傳時間： 2017-05-13
上傳用戶：lvzhr
Neighborhood rough set based heterogeneous feature subset selection
標簽： heterogeneous Neighborhood selection feature
上傳時間： 2017-05-13
上傳用戶：GavinNeko
基于信息融合的圖像邊緣檢測方法研究,⑴直方圖均衡化（histogram equalization），⑵直方圖匹配（histogram matching），⑶鄰域平均（Neighborhood averaging），⑷局域增強（local enhancement）， ⑸中值濾波（median filtering）。
標簽： equalization histogram 信息融合 圖像邊緣檢測
上傳時間： 2014-11-07
上傳用戶：frank1234
The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 "
標簽： L. A. classifiers implements
上傳時間： 2017-08-02
上傳用戶：無聊來刷下
Fundamental to advance image processing: basic image, multiscale, and 3D representation based alot on random variables and Neighborhood operation
標簽： image representation Fundamental processing
上傳時間： 2017-08-24
上傳用戶：標點符號
Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large Neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
標簽： Super-resolution Multi-scale Dictionary Single Image for
上傳時間： 2019-03-28
上傳用戶：fullout
