Title :
Optimal correspondence of string subsequences
Author :
Wang, Ynjiun P. ; Pavlidis, Theo
Author_Institution :
Dept. of Comput. Sci., State Univ. of New York, Stony Brook, NY, USA
fDate :
11/1/1990 12:00:00 AM
Abstract :
The definition of optimal correspondent subsequence (OCS), which extends the finite alphabet editing error minimization matching to infinite alphabet penalty minimization matching, is given. The authors prove that the string distance derived from OCS is a metric. An algorithm to compute the string-to-string OCS is given. The computational complexity of OCS is analyzed. OCS is more efficient than relaxation and elastic matching for 1D problems. An algorithm combining syntactic information in template matching is given to show the ease of integrating regular grammar into the OCS technique. Since in different applications different penalty functions may be required, two of them are discussed: one pointwise and the other piecewise. The pointwise application consists of a stereo epipolar line matching problem solved by using string-to-string OCS. The feasibility of applying OCS to UPC bar-code recognition is investigated, showing the elegance of string-to-regular-expression OCS compared to the relaxation and elastic matching techniques
Keywords :
computational complexity; optimisation; pattern recognition; UPC bar-code recognition; computational complexity; elastic matching; finite alphabet editing error minimization matching; infinite alphabet penalty minimization matching; metric; optimal correspondent subsequence; regular grammar; relaxation; stereo epipolar line matching problem; string distance; string subsequences; string-to-regular-expression subsequence; string-to-string subsequence; syntactic information; template matching; Computational complexity; Computer science; Helium; Image recognition; Noise level; Pattern matching; Pattern recognition; Quantization; Speech analysis; Speech recognition; Stereo vision;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on