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NSCT融合

于 2021-04-22 发布
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说明：  主要用于图像融合。 NSCT融合有利于更好的保持图像的边缘信息和轮廓结构，增强图像的平移不变性。(It is mainly used for image fusion. NSCT fusion is conducive to better preserve the edge information and contour structure of the image, and enhance the translation invariance of the image.)

文件列表：

NSCT融合\NSCT\atrousc.c, 2928 , 2015-06-18
NSCT融合\NSCT\atrousc.mexmac, 8664 , 2015-06-18
NSCT融合\NSCT\atrousc.mexw32, 7168 , 2015-10-23
NSCT融合\NSCT\atrousdec.m, 1192 , 2015-06-18
NSCT融合\NSCT\atrousfilters.m, 19170 , 2015-06-18
NSCT融合\NSCT\atrousrec.m, 973 , 2015-06-18
NSCT融合\NSCT\decdemo.m, 3214 , 2015-06-18
NSCT融合\NSCT\DEMO.m, 671 , 2015-11-12
NSCT融合\NSCT\dfbdecdemo.m, 1612 , 2015-09-21
NSCT融合\NSCT\dfilters.m, 12802 , 2015-06-18
NSCT融合\NSCT\dmaxflat.m, 2531 , 2015-06-18
NSCT融合\NSCT\efilter2.m, 1065 , 2015-06-18
NSCT融合\NSCT\expEdge.m, 209 , 2008-10-29
NSCT融合\NSCT\extend2.m, 1792 , 2015-06-18
NSCT融合\NSCT\filtergrid.m, 2508 , 2015-10-15
NSCT融合\NSCT\findalph.m, 506 , 2008-07-02
NSCT融合\NSCT\fusionIH.m, 2740 , 2015-10-19
NSCT融合\NSCT\fusionNSCT.bmp, 553838 , 2016-01-05
NSCT融合\NSCT\fusionNSCT.bmp.enp, 141728 , 2016-01-05
NSCT融合\NSCT\Hist Match.m, 1128 , 2015-10-27
NSCT融合\NSCT\HISTdemo.m, 178 , 2015-10-27
NSCT融合\NSCT\histmatch.m, 5779 , 2015-10-27
NSCT融合\NSCT\IHS.m, 516 , 2015-10-19
NSCT融合\NSCT\IHSDEMO.m, 159 , 2015-12-12
NSCT融合\NSCT\imhist_my.m, 762 , 2015-10-27
NSCT融合\NSCT\invIHS.m, 2149 , 2015-10-19
NSCT融合\NSCT\ld2quin.m, 819 , 2015-06-18
NSCT融合\NSCT\ldfilter.m, 638 , 2015-06-18
NSCT融合\NSCT\lowpassfilter.m, 2520 , 2015-10-15
NSCT融合\NSCT\mctrans.m, 854 , 2015-06-18
NSCT融合\NSCT\modIHS.m, 825 , 2015-10-29
NSCT融合\NSCT\modulate2.m, 735 , 2015-06-18
NSCT融合\NSCT\NSCT.m, 2533 , 2015-12-30
NSCT融合\NSCT\nsct1.m, 1712 , 2015-10-22
NSCT融合\NSCT\nsctdec.m, 4850 , 2015-12-11
NSCT融合\NSCT\nsctdec1.m, 4983 , 2015-10-22
NSCT融合\NSCT\nsctdemo.asv, 2783 , 2015-12-16
NSCT融合\NSCT\nsctfunction.m, 2602 , 2015-12-11
NSCT融合\NSCT\nsctrec.m, 3164 , 2015-06-18
NSCT融合\NSCT\nsdfbdec.m, 4897 , 2015-06-18
NSCT融合\NSCT\nsdfbrec.m, 4787 , 2015-06-18
NSCT融合\NSCT\nsfbdec.m, 923 , 2015-09-23
NSCT融合\NSCT\nsfbrec.m, 795 , 2015-06-18
NSCT融合\NSCT\nssfbdec.m, 2424 , 2015-06-18
NSCT融合\NSCT\nssfbrec.m, 2785 , 2015-06-18
NSCT融合\NSCT\parafilters.m, 1496 , 2015-06-18
NSCT融合\NSCT\perfft2.m, 3233 , 2015-10-15
NSCT融合\NSCT\phasecong3.m, 26476 , 2015-10-21
NSCT融合\NSCT\phasecongmono.m, 22991 , 2015-10-15
NSCT融合\NSCT\qupz.m, 1161 , 2015-06-18
NSCT融合\NSCT\README.txt, 1529 , 2015-06-18
NSCT融合\NSCT\resampz.m, 1883 , 2015-06-18
NSCT融合\NSCT\shownsct.m, 1001 , 2015-06-18
NSCT融合\NSCT\symext.m, 856 , 2015-06-18
NSCT融合\NSCT\threshold.m, 1228 , 2015-10-26
NSCT融合\NSCT\upsample2df.m, 155 , 2015-06-18
NSCT融合\NSCT\wfilters.m, 3225 , 2015-06-18
NSCT融合\NSCT\zconv2.c, 4056 , 2015-06-18
NSCT融合\NSCT\zconv2.mexmac, 8664 , 2015-06-18
NSCT融合\NSCT\zconv2.mexw32, 7680 , 2015-10-23
NSCT融合\NSCT\zconv2S.c, 4042 , 2015-06-18
NSCT融合\NSCT\zconv2S.mexmac, 8700 , 2015-06-18
NSCT融合\NSCT\zconv2S.mexw32, 7680 , 2015-10-23
NSCT融合\NSCT, 0 , 2021-02-02
NSCT融合, 0 , 2021-02-02

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    说明：  近年来，随着互联网的高速发展，网上的多媒体信息也急剧增加，这些多媒体信息以图像为主。如何从浩瀚的图像数据库中快速、准确地找出所需要的图像，己成为一个备受关注的研究课题。有效地组织、管理和检索大规模的图像数据成为迫切需要解决的问题。于是基于内容的图像检索(Content-Based Image Retrieval: CBIR)作为一个崭新的研究领域出现了。 本课题拟研究、分析彩色图像红、绿、蓝三基色直方图的生成、特征提取和相似度等问题，应用图像的颜色信息—三基色直方图对图像进行检索。针对基于颜色的图像检索，本文采用应用广泛的RGB颜色空间来表示图像的颜色特征，对颜色分量进行等间隔量化并形成特征矢量并对特征矢量进行归一化处理，采用图像均匀分块的方法引入图像中色彩所处的位置信息，用距离度量函数进行图像的相似性匹配。在此基础上实现了基于三基色直方图算法的检索系统。 本文的研究和实践对于促进基于内容的图像数据库检索技术的应用具有一定的参考价值和实践意义。 (With the rapid development of Internet, the multimedia information is booming. All this information is mostly images. Effective recognizing, management and searching all these images have been an emergent problem. This has led the rise of a new research and development field: Content-Based Image Retrieval (CBIR). The topics to research, analysis color images red, green and blue color histogram generation, feature extraction and the similarity of the issues, application of the color image- trichromatic histogram of the image retrieval. Based on the color against the static image retrieval, this paper application of a wide range of RGB color space to indicate the color image features, the color components, such as spacing and quantitative characteristics of a feature vector and a normalization of vector processing, using uniform image block the introduction of the method in which the color image Location information, and distance measuring function similar to the image of the match. On)
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