计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 213-218.doi: 10.19678/j.issn.1000-3428.0046638

• 图形图像处理 • 上一篇    下一篇

基于凹点检测的粮仓粘连害虫图像分割算法

陈树越,吴正林,朱军,刘佳镔   

  1. 常州大学 信息科学与工程学院,江苏 常州 213164
  • 收稿日期:2017-04-05 出版日期:2018-06-15 发布日期:2018-06-15
  • 作者简介:陈树越(1963—),男,教授、博士,主研方向为图像处理;吴正林、朱军,硕士研究生;刘佳镔,本科生。
  • 基金项目:

    江苏省高等学校大学生创新创业训练计划项目(201610292023Z);常州市科技支撑(工业)计划项目(CE20160024)。

Image Segmentation Algorithm for Grain Overlapping Pests Based on Pitting Detection

CHEN Shuyue,WU Zhenglin,ZHU Jun,LIU Jiabin   

  1. School of Information Science and Engineering,Changzhou University,Changzhou,Jiangsu 213164,China
  • Received:2017-04-05 Online:2018-06-15 Published:2018-06-15

摘要: 针对计数过程中粘连害虫难以准确分割的问题,提出一种改进的凹点检测和精确分割点定位的粘连害虫分割算法。通过形状因子和像素面积因子约束提取害虫粘连区域,采用改进的Harris算法计算角点的像素相似度,选出候选角点,剔除候选角点中的非凹点,将剩余的凹点进行局部非极大值抑制选出真正的凹点。对粘连害虫轮廓进行逐层剥离,找出分离点,通过分离点与凹点的距离以及害虫像素面积因子约束确定最终的分割点,连接分割点画出分割线。实验结果表明,粘连分割算法能够准确地选出粘连害虫的分割线,且运算效率较高。

关键词: 粮食仓储, 害虫, 图像分割, 计数, 凹点搜索

Abstract: Aiming at the problem of accurate segmentation of overlapping pests in the counting process,an improved algorithm of segmentation of pests with pits detection and dividing point location is proposed.The overlapping area of pests is extracted by the shape factor and the pixel area factor constraint.The improved Harris algorithm is used to calculate the pixel similarity of the corner points to find the candidate corners.The non-pits in the candidate corners are removed and the remaining pits perform local non-maximal suppression to find true pits.The separation point of the overlapping pests is stripped layer by layer to find the separation points,and the final dividing points can be determined by the factor of pest pixel area constraint and the distance between the separated points and the pits.Experimental results show that the proposed algorithm can accurately find the dividing line of overlapping pests and make the operation more efficient.

Key words: grain storage, pest, image segmentation, counting, pitting search

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