DocumentCode :
1051815
Title :
Vector-based arc segmentation in the machine drawing understanding system environment
Author :
Dori, Dov
Author_Institution :
Fac. of Ind. Eng. & Manage., Technion-Israel Inst. of Technol., Haifa, Israel
Volume :
17
Issue :
11
fYear :
1995
fDate :
11/1/1995 12:00:00 AM
Firstpage :
1057
Lastpage :
1068
Abstract :
Arcs are important primitives in engineering drawings. Extracting these primitives during the lexical analysis phase is a prerequisite to syntactic and semantic understanding of engineering drawings within the machine drawing understanding system. Bars are detected by the orthogonal zig-zag vectorization algorithm. Some of the detected bars are linear approximations of arcs. As such, they provide the basis for arc segmentation. An arc is detected by finding a chain of bars and a triplet of points along the chain. The arc center is first approximated as the center of mass of the triangle formed by the intersection of the perpendicular bisectors of the chords these points define. The location of the center is refined by recursively finding more such triplets and converging to within no more than a few pixels from the actual arc center after two or three iterations. The high performance of the algorithm, demonstrated on a set of real engineering drawings, is due to the fact that it avoids both raster-to-vector and massive pixel-level operations, as well as any space transformations
Keywords :
Hough transforms; approximation theory; computer vision; edge detection; engineering graphics; image recognition; image segmentation; iterative methods; Hough transform; bar detection; document analysis; engineering drawings; iterative method; linear approximations; machine drawing understanding system; orthogonal zig-zag vectorization; primitives; raster-to-vector operation; vector-based arc segmentation; Algorithm design and analysis; Automation; Bars; Documentation; Engineering drawings; Geometry; Image segmentation; Linear approximation; Merging; Phase detection;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.473231
Filename :
473231
Link To Document :
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