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基于多种易用标记的图像着色 被引量:3

Colorization based on multiple user-friendly input labels
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摘要 在现有的基于颜色标记的着色算法中,用户提供的颜色标记仅用于在图像局部区域指定颜色。由于功能方式单一,难以全面地反映用户的意图,导致着色结果容易出现失真。本文提出一种利用多种颜色标记方式提高着色质量的方法。首先定量讨论其着色结果与用户输入的颜色标记之间的关系,并对失真的主要成因进行数学分析;进而提出一种新算法综合处理传统方式的阳性颜色以及新增的非局域性和阴性颜色3种标记方式,其中非局域性标记用于实现信息的远距离传播,为实现零碎区域着色提供有效手段,而阴性颜色标记与传统标记互补,通过在图像局部区域取消某种颜色,便于更最直观、灵活地修改着色结果。实验表明,引入这些标记不仅操作简单,而且有助于计算机准确理解用户意愿,便于用户直观明确地控制着色效果。 In existing label-based colorization method,labels are employed to only specify chrominance of pixels in several are as during colorization.Since this single kind of user label cannot pro vide enough user′s intention for colorization,the exiting method suffers from falsehood in r esult.To solve this problem,a colorization method which can deal with multiple intuitive labels is proposed in this paper.Firstly,the mathematical analysis on mechanism of optimization colorization algorithms is performed,whic h helps not only to extract relation between output colorization result and user′s input labels,but also to identify the very reason for poor quality result,and encourages a colorization frame to synthesize two additional kinds o f intuitive labels with the existing one. Among them,the non-local one makes it available to take distant propagation,whi ch is beneficial to high-quality colorization on scattered area,and another one named negative label,in contrast to traditional label,reduces the influence of specific label in indicated area,a nd extends flexibility in improvement for poor result.Experiment results s how that the proposed algorithm with intuitive labels can not only provide intelligent ways to understand user i ntention,but also make it easier to control visual effects directly and effectively.
出处 《光电子.激光》 EI CAS CSCD 北大核心 2014年第2期386-392,共7页 Journal of Optoelectronics·Laser
关键词 着色 数学分析 多种标记 易用 colorization mathematical analysis multiple labels user-friendly
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  • 1贾云涛,胡事民.基于图切分的交互式图像染色算法[J].计算机学报,2006,29(3):508-512. 被引量:15
  • 2Huang D, Shan C,Ardabilian M, et al Local binary pat- terns and its application to facial image analysis..a survey [J].IEEE Transactions on Systems, Man, And Cybernet- ics-Part C .. Applications And Reviews, 2011,41 ( 6 ) : 765- 781.
  • 3Tan K S,Isa N A M Color image segmentation using his- togram thresholding fuzzy c-means hybrid approach[J].Pattern Recognition,2011,44(1) .. 1-15.
  • 4Yuksel M E,Borlu M Accurate segmentation of dermosc- opic images by image thresholding based on type-2 fuzzy Iogic[J].IEEE Transactions On Fuzzy Systems, 2009,17 (4) :976-982.
  • 5Zeng X Y,Che Y W N,Nakao Z,et al. Texture represen- tation based on pattern map[J]. Signal Process, 2004,84 (3) :589-599.
  • 6HUANG Rui, SANG Nong, LUO Da-peng,et al. Image seg- mentation via coherent clustering in Lab color space[J]. Pattern Recognition Letters,2011,32(7) : 891-902.
  • 7Krinidis S,Chatzis V. A robust fuzzy local informatio'n cmeans clustering algorithm[J]. IEEE Transactions On Image Process,2010,19(5) :1328-1337.
  • 8Zhang K,Zhang L,Song H,et al. Active contours with selective local or global segmentation: a new formulation and level set method[J].Image and Vision Computing, 2010,28(4) : 668-676.
  • 9Mirandaa P A V,Facaoa A X,Udupa J K. Synergistic arc- weight estimation for interactive image segmentation using graphs[J]. Computer Vision and Image Understand- ing,2010,114(1) :85-99.
  • 10Ugarriza G, Saber L, Vantaram E, et al. Automatic image segmentation by dynamic region growth and multiresolu- tion merging[JJ. IEEE Transactions on Image Processing, 2009,18(10) .. 2275-2288.

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  • 1张洁,朱莉娟.贪心算法与动态规划的比较[J].新乡师范高等专科学校学报,2005,0(5):18-20. 被引量:4
  • 2崔建弘,吕晓华.用Java实现L型骨牌覆盖问题的分治算法分析[J].硅谷,2008,1(17). 被引量:1
  • 3Mullet A C, Narayanan S. Cognitively-engineered multi- sensor image fusion for military applications[J]. Informa- tion Fusion,2009,10(2) : 137-149.
  • 4Alaniz J R J,Suarez Q Y,Oristerna R V,et al. Design and validation of a one channel near-infrared spectroscopy system for applications in medicine[J]. IFMBE Proceeds- ing, 2015,49 : 103-106.
  • 5Saichandana B,Ramesh S, Srinivas K,et al. Image fusion technique for remote sensing image enhancement[J]. Ad- vances in Intelligent Systems & Computing, 2014,249: 235-242.
  • 6Waxman A M,Fay D A,Gove A N,et al. Color night vi- sion:fusion of intensified visible and thermal IR imagery [A]. Proc. of SPIE[C]. 1995,2463: 58-68.
  • 7Toet A, Walraven J. New false color mapping for image fusion[J]. Optical Engineering, 1996,35(3) : 650-658.
  • 8Scribner D A,Schuler J M,Warren P R,et al. Infrared col- or vision., separating objects from backgrounds [ A ]. Proc. of SPIE[C]. 1998,3379 : 2-13.
  • 9Reinhard E, Ashikhmin M, Gooch B, et al. Color transfer between images[J]. IEEE Computer Graphics and Appli- cations, 2001,21 (5) :34-41.
  • 10Welsh T, Ashikhmin M, Mueller M. Transferring color to grayscale images [J]. ACM Transactions on Graphics, 2002,21 (3) : 277-280.

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