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Perimeter Estimation of Target Object Boundary Based on Adaptive Image Granule

ZHOU Qi,WU Qin,LIANG Jiuzhen   

  1. (School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)
  • Received:2015-01-26 Online:2016-04-15 Published:2016-04-15

基于自适应图像粒的目标对象边界周长估算

周琪,吴秦,梁久祯   

  1. (江南大学物联网工程学院,江苏 无锡 214122)
  • 作者简介:周琪(1990-),男,硕士研究生,主研方向为图像处理、模式识别;吴秦,副教授;梁久祯(通讯作者),教授。
  • 基金资助:
    国家自然科学基金资助项目(61202312,61170121);教育部留学回国人员科研启动基金资助项目。

Abstract: In estimating the perimeter of a target,the blurred and complicated target boundaries seriously influence the accuracy of perimeter estimation,and contribute to a long computing time.In order to solve this problem,combined with granular computing,an adaptive algorithm to calculate different boundary perimeter is proposed.A probability model based on image grains is designed to define the boundary thickness of the target object.The image is preprocessed by blocking and granulating based on the optimal size of image grain that is derived from the boundary thickness,which results in dimension reduction of the image data.And the classical algorithms are applied to the preprocessed image to estimate the perimeter of the boundary of the target object.Experimental results show that,compared with traditional algorithms like Digital Straight Segment(DSS),Minimum Length Polygon(MLP) and Gray-Level(GL),the proposed method can adaptively deal with different boundary thickness of the target objects,which uses less time in the case of maintaining the accuracy of perimeter estimation,and has better accuracy and adaptability,especially for images with blurred and complicated boundaries.

Key words: perimeter estimation, fuzzy complication, image granule, adaptation, dimension reduction

摘要: 在图像目标对象边界周长估计中,目标边界的模糊复杂化严重影响了周长估算精度,且周长计算时间较长。为此,结合粒计算,提出一种自适应计算不同边界周长的算法。设计基于图像粒思想的概率模型,用于定义目标图像的边界厚度,根据得到的边界厚度,选取最优的图像粒度对图像进行分块和粒化预处理,使得图像数据得到降维,并应用经典算法估算预处理后的目标边界周长。实验结果表明,与传统的数字化直线片段法、最小多边形法和灰度级信息法相比,该方法能自适应地处理不同边界厚度的目标对象,在保持周长计算精度的情况下,计算时间更短,特别是在边界模糊复杂化的情况下,具有更好的精度和适应性。

关键词: 周长估算, 模糊复杂化, 图像粒, 自适应, 降维

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