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
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