物体边沿特征提取及应用

计算机科学 ›› 2013, Vol. 40 ›› Issue (7): 280-282.

物体边沿特征提取及应用

刘纯利,张弓

安徽科技学院机电学院凤阳233100;南京航空航天大学电子信息工程学院南京210016

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受安徽省教育厅高校科学研究项目(KJ2012A065)资助

Grain Classification Based on Edge Feature

LIU Chun-li and ZHANG Gong

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 提出了利用边缘检测提取物体特征的算法,首先利用动态边缘检测算法提取物体的边缘,接着计算边沿到重心的距离,再将特征长度归一化。利用获取的特征训练SVM分类器。最后利用粮食图像对该方法进行了仿真实验。实验表明,提出的方法能有效地提取边缘特征,并且具有较高的分类正确率。

关键词: 边缘检测,边界描述,特征提取,SVM分类器中图法分类号TP393文献标识码A

Abstract: This paper proposed an object detection approach using edge detection．Firstly,it uses dynamic edge detection algorithm to extract the grain edge,secondly,computes the distance between the edge points and the center of gravity,and obtains a vector as the primitive feature which will be processed via amplitude and length normalization and become the feature．The features are used to train the SVM classifier．At last,we conducted simulation experiments using grain image,and the experiment results reveal that the proposed method can efficiently extract the edge feature,and has higherclassification accurate rate.

Key words: Edge detection,Edge representation,Feature extraction,SVM classifier

刘纯利,张弓. 物体边沿特征提取及应用[J]. 计算机科学, 2013, 40(7): 280-282. https://doi.org/

LIU Chun-li and ZHANG Gong. Grain Classification Based on Edge Feature[J]. Computer Science, 2013, 40(7): 280-282. https://doi.org/

参考文献

[1] Nikolova M．A Variational Approach to Remove Outliers and Impulse Noise[J]．Journal of Mathematical Imaging and Vision,2004,20(1):99-120
[2] Torralba A,Fergus R,Freeman W．Million tiny images:A large data set for nonparametric object and scene recognition[J]．IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,30(11):1958-1970
[3] Shen Z．Wavelet Frames and Image Restorations[C]∥Rajendra B．Proceedings of the International Congress of Mathematicians 2010．ICM 2010．Hyderabad,India:World Scientific Publishing,2010:2834-2863
[4] Ricard J,Coeurjolly D,Baskurt A．Generalizations of angular radial transform for 2D and 3D shape retrieval[J]．Pattern Recognition Letters,2005,26(14):2174-2186
[5] Tello R．Fourier descriptors for computer graphics[J]．IEEETransactions on Systems,Man and Cybernetics,1995,25(5):861-865
[6] Li C,Xu C,Gui C,et al．Distance regularized level set evolution and its application to image segmentation[J]．IEEE transactions on image processing,2010,19(12):3243-3254
[7] Zhao H,Chan T,Merriman B,et al．A variational level set approach to multiphase motion[J]．Journal of computational phy-sics,1996,127(1):179-195
[8] Li C,Xu C,Gui C,et al．Level set evolution withoutre-initialization:A new variational formulation[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition．San Diego:IEEE Conference Publications,2005:430-436
[9] Laptev I,Caputo B．Recognizing human actions:A local SVMapproach[C]∥Kittler J．Proceedings of the 17th International Conference on Pattern Recognition．Cambridge:IEEE CS Press,2004:32-36
[10] Chapelle O．Support vector machines:Induction Principles,adaptive tuninig and prior knowledge[D]．Laboratoire d’Informatique,Univ．Paris 6,Paris,France,2002
[11] Inoue T,Abe S．Fuzzy support vector machines for pattern classification[C]∥Proc．IEEE Int．Joint Conf．Neural Networks．2001:1449-1454
[12] Platt J．Probabilistic outputs for SVMs and comparisons to regularized likelihood methods[M]．Advances in Large Margin Classifiers,Cambridge,MA:MIT Press,2000:61-74
[13] Cortes C,Vapnik V N．Support vector networks[J]．Mach．Learn.,1995,20(3):273-297
[14] Chang C,Lin C．LIBSVM -A Library for Support Vector Machines[EB/OL]．http://www.csie.ntu.edu.tw /~cjlin/libsvm,2012-04-01
[15] Goh K,Chang E,Li B．Using one-class and two-class SVMs for multiclass image annotation[J]．IEEE Trans．Knowl．Data Eng.,2005,17(10):1333-1346
[16] 程仁贵,刘书炘．基于边缘检测的影像多线自动测量算法[J]．重庆理工大学学报:自然科学版,2013,27(2):89-92

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