Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 176-179.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Research on High Rate of Log’s Output Based on Computer Vision

ZHONG Ping-chuan1, WANG Na1, XIAO Yi-di2, ZHENG Ze-zhong1   

  1. School of Resources and Environment,University of Electronic Science and Technology of China,Chengdu 611731,China1
    Glasgow College,University of Electronic Science and Technology of China,Chengdu 611731,China2
  • Online:2019-02-26 Published:2019-02-26

Abstract: The computer can simulate the human visual environment to identify and measure things in the field of vision.With the increase of accuracy,computer vision can replace the function of human’s eyes to achieve simple and repetitive manual operations.The introduction of computer vision into logs can increase the yield of logs,reduce wood loss,maxmize the utilization rate of logs with high-efficiency and accurate performance of the computer,minimize the production of raw materials that generate square waste,and increase the output rate of logs.This algorithm is applied to automated band saw log cutting systems.The basic process includes eliminating image noise through image preproces-sing,removing the background through color segmentation,giving the contour of the region of interest by edge detection,filling the misprocessed contour edges through morphological operations,and calculating the largest area of the fitted ellipse.The experimental results show that the arithmetic can meet the requests of actual production,and the accuracy reaches 95%.

Key words: Fitting ellipse, Image processing, OpenCV, Outline of Log, Rate of output

CLC Number: 

  • TP311
[1]XU Z,WANG J,LIU Y,et al.A review of computer vision development and trends[J].Journal of Xi’an University of Posts and Telecommunications,2013,17(6):1-8.
[2]丁建文,业宁,王厚立,等.运用信息技术提高木材利用率和使用价值[J].木材加工机械,2007,18(1):44-46.
[3]ASILTURK I,UNUVAR A.Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system[J].Journal of Materials Processing Technology,2009,209(5):2302-2313.
[4]梅振荣,任洪娥,朱朦.基于非线性最小二乘原理的原木端面识别算法[J].计算机工程与应用,2012(2):177-178.
[5]刘明媚.基于区域显著性的彩色图像分割[J].电子设计工程,2013,21(18):133-135.
[6]刘越,彭宏京,钱素静.基于核空间 LLE 的彩色图像分割方法[J].计算机科学,2013,40(S1):180-183.
[7]梅振荣.基于端面图像处理的原木径级识别算法研究与实现[D].哈尔滨:东北林业大学,2011.
[8]赵亚凤,任洪娥.遗传算法和同态滤波在原木端面图像处理中的应用[J].东北林业大学学报,2014,42(2):129-132.
[9]CHENG H D,JIANG X H,SUN Y,et al.Color image segmentation:advances and prospects[J].Pattern Recognition,2001,34(12):2259-2281.
[10]景林,林耀海,温永仙,等.结合色彩特征和空域特征的成捆原木轮廓识别[J].计算机系统应用,2013,22(7):196-199.
[11]杨璟,朱雷.基于RGB颜色空间的彩色图像分割方法[J].计算机与现代化,2010(8):147-149.
[12]刘明媚.基于区域显著性的彩色图像分割[J].电子设计工程,2013,21(18):133-135.
[13]CANNY J.A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986(6):679-698.
[14]曾俊.图像边缘检测技术及其应用研究[D].武汉:华中科技大学图书馆,2011.
[15]https://en.wikipedia.org/wiki/HSL_and_HSV#/media/File:HSV_color_solid_cone_chroma_gray.png.
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