|
|
|
| Feature Extraction Method Based on Global Binary Patternand Its Application |
| XU Ke,SONG Chang |
National Engineering Research Center for Advanced Rolling,University of Science
and Technology Beijing,Beijing 100083 |
|
|
|
|
Abstract Texture analysis based on Global Binary Pattern (GBP) is proposed to solve the problem that Local Binary Pattern (LBP) is sensitive to noises. In GBP,center pixels used in LBP are replaced by the mean values of large neighborhood templates,and effects of noises are weakened. However,the resistance to uneven illumination by GBP is worse than that by LBP. For surface defect recognition of steel plates,both noises and uneven illumination are serious in the images of steels. The combination of GBP and LBP with bivariate histogram is presented and applied to surface defect recognition of steel plates and slabs. The experimental results show that the combination of GBP and LBP is invariant to uneven illumination and insensitive to noises,and classification rate of cracks is up to 96%.
|
|
Received: 05 November 2012
|
|
|
|
|
|
[1]Ojala T,Pietikinen M,Maenpaa T. Multiresolution Gray Scale and Rotation Invariant Texture Classifacation with Local Binary Pattern. IEEE Trans on Pattern Analysis and Machine Intelligence,2002,24(7): 971-87
[2]Heikkila M,Pietikainen M,Schmid C.Description of Interest Regions with Local Binary Patterns.Pattern Recognition,2009,42(3): 425-436
[3]Lian Qiusheng,Li Qin,Kong Lingfu.The Texture Image Retrieval Algorithm Combined Statistical Features of the Circular Symmetric Contourlet with Local Binary Pattern. Chinese Journal of Computers,2007,30(12): 2198-2204 (in Chinese)(练秋生,李 芹,孔令富．融合圆对称轮廓波统计特征和LBP的纹理图像检索.计算机学报,2007,30(12): 2198-2204)
[4]Ahonen T,Hadid A,Pietikinen M. Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence,2006,28(12): 2037-2041
[5]Liao Shengcai,Zhu Xiangxin,Lei Zhen,et al.Learning Multi Scale Block Local Binary Patterns for Face Recognition // Proc of the International Conference on Advances in Biometrics. Seoul,Republic of Korea,2007: 828-837
[6]Menp T,Turtinen M,Pietik inen M. Real Time Surface Inspection by Texture. Real Time Imaging,2003,9(5): 289-296
[7]Niskanen M,Kauppinen H,Silvén O. Time Aspects of SOM Based Visual Surface Inspection // Proc of the SPIE on Machine Vision Applications in Industrial Inspection. San Jose,USA,2002: 123-134
[8]Zhang Wenchao,Shan Shiguang,Gao Wen,et al. Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non Statistical Model for Face presentation and Recognition // Proc of the 10th IEEE Conference on Computer Vision. Beijing,China,2005: 786-791
[9]Tan Xiaoyang,Triggs B. Enhanced Local Texture Feature Sets for Face Recognition under Difficult ighting Conditions. IEEE Trans on Image Processing,2010,19(6): 1635-1650
[10]Song Yu,LI Mantian,Sun Lining. Image Salt & Pepper Noise Self Adaptive Suppression Algorithm Based on Similarity Function. Acta Automatica Sinica,2007,33(5): 474-479 (in Chinese)(宋 宇,李满天,孙立宁.基于相似度函数的图像椒盐噪声自适应滤除算法.自动化学报,2007,33(5): 474-479)
[11]Xu Ke,Xu Jinwu,Ban Xiaojuan. Research on Pattern Recognition Method of Automatic Surface Quality Inspection System for Cold Rolled Strips. Iron and Steel,2002,37(6): 28-31 (in Chinese)(徐 科,徐金梧,班晓娟.冷轧带钢表面质量自动监测系统的模式识别方法研究.钢铁,2002,37(6): 28-31) |
|
|
|
2013, Vol. 26 
