Adaptive vectorization of line drawing images

被引:47
作者
Janssen, RDT
Vossepoel, AM
机构
[1] Delft University of Technology, Faculty of Applied Physics, Pattern Recognition Group, 2600 GA Delft
关键词
D O I
10.1006/cviu.1996.0484
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel method for vectorizing line drawing images is presented. The method is based on a sequence of a standard vectorization algorithm and maximum threshold morphology, which can be iterated until a fitting criterion is met. First, ''anchor points'' are found in a coarse vectorization. The position of each anchor point is corrected by morphological operations, after which it is fixed. Next, the vectorization is refined between the anchor points. The method is adaptive because the original input image is used for correcting and improving the vectorization. Postprocessing modules can be added to anticipate specific properties of the line drawings to be vectorized. The vectorization method is evaluated using line drawings differing in scale and resolution. (C) 1997 Academic Press.
引用
收藏
页码:38 / 56
页数:19
相关论文
共 30 条
[1]   SOME INFORMATIONAL ASPECTS OF VISUAL PERCEPTION [J].
ATTNEAVE, F .
PSYCHOLOGICAL REVIEW, 1954, 61 (03) :183-193
[2]  
DETTORI G, 1982, P 6 INT C PATT REC M, V2, P739
[3]  
Dori D., 1993, Machine Vision and Applications, V6, P69, DOI 10.1007/BF01211932
[4]  
Douglas D. H., 1973, CARTOGRAPHICA, V10, P112, DOI [10.3138/fm57-6770-u75u-7727., DOI 10.3138/FM57-6770-U75U-7727, 10.3138/FM57-6770-U75U-7727]
[5]  
EJIRI M, 1990, IMAGE ANAL APPL, P73
[6]   AUTOMATED CONVERSION OF ENGINEERING DRAWINGS TO CAD FORM [J].
FILIPSKI, AJ ;
FLANDRENA, R .
PROCEEDINGS OF THE IEEE, 1992, 80 (07) :1195-1209
[7]  
Gonzalez R.C., 1992, DIGITAL IMAGE PROCES
[8]  
Hilditch C. J., 1969, Machine Intelligence, P403
[9]  
Janssen R. D. T., 1994, Proceedings of the Second IEEE Workshop on Applications of Computer Vision (Cat. No.94TH06742), P36, DOI 10.1109/ACV.1994.341286
[10]  
Janssen R. D. T., 1993, Proceedings of the Second International Conference on Document Analysis and Recognition (Cat. No.93TH0578-5), P125, DOI 10.1109/ICDAR.1993.395767