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.
标签： Neighborhood structures Adaptive advances
上传时间： 2015-12-05
上传用户：784533221
功能为Neighborhood components analysis，a quick matlab implementation of NCA (see Goldberger et al, NIPS04).
标签： Neighborhood components analysis
上传时间： 2013-12-11
上传用户：tianjinfan
Neighborhood rough set based feature evaluation and reduction
标签： Neighborhood evaluation reduction feature
上传时间： 2017-05-13
上传用户：songnanhua
Neighborhood rough set based feature evaluation and reduction
标签： Neighborhood evaluation reduction feature
上传时间： 2013-12-19
上传用户：nanxia
Neighborhood rough set based feature evaluation and reduction
标签： Neighborhood evaluation reduction feature
上传时间： 2017-05-13
上传用户：lvzhr
Neighborhood rough set based heterogeneous feature subset selection
标签： heterogeneous Neighborhood selection feature
上传时间： 2017-05-13
上传用户：GavinNeko
基于信息融合的图像边缘检测方法研究,⑴直方图均衡化（histogram equalization），⑵直方图匹配（histogram matching），⑶邻域平均（Neighborhood averaging），⑷局域增强（local enhancement）， ⑸中值滤波（median filtering）。
标签： equalization histogram 信息融合 图像边缘检测
上传时间： 2014-11-07
上传用户：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 "
标签： L. A. classifiers implements
上传时间： 2017-08-02
上传用户：无聊来刷下
Fundamental to advance image processing: basic image, multiscale, and 3D representation based alot on random variables and Neighborhood operation
标签： image representation Fundamental processing
上传时间： 2017-08-24
上传用户：标点符号
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.
标签： Super-resolution Multi-scale Dictionary Single Image for
上传时间： 2019-03-28
上传用户：fullout
