期刊文献+

苹果果梗和缺陷的识别技术研究 被引量:26

Identification technique for stem-end and blemish in apples
下载PDF
导出
摘要 为解决苹果自动检测和分选中苹果果梗和缺陷的识别这一瓶颈问题,提出基于HIS颜色模型下亮度I的分布特性,从信号处理的角度确定苹果的果梗和缺陷区域 再提取区域纹理特征,构筑模拟退火神经网络作为模式分类器,区分果梗和缺陷 试验结果表明用该方法识别准确率接近90% A new method was developed to identify stem-end and blemish in apples precisely. Stem-end and blemish in apples were determined using one-dimension signal processing method based on HIS color model. By taking texture parameters of those two areas as input, a simulated annealing neural network was developed to identify stem-end and blemish in apples. Results show that the new technique is precise and effective in identification, and is feasible for practical apple sorting.
出处 《江苏大学学报(自然科学版)》 EI CAS 2004年第3期193-195,共3页 Journal of Jiangsu University:Natural Science Edition
基金 江苏省自然科学基金资助项目(BK2002005) 国家863计划项目(2002AA248051)
关键词 苹果 识别 计算机视觉 神经网络 apple identification computer vision neural network
  • 相关文献

参考文献8

  • 1Leemans V,Magein H,Destain M F.Defects segmentation on "Golden Delicious"apple by using colour machine vision comput[J].Electron Agric,1998,20:117-130.
  • 2Kazuhiro Nakano.Application of neural networks to the color grading of apples[J].Computer Electron Agric,1997,18:105-116.
  • 3Tao Y.Method and apparatus for sorting objects including stable color transformation[P].U S Patent,1996- No.5-533-628.
  • 4Yang Q,Machant J A.Accurate blemish detection with active contour models[J].Computer Electron Agric,1995,14:77-89.
  • 5Wen Z,Tao Y.Dual-camera NIR/MIR imaging forstem-end/calyx identification in apple defect sorting[J].Transaction of ASAE,2000,43(2):446-452.
  • 6王桂琴,杨子彪,郑丽敏,朱虹,廖树华,单成钢,吴富宁.计算机视觉在农产品检测中的应用[J].中国农业科技导报,2003,5(3):52-56. 被引量:18
  • 7Sexton R,Dorsey R,Johnson J.Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing[J].European Journal of Operational Research,1999,114:589-601.
  • 8张成,雷玉成,程晓农,陈希章.新型模糊神经网络控制器在GTAW焊中的应用[J].江苏大学学报(自然科学版),2003,24(3):28-31. 被引量:3

二级参考文献44

  • 1Gunasekaran S, Cooper T M, Berlage A G, et al. Image Processing for Stress cracks in Corn Kernels. [J] Trans of the ASAE, 1988,31:257--263.
  • 2Berlage A G, Cooper T M, Aristazabal J F. Machine Vision Identification of Diploid and Tetraploid Ryegrass Seed[J]. Transof the ASAE, 1988,31(1) :24--27.
  • 3Rigney M P, Kranzler G A. Machine Vision for Grading Southern pine seedlings[J]. Trans of the ASAE, 1988,31 (2) : 642--646.
  • 4Delwiche M J, Tang S, Thompson J f. Prune Defect Detection by Line-scan Imaging[J]. Trans of the ASAE, 1990,33(3):950--954.
  • 5Elster R T, Goodrum J W. Detection of Cracks in Eggs Using machine vision[J]. Trans of the ASAE, 1991,34(1) :307--312.
  • 6Kranzler G A. Applying Digital Image Processing in Agriculture[J]. Agricultural Engineering, 1985, 66(3):11--14.
  • 7Liao K, Paulsen M R, Reid J F, et al, Corn Kernel Breakage Classification by Machine Vision Using a Neural Network Classifier[J], Trans of the ASAE, 1993,36(6) :1949-- 1953.
  • 8Rigney M P, Brusewitz G H, Kranzler G A. Asparagus Defect inspection with machine vision[J]. Trans of the ASAE, 1992,35(6):1873--1878.
  • 9Sarkar N, Wolfe R R. Image Prossing for Tomato Grading[J].Trans of the ASAE, 1990,33(4):564--572.
  • 10Shearer S A, Payne F A. Color and Defect Sorting of Bell Peppers Using Machine Vision[J]. Trans of the ASAE, 1990,33(6):2045--2050.

共引文献19

同被引文献238

引证文献26

二级引证文献275

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部