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纹理合成的自相关性判别法及其应用
  • ISSN号:1000-1239
  • 期刊名称:《计算机研究与发展》
  • 时间:0
  • 分类:TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构:[1]浙江理工大学,杭州310033
  • 相关基金:国家自然科学基金项目(60473038);浙江省自然科学基金项目(Y106102)
中文摘要:

从加快纹理相似性的判别速度出发,提出了一种纹理合成的自相关性判别法.传统的纹理合成算法随着邻域和样本的增大,计算量将成倍增加,纹理合成速度减慢的劣势逐渐体现出来.因此,算法对样本纹理建立简单的自相关性距离查找表,利用L邻域内像素的自相关性距离作为像素匹配的判别依据。以查找取代传统匹配过程中的繁琐计算,极大地加快了合成速度,可实现动态的、多精度的合成效果调控,以及避免块匹配中易出现纹理接缝的问题.经验证,该算法可在纹理合成、图像修补及纹理检索中应用,并可很好地达到实时的应用要求.

英文摘要:

Texture synthesis from sample is a most important part of computer graphics. And the key of the texture synthesis technique from samples is local texture similarity matching. In order to improve the speed of texture comparability distinguishing, a method of texture synthesis with self-relativity distinguishing is presented in this paper. With the increase in the size of neighborhood and sample, the amount of calculation in the traditional texture synthesis approach will grow quickly and the inferior position in the synthesis speed will tack on progressively. And in the neighborhood L, the distribution of pixel is consectary at the geometry but discrete at the color space. So, this algorithm sets up a simple finding-list of self-relativity distance to the sample texture. By using the self-relativity distance of pixels in the neighborhood L as the distinguishing rule, the algorithm uses finding instead of fussy calculation in the traditional approach. It reflects the inter-character and the correlation of the texture into the distinguishing rule, quickens the speed of texture synthesis, and adjusts the multiple precision texture synthesis to avoid the problem of texture joint. After the examination, the algorithm presented has vast application in texture synthesis, image repair and texture searches, and fits for the demand of real-time in application.

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期刊信息
  • 《计算机研究与发展》
  • 中国科技核心期刊
  • 主管单位:中国科学院
  • 主办单位:中国科学院计算技术研究所
  • 主编:徐志伟
  • 地址:北京市科学院南路6号中科院计算所
  • 邮编:100190
  • 邮箱:crad@ict.ac.cn
  • 电话:010-62620696 62600350
  • 国际标准刊号:ISSN:1000-1239
  • 国内统一刊号:ISSN:11-1777/TP
  • 邮发代号:2-654
  • 获奖情况:
  • 2001-2007百种中国杰出学术期刊,2008中国精品科...,中国期刊方阵“双效”期刊
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,荷兰文摘与引文数据库,美国工程索引,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊(2004版),中国北大核心期刊(2008版),中国北大核心期刊(2011版),中国北大核心期刊(2014版),中国北大核心期刊(2000版)
  • 被引量:40349