A computational model of vision attention for inspection of surface quality in production line

被引:11
作者
Li, Guohui [1 ]
Shi, Jinfang [2 ]
Luo, Hongsen [1 ]
Tang, Miangang [1 ]
机构
[1] Sichuan Normal Univ, Sch Engn, Chengdu 610101, Peoples R China
[2] Southwest Univ Sci & Technol, Sch Mfg Sci & Engn, Mianyang 621010, Peoples R China
关键词
Visual attention; Regions of interest; Saliency map; DISCRIMINANT SALIENCY; VISUAL-ATTENTION;
D O I
10.1007/s00138-012-0429-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There is need to detect regions of small defects in a large background, when product surface quality in line is inspected by machine vision systems. A computational model of visual attention was developed for solving the problem, inspired by the behavior and the neuronal architecture of human visual attention. Firstly, the global feature was extracted from input image by law's rules, then local features were extracted and evaluated with an improved saliency map model of Itti. The local features were fused into a single topographical saliency map by a multi-feature fusion operator differenced from Itti model, in which the better feature has the higher weighting coefficient and more contribution to fusion of the feature's images. Finally, the regions were "popped out" in the map. Experimental results show that the model can locate regions of interest and exclude the most background regions.
引用
收藏
页码:835 / 844
页数:10
相关论文
共 37 条
[1]  
Achanta R, 2009, PROC CVPR IEEE, P1597, DOI 10.1109/CVPRW.2009.5206596
[2]   The Phase Only Transform for unsupervised surface defect detection [J].
Aiger, Dror ;
Talbot, Hugues .
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, :295-302
[3]  
[Anonymous], 2011, J AM SCI
[4]  
[Anonymous], 2006, FEATURE EXTRACTION C
[5]  
[Anonymous], RES MULTISPECTRAL RE
[6]  
[Anonymous], WORKSH EARL LCOGN VI
[7]  
[Anonymous], 2007, PROC IEEE C COMPUT V, DOI 10.1109/CVPR.2007.383267
[8]  
[Anonymous], 2007, P IEEE INT C IM PROC
[9]   Cost-sensitive learning of top-down modulation for attentional control [J].
Borji, Ali ;
Ahmadabadi, Majid N. ;
Araabi, Babak N. .
MACHINE VISION AND APPLICATIONS, 2011, 22 (01) :61-76
[10]   Assessment of the influence of adaptive components in trainable surface inspection systems [J].
Eitzinger, Christian ;
Heidl, W. ;
Lughofer, E. ;
Raiser, S. ;
Smith, J. E. ;
Tahir, M. A. ;
Sannen, D. ;
Van Brussel, H. .
MACHINE VISION AND APPLICATIONS, 2010, 21 (05) :613-626