|Table of Contents|

Counting of Overlapping Soybean Grain by Support Vector Machine(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2009年01期
Page:
151-155
Research Field:
Publishing date:

Info

Title:
Counting of Overlapping Soybean Grain by Support Vector Machine
Author(s):
ZHU Wei-xing; SU Wei; ZHANG Huai-de
Electrical and Information Engineering College，Jiangsu University，Zhenjiang 212013，Jiangsu，China
Keywords:
Support Vector Machine; Segment; Count; Soybean
PACS:
TP391.41
DOI:
10.11861/j.issn.1000-9841.2009.01.0151
Abstract:
In order to improve the efficiency and precision for measuring the mass of 1 000 soybean grains，a novel new method which can effectively segment overlapping granule and count it based on the machine vision technique was proposed.Firstly collecting the products grain image and be preprocessed，then distilling the region of all overlapping grain；The concave points，grains’ approximate center of area and the euler number of topology shape were found out as the feature vectors of the overlapping area；the types of overlapping particle intellectively were finally identified into serial，parallel and double-deck overlapping types by the classification of Support Vector Machine，and automatically divided overlapping granule into lots of single grains.The experimental results show that the paper can effectively count the double-deck overlapping or profoundly cohesive soybean grains.

References:

[1]杨蜀秦，何东健．连接大米籽粒图像的自动分割算法研究[J]．农机化研究，2005(5)：62-65．(Yang S Q，He D J.Automated identification and separation of touching rice grains with machine vision[J].Agricultural Mechanize Research，2005(5)：62-65.)

[2] Roerdink J B，Meijster A.The watershed transform：definitions，algorithms and strategies[J].Fundamental Information，2001，41：187-288．
[3]凌云，王一鸣，孙明，等.基于流域算法的谷物籽粒图像分割技术[J]．农业机械学报，2005，36(3)：95-98．(Lin Y，Wang Y L，Sun M，et al.Application of watershed algorithm to paddy image segmentation[J].Journal of Agricultural Machine，2005，36(3)：95-98.)
[4] Wang W．Study on cell cluster splitting[J].IEEE Computer Society，2007，183：427-432．
[5]赵杰文，刘少鹏，邹小波，等.基于支持向量机的缺陷红枣机器视觉识别[J]．农业机械学报，2008，39(3)：113-116.(Zhao J W，Liu S P，Zhou X B，et al.Recognition of defect Chinese dates by machine vision and support vector machine[J].Journal of Agricultural Machine，2008，39(3)：113-116.)
[6]傅蓉，申洪，陈浩.基于凹点搜寻的重叠细胞图像自动分离的算法研究[J]．计算机工程与应用，2007，43(17)：21-24．(Fu R，Shen H，Chen H.Research of automatically separating algorithm for overlap cell based on searching concave spot[J].Computer Engineering and Applications，2007，43(17)：21-24.)
[7] Ioannou D，Huda W，Laine A F．Circle recognition through a 2D hough transform and radius histogramming[J]．Image and Vision Computing，1999，17：15-26．
[8]林金龙，石青云．用点Hough变换实现圆检测的方法[J]．计算机工程，2003，29(11)：17-19．(Lin J L，Shi Q Y.Circle recognition through a point Hough transformation[J].Computer Engineering，2003，29(11)：17-19.)
[9]罗三定，肖飞.不规则类圆形团块目标图像识别的新方法[J].中南大学学报，2002，35(4)：632-637．(Luo S D，Xiao F.A new method of recognizing quasi circular object[J].Journal of South University，2002，35(4)：632-637.)
[10]雷国伟，郭东辉.基于CNN二值图像处理的欧拉数计算[J].哈尔滨工程大学学报，2006，27(10)：202-204．(Lei G W，Guo D H.Euler solving based on binary image processing via CNN[J].Journal of Harbin Institute of Technology，2006，27(10)：202-204.)
[11]王立国，张晔，谷延锋.支持向量机多类目标分类器的结构简化研究[J]．中国图象图形学报，2005，10(5)：571-574．(Wang L G，Zhang Y，Gu Y F.Research of simplification of structure of multi class classifier of SVM[J].Journal of Image and Graphics，2005，10(5)：571-574.)
[12] Vapnik V N.Statistical learning theory[M].New York，John Wiley，1998，95-104.
[13] HSU C W，L IN C J.A comparison of methods for multi class support vector machines [J].IEEE Transon Neural Networks，2002，13（2）：415-425.

Memo

Memo:
-
Last Update: 2014-10-03